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@@ -741,6 +741,8 @@ <h1>Source code for torch.backends.cuda</h1><div class="highlight"><pre>
             <span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">_C</span><span class="o">.</span><span class="n">_get_cublas_allow_fp16_reduced_precision_reduction</span><span class="p">()</span>
         <span class="k">elif</span> <span class="n">name</span> <span class="o">==</span> <span class="s2">&quot;allow_bf16_reduced_precision_reduction&quot;</span><span class="p">:</span>
             <span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">_C</span><span class="o">.</span><span class="n">_get_cublas_allow_bf16_reduced_precision_reduction</span><span class="p">()</span>
+        <span class="k">elif</span> <span class="n">name</span> <span class="o">==</span> <span class="s2">&quot;allow_fp16_accumulation&quot;</span><span class="p">:</span>
+            <span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">_C</span><span class="o">.</span><span class="n">_get_cublas_allow_fp16_accumulation</span><span class="p">()</span>
         <span class="k">raise</span> <span class="ne">AttributeError</span><span class="p">(</span><span class="s2">&quot;Unknown attribute &quot;</span> <span class="o">+</span> <span class="n">name</span><span class="p">)</span>
 
     <span class="k">def</span> <span class="fm">__setattr__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">name</span><span class="p">,</span> <span class="n">value</span><span class="p">):</span>
@@ -750,6 +752,8 @@ <h1>Source code for torch.backends.cuda</h1><div class="highlight"><pre>
             <span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">_C</span><span class="o">.</span><span class="n">_set_cublas_allow_fp16_reduced_precision_reduction</span><span class="p">(</span><span class="n">value</span><span class="p">)</span>
         <span class="k">elif</span> <span class="n">name</span> <span class="o">==</span> <span class="s2">&quot;allow_bf16_reduced_precision_reduction&quot;</span><span class="p">:</span>
             <span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">_C</span><span class="o">.</span><span class="n">_set_cublas_allow_bf16_reduced_precision_reduction</span><span class="p">(</span><span class="n">value</span><span class="p">)</span>
+        <span class="k">elif</span> <span class="n">name</span> <span class="o">==</span> <span class="s2">&quot;allow_fp16_accumulation&quot;</span><span class="p">:</span>
+            <span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">_C</span><span class="o">.</span><span class="n">_set_cublas_allow_fp16_accumulation</span><span class="p">(</span><span class="n">value</span><span class="p">)</span>
         <span class="k">raise</span> <span class="ne">AttributeError</span><span class="p">(</span><span class="s2">&quot;Unknown attribute &quot;</span> <span class="o">+</span> <span class="n">name</span><span class="p">)</span>
 
 
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@@ -677,11 +677,14 @@ <h1>Source code for torch.fx.node</h1><div class="highlight"><pre>
     <span class="p">]</span>
 <span class="p">)</span>
 
-<span class="n">_side_effectful_need_to_be_preserved_pre_dispatch</span><span class="p">:</span> <span class="nb">set</span><span class="p">[</span><span class="n">Callable</span><span class="p">]</span> <span class="o">=</span> <span class="p">{</span>
+<span class="c1"># Dynamo is unable to trace global set[Callable].__contains__.</span>
+<span class="c1"># See https://github.com/pytorch/pytorch/issues/145761. Since we only have</span>
+<span class="c1"># a handful of ops so switch to list of callables.</span>
+<span class="n">_side_effectful_need_to_be_preserved_pre_dispatch</span><span class="p">:</span> <span class="nb">list</span><span class="p">[</span><span class="n">Callable</span><span class="p">]</span> <span class="o">=</span> <span class="p">[</span>
     <span class="n">torch</span><span class="o">.</span><span class="n">_C</span><span class="o">.</span><span class="n">_set_grad_enabled</span><span class="p">,</span>
     <span class="n">torch</span><span class="o">.</span><span class="n">amp</span><span class="o">.</span><span class="n">_enter_autocast</span><span class="p">,</span>
     <span class="n">torch</span><span class="o">.</span><span class="n">amp</span><span class="o">.</span><span class="n">_exit_autocast</span><span class="p">,</span>
-<span class="p">}</span>
+<span class="p">]</span>
 
 <span class="c1"># TODO: Either refactor this into 2 functions 1 dce for functional graphs and 1 dce for all graphs,</span>
 <span class="c1"># or add logic to correctly mark all inplace ops as side effectful.</span>
@@ -698,7 +701,8 @@ <h1>Source code for torch.fx.node</h1><div class="highlight"><pre>
     <span class="n">_ops</span><span class="o">.</span><span class="n">profiler</span><span class="o">.</span><span class="n">_record_function_exit</span><span class="p">,</span>
     <span class="n">_ops</span><span class="o">.</span><span class="n">inductor</span><span class="o">.</span><span class="n">accumulate_grad_</span><span class="o">.</span><span class="n">default</span><span class="p">,</span>
     <span class="n">operator</span><span class="o">.</span><span class="n">setitem</span><span class="p">,</span>
-<span class="p">}</span> <span class="o">|</span> <span class="n">_side_effectful_need_to_be_preserved_pre_dispatch</span>
+<span class="p">}</span> <span class="o">|</span> <span class="nb">set</span><span class="p">(</span><span class="n">_side_effectful_need_to_be_preserved_pre_dispatch</span><span class="p">)</span>
+
 <span class="k">if</span> <span class="nb">hasattr</span><span class="p">(</span><span class="n">_ops</span><span class="o">.</span><span class="n">inductor</span><span class="p">,</span> <span class="s2">&quot;resize_storage_bytes_&quot;</span><span class="p">):</span>
     <span class="n">_side_effectful_functions</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">_ops</span><span class="o">.</span><span class="n">inductor</span><span class="o">.</span><span class="n">resize_storage_bytes_</span><span class="o">.</span><span class="n">default</span><span class="p">)</span>
 
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index b35d1a98161..b787eef9f0e 100644
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diff --git a/main/_modules/torch/onnx.html b/main/_modules/torch/onnx.html
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@@ -773,7 +773,6 @@ <h1>Source code for torch.onnx</h1><div class="highlight"><pre>
     <span class="n">custom_opsets</span><span class="p">:</span> <span class="n">Mapping</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="nb">int</span><span class="p">]</span> <span class="o">|</span> <span class="kc">None</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
     <span class="n">export_modules_as_functions</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">|</span> <span class="n">Collection</span><span class="p">[</span><span class="nb">type</span><span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">]]</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span>
     <span class="n">autograd_inlining</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">True</span><span class="p">,</span>
-    <span class="o">**</span><span class="n">_</span><span class="p">:</span> <span class="n">Any</span><span class="p">,</span>  <span class="c1"># ignored options</span>
 <span class="p">)</span> <span class="o">-&gt;</span> <span class="n">ONNXProgram</span> <span class="o">|</span> <span class="kc">None</span><span class="p">:</span>
 <span class="w">    </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;Exports a model into ONNX format.</span>
 
@@ -1085,7 +1084,7 @@ <h1>Source code for torch.onnx</h1><div class="highlight"><pre>
                 <span class="s2">&quot;You are using an experimental ONNX export logic, which currently only supports dynamic shapes. &quot;</span>
                 <span class="s2">&quot;For a more comprehensive set of export options, including advanced features, please consider using &quot;</span>
                 <span class="s2">&quot;`torch.onnx.export(..., dynamo=True)`. &quot;</span><span class="p">,</span>
-                <span class="n">category</span><span class="o">=</span><span class="ne">FutureWarning</span><span class="p">,</span>
+                <span class="n">category</span><span class="o">=</span><span class="ne">DeprecationWarning</span><span class="p">,</span>
             <span class="p">)</span>
 
         <span class="k">if</span> <span class="n">export_options</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="n">export_options</span><span class="o">.</span><span class="n">dynamic_shapes</span><span class="p">:</span>
diff --git a/main/_modules/torch/onnx/_internal/_exporter_legacy.html b/main/_modules/torch/onnx/_internal/_exporter_legacy.html
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@@ -631,6 +631,7 @@ <h1>Source code for torch.onnx._internal._exporter_legacy</h1><div class="highli
 <span class="kn">import</span> <span class="nn">warnings</span>
 <span class="kn">from</span> <span class="nn">collections</span> <span class="kn">import</span> <span class="n">defaultdict</span>
 <span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Any</span><span class="p">,</span> <span class="n">Callable</span><span class="p">,</span> <span class="n">TYPE_CHECKING</span><span class="p">,</span> <span class="n">TypeVar</span>
+<span class="kn">from</span> <span class="nn">typing_extensions</span> <span class="kn">import</span> <span class="n">deprecated</span>
 
 <span class="kn">import</span> <span class="nn">torch</span>
 <span class="kn">import</span> <span class="nn">torch._ops</span>
@@ -689,6 +690,10 @@ <h1>Source code for torch.onnx._internal._exporter_legacy</h1><div class="highli
 <span class="w">    </span><span class="sd">&quot;&quot;&quot;List of paths of files that contain the model :meth:`state_dict`&quot;&quot;&quot;</span>
 
 
+<span class="nd">@deprecated</span><span class="p">(</span>
+    <span class="s2">&quot;torch.onnx.dynamo_export is deprecated since 2.6.0. Please use torch.onnx.export(..., dynamo=True) instead.&quot;</span><span class="p">,</span>
+    <span class="n">category</span><span class="o">=</span><span class="ne">DeprecationWarning</span><span class="p">,</span>
+<span class="p">)</span>
 <span class="k">class</span> <span class="nc">OnnxRegistry</span><span class="p">:</span>
 <span class="w">    </span><span class="sd">&quot;&quot;&quot;Registry for ONNX functions.</span>
 
@@ -833,6 +838,10 @@ <h1>Source code for torch.onnx._internal._exporter_legacy</h1><div class="highli
         <span class="p">}</span>
 
 
+<span class="nd">@deprecated</span><span class="p">(</span>
+    <span class="s2">&quot;torch.onnx.dynamo_export is deprecated since 2.6.0. Please use torch.onnx.export(..., dynamo=True) instead.&quot;</span><span class="p">,</span>
+    <span class="n">category</span><span class="o">=</span><span class="ne">DeprecationWarning</span><span class="p">,</span>
+<span class="p">)</span>
 <span class="k">class</span> <span class="nc">ExportOptions</span><span class="p">:</span>
 <span class="w">    </span><span class="sd">&quot;&quot;&quot;Options to influence the TorchDynamo ONNX exporter.</span>
 
@@ -1040,6 +1049,10 @@ <h1>Source code for torch.onnx._internal._exporter_legacy</h1><div class="highli
     <span class="p">)</span>  <span class="c1"># type: ignore[assignment]</span>
 
 
+<span class="nd">@deprecated</span><span class="p">(</span>
+    <span class="s2">&quot;torch.onnx.dynamo_export is deprecated since 2.6.0. Please use torch.onnx.export(..., dynamo=True) instead.&quot;</span><span class="p">,</span>
+    <span class="n">category</span><span class="o">=</span><span class="ne">DeprecationWarning</span><span class="p">,</span>
+<span class="p">)</span>
 <span class="k">class</span> <span class="nc">ONNXRuntimeOptions</span><span class="p">:</span>
 <span class="w">    </span><span class="sd">&quot;&quot;&quot;Options to influence the execution of the ONNX model through ONNX Runtime.</span>
 
@@ -1294,6 +1307,10 @@ <h1>Source code for torch.onnx._internal._exporter_legacy</h1><div class="highli
         <span class="k">raise</span> <span class="n">missing_opset</span><span class="p">(</span><span class="s2">&quot;onnxscript&quot;</span><span class="p">)</span>
 
 
+<span class="nd">@deprecated</span><span class="p">(</span>
+    <span class="s2">&quot;torch.onnx.dynamo_export is deprecated since 2.6.0. Please use torch.onnx.export(..., dynamo=True) instead.&quot;</span><span class="p">,</span>
+    <span class="n">category</span><span class="o">=</span><span class="ne">DeprecationWarning</span><span class="p">,</span>
+<span class="p">)</span>
 <span class="k">def</span> <span class="nf">dynamo_export</span><span class="p">(</span>
     <span class="n">model</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span> <span class="o">|</span> <span class="n">Callable</span><span class="p">,</span>
     <span class="o">/</span><span class="p">,</span>
diff --git a/main/_modules/torch/onnx/_internal/exporter/_onnx_program.html b/main/_modules/torch/onnx/_internal/exporter/_onnx_program.html
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@@ -299,7 +299,7 @@
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diff --git a/main/_modules/torch/onnx/_type_utils.html b/main/_modules/torch/onnx/_type_utils.html
index f28dfffb4d5..1987838b5f3 100644
--- a/main/_modules/torch/onnx/_type_utils.html
+++ b/main/_modules/torch/onnx/_type_utils.html
@@ -299,7 +299,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
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diff --git a/main/_modules/torch/onnx/utils.html b/main/_modules/torch/onnx/utils.html
index f8ae293bbad..b2d6ebe8ea0 100644
--- a/main/_modules/torch/onnx/utils.html
+++ b/main/_modules/torch/onnx/utils.html
@@ -299,7 +299,7 @@
           <div class="pytorch-left-menu-search">
             
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-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
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@@ -1092,14 +1092,14 @@ <h1>Source code for torch.onnx.utils</h1><div class="highlight"><pre>
         <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span>
             <span class="s2">&quot;Setting `operator_export_type` to something other than default is deprecated. &quot;</span>
             <span class="s2">&quot;The option will be removed in a future release.&quot;</span><span class="p">,</span>
-            <span class="n">category</span><span class="o">=</span><span class="ne">FutureWarning</span><span class="p">,</span>
+            <span class="n">category</span><span class="o">=</span><span class="ne">DeprecationWarning</span><span class="p">,</span>
         <span class="p">)</span>
     <span class="k">if</span> <span class="n">training</span> <span class="o">==</span> <span class="n">_C_onnx</span><span class="o">.</span><span class="n">TrainingMode</span><span class="o">.</span><span class="n">TRAINING</span><span class="p">:</span>
         <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span>
             <span class="s2">&quot;Setting `training` to something other than default is deprecated. &quot;</span>
             <span class="s2">&quot;The option will be removed in a future release. Please set the training mode &quot;</span>
             <span class="s2">&quot;before exporting the model.&quot;</span><span class="p">,</span>
-            <span class="n">category</span><span class="o">=</span><span class="ne">FutureWarning</span><span class="p">,</span>
+            <span class="n">category</span><span class="o">=</span><span class="ne">DeprecationWarning</span><span class="p">,</span>
         <span class="p">)</span>
 
     <span class="n">args</span> <span class="o">=</span> <span class="p">(</span><span class="n">args</span><span class="p">,)</span> <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">args</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">)</span> <span class="k">else</span> <span class="n">args</span>
diff --git a/main/_modules/torch/onnx/verification.html b/main/_modules/torch/onnx/verification.html
index bb290db4007..7f3b208894e 100644
--- a/main/_modules/torch/onnx/verification.html
+++ b/main/_modules/torch/onnx/verification.html
@@ -299,7 +299,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
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+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
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@@ -627,6 +627,7 @@ <h1>Source code for torch.onnx.verification</h1><div class="highlight"><pre>
 <span class="kn">import</span> <span class="nn">itertools</span>
 <span class="kn">import</span> <span class="nn">os</span>
 <span class="kn">import</span> <span class="nn">tempfile</span>
+<span class="kn">import</span> <span class="nn">typing_extensions</span>
 <span class="kn">import</span> <span class="nn">warnings</span>
 <span class="kn">from</span> <span class="nn">collections.abc</span> <span class="kn">import</span> <span class="n">Collection</span><span class="p">,</span> <span class="n">Mapping</span><span class="p">,</span> <span class="n">Sequence</span>
 <span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Any</span><span class="p">,</span> <span class="n">Callable</span><span class="p">,</span> <span class="n">Union</span>
@@ -1381,6 +1382,11 @@ <h1>Source code for torch.onnx.verification</h1><div class="highlight"><pre>
     <span class="p">)</span>
 
 
+<span class="nd">@typing_extensions</span><span class="o">.</span><span class="n">deprecated</span><span class="p">(</span>
+    <span class="s2">&quot;torch.onnx.verification.* is deprecated. Consider using torch.onnx.export(..., dynamo=True) &quot;</span>
+    <span class="s2">&quot;and use ONNXProgram to test the ONNX model&quot;</span><span class="p">,</span>
+    <span class="n">category</span><span class="o">=</span><span class="ne">DeprecationWarning</span><span class="p">,</span>
+<span class="p">)</span>
 <span class="k">def</span> <span class="nf">verify</span><span class="p">(</span>
     <span class="n">model</span><span class="p">:</span> <span class="n">_ModelType</span><span class="p">,</span>
     <span class="n">input_args</span><span class="p">:</span> <span class="n">_InputArgsType</span><span class="p">,</span>
@@ -1468,6 +1474,11 @@ <h1>Source code for torch.onnx.verification</h1><div class="highlight"><pre>
         <span class="p">)</span>
 
 
+<span class="nd">@typing_extensions</span><span class="o">.</span><span class="n">deprecated</span><span class="p">(</span>
+    <span class="s2">&quot;torch.onnx.verification.* is deprecated. Consider using torch.onnx.export(..., dynamo=True) &quot;</span>
+    <span class="s2">&quot;and use ONNXProgram to test the ONNX model&quot;</span><span class="p">,</span>
+    <span class="n">category</span><span class="o">=</span><span class="ne">DeprecationWarning</span><span class="p">,</span>
+<span class="p">)</span>
 <span class="k">def</span> <span class="nf">verify_aten_graph</span><span class="p">(</span>
     <span class="n">graph</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Graph</span><span class="p">,</span>
     <span class="n">input_args</span><span class="p">:</span> <span class="nb">tuple</span><span class="p">[</span><span class="n">Any</span><span class="p">,</span> <span class="o">...</span><span class="p">],</span>
@@ -1758,7 +1769,12 @@ <h1>Source code for torch.onnx.verification</h1><div class="highlight"><pre>
         <span class="n">_compare_onnx_pytorch_outputs_in_np</span><span class="p">(</span><span class="n">run_outputs</span><span class="p">,</span> <span class="n">expected_outs</span><span class="p">,</span> <span class="n">options</span><span class="p">)</span>
 
 
-<div class="viewcode-block" id="GraphInfo"><a class="viewcode-back" href="../../../generated/torch.onnx.verification.GraphInfo.html#torch.onnx.verification.GraphInfo">[docs]</a><span class="nd">@dataclasses</span><span class="o">.</span><span class="n">dataclass</span>
+<div class="viewcode-block" id="GraphInfo"><a class="viewcode-back" href="../../../generated/torch.onnx.verification.GraphInfo.html#torch.onnx.verification.GraphInfo">[docs]</a><span class="nd">@typing_extensions</span><span class="o">.</span><span class="n">deprecated</span><span class="p">(</span>
+    <span class="s2">&quot;torch.onnx.verification.* is deprecated. Consider using torch.onnx.export(..., dynamo=True) &quot;</span>
+    <span class="s2">&quot;and use ONNXProgram to test the ONNX model&quot;</span><span class="p">,</span>
+    <span class="n">category</span><span class="o">=</span><span class="ne">DeprecationWarning</span><span class="p">,</span>
+<span class="p">)</span>
+<span class="nd">@dataclasses</span><span class="o">.</span><span class="n">dataclass</span>
 <span class="k">class</span> <span class="nc">GraphInfo</span><span class="p">:</span>
 <span class="w">    </span><span class="sd">&quot;&quot;&quot;GraphInfo contains validation information of a TorchScript graph and its converted ONNX graph.&quot;&quot;&quot;</span>
 
diff --git a/main/_modules/torch/optim.html b/main/_modules/torch/optim.html
index 1a37b57d687..66a7a7c7aeb 100644
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+++ b/main/_modules/torch/optim.html
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index 4bb739ef0b3..a6b2216e7d4 100644
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@@ -299,7 +299,7 @@
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index 2adb1f72b22..6e90f00adbb 100644
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@@ -299,7 +299,7 @@
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@@ -299,7 +299,7 @@
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diff --git a/main/_modules/torch/optim/adam.html b/main/_modules/torch/optim/adam.html
index c2fde2c7caf..fb9351be1d2 100644
--- a/main/_modules/torch/optim/adam.html
+++ b/main/_modules/torch/optim/adam.html
@@ -299,7 +299,7 @@
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diff --git a/main/_modules/torch/optim/adamax.html b/main/_modules/torch/optim/adamax.html
index 5a793d9ad16..f8a95cc23da 100644
--- a/main/_modules/torch/optim/adamax.html
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@@ -299,7 +299,7 @@
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diff --git a/main/_modules/torch/optim/adamw.html b/main/_modules/torch/optim/adamw.html
index 31ca4945b18..491abb3412a 100644
--- a/main/_modules/torch/optim/adamw.html
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@@ -299,7 +299,7 @@
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-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
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index 66efa492917..7d12a740fc4 100644
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+++ b/main/_modules/torch/optim/asgd.html
@@ -299,7 +299,7 @@
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diff --git a/main/_modules/torch/optim/lbfgs.html b/main/_modules/torch/optim/lbfgs.html
index c679a919445..1aba758507a 100644
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@@ -299,7 +299,7 @@
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index f035edffe19..e14fe13e758 100644
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index dc8b18882a6..a834985b107 100644
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@@ -299,7 +299,7 @@
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--- a/main/_modules/torch/utils/tensorboard/writer.html
+++ b/main/_modules/torch/utils/tensorboard/writer.html
@@ -299,7 +299,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/_modules/torch/xpu.html b/main/_modules/torch/xpu.html
index a3d8d92115a..d81c5a6865d 100644
--- a/main/_modules/torch/xpu.html
+++ b/main/_modules/torch/xpu.html
@@ -299,7 +299,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/_modules/torch/xpu/memory.html b/main/_modules/torch/xpu/memory.html
index 841e8ea5b32..2dd69c8261b 100644
--- a/main/_modules/torch/xpu/memory.html
+++ b/main/_modules/torch/xpu/memory.html
@@ -299,7 +299,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
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     <div class="searchbox" id="googleSearchBox">
diff --git a/main/_modules/torch/xpu/random.html b/main/_modules/torch/xpu/random.html
index fd372d8a8f7..000596eee65 100644
--- a/main/_modules/torch/xpu/random.html
+++ b/main/_modules/torch/xpu/random.html
@@ -299,7 +299,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/_modules/torch/xpu/streams.html b/main/_modules/torch/xpu/streams.html
index d38e6dd7e7a..0e80efe730c 100644
--- a/main/_modules/torch/xpu/streams.html
+++ b/main/_modules/torch/xpu/streams.html
@@ -299,7 +299,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/_modules/typing.html b/main/_modules/typing.html
index d00f489c7e9..7899e69502a 100644
--- a/main/_modules/typing.html
+++ b/main/_modules/typing.html
@@ -299,7 +299,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/_sources/generated/exportdb/index.rst.txt b/main/_sources/generated/exportdb/index.rst.txt
index 5f105d85dd8..b9393b07b61 100644
--- a/main/_sources/generated/exportdb/index.rst.txt
+++ b/main/_sources/generated/exportdb/index.rst.txt
@@ -458,7 +458,7 @@ cond_closed_over_variable
 
 .. note::
 
-    Tags: :doc:`torch.cond <torch.cond>`, :doc:`python.closure <python.closure>`
+    Tags: :doc:`python.closure <python.closure>`, :doc:`torch.cond <torch.cond>`
 
     Support Level: SUPPORTED
 
@@ -669,7 +669,7 @@ constrain_as_size_example
 
 .. note::
 
-    Tags: :doc:`torch.dynamic-value <torch.dynamic-value>`, :doc:`torch.escape-hatch <torch.escape-hatch>`
+    Tags: :doc:`torch.escape-hatch <torch.escape-hatch>`, :doc:`torch.dynamic-value <torch.dynamic-value>`
 
     Support Level: SUPPORTED
 
@@ -736,7 +736,7 @@ constrain_as_value_example
 
 .. note::
 
-    Tags: :doc:`torch.dynamic-value <torch.dynamic-value>`, :doc:`torch.escape-hatch <torch.escape-hatch>`
+    Tags: :doc:`torch.escape-hatch <torch.escape-hatch>`, :doc:`torch.dynamic-value <torch.dynamic-value>`
 
     Support Level: SUPPORTED
 
@@ -1285,7 +1285,7 @@ list_contains
 
 .. note::
 
-    Tags: :doc:`python.assert <python.assert>`, :doc:`torch.dynamic-shape <torch.dynamic-shape>`, :doc:`python.data-structure <python.data-structure>`
+    Tags: :doc:`python.assert <python.assert>`, :doc:`python.data-structure <python.data-structure>`, :doc:`torch.dynamic-shape <torch.dynamic-shape>`
 
     Support Level: SUPPORTED
 
@@ -2107,6 +2107,6 @@ Result:
 
 .. code-block::
 
-    Unsupported: torch.* op returned non-Tensor int call_function <function sym_min at 0x7ff65abf64c0>
+    Unsupported: torch.* op returned non-Tensor int call_function <function sym_min at 0x7f7a79cff3a0>
 
 
diff --git a/main/_sources/generated/exportdb/python.assert.rst.txt b/main/_sources/generated/exportdb/python.assert.rst.txt
index 27f3bc55289..a07de29292f 100644
--- a/main/_sources/generated/exportdb/python.assert.rst.txt
+++ b/main/_sources/generated/exportdb/python.assert.rst.txt
@@ -54,7 +54,7 @@ list_contains
 
 .. note::
 
-    Tags: :doc:`python.assert <python.assert>`, :doc:`torch.dynamic-shape <torch.dynamic-shape>`, :doc:`python.data-structure <python.data-structure>`
+    Tags: :doc:`python.assert <python.assert>`, :doc:`python.data-structure <python.data-structure>`, :doc:`torch.dynamic-shape <torch.dynamic-shape>`
 
     Support Level: SUPPORTED
 
diff --git a/main/_sources/generated/exportdb/python.closure.rst.txt b/main/_sources/generated/exportdb/python.closure.rst.txt
index 8529db02339..810d906087a 100644
--- a/main/_sources/generated/exportdb/python.closure.rst.txt
+++ b/main/_sources/generated/exportdb/python.closure.rst.txt
@@ -5,7 +5,7 @@ cond_closed_over_variable
 
 .. note::
 
-    Tags: :doc:`torch.cond <torch.cond>`, :doc:`python.closure <python.closure>`
+    Tags: :doc:`python.closure <python.closure>`, :doc:`torch.cond <torch.cond>`
 
     Support Level: SUPPORTED
 
diff --git a/main/_sources/generated/exportdb/python.data-structure.rst.txt b/main/_sources/generated/exportdb/python.data-structure.rst.txt
index cb4f6023c0d..d164509803b 100644
--- a/main/_sources/generated/exportdb/python.data-structure.rst.txt
+++ b/main/_sources/generated/exportdb/python.data-structure.rst.txt
@@ -127,7 +127,7 @@ list_contains
 
 .. note::
 
-    Tags: :doc:`python.assert <python.assert>`, :doc:`torch.dynamic-shape <torch.dynamic-shape>`, :doc:`python.data-structure <python.data-structure>`
+    Tags: :doc:`python.assert <python.assert>`, :doc:`python.data-structure <python.data-structure>`, :doc:`torch.dynamic-shape <torch.dynamic-shape>`
 
     Support Level: SUPPORTED
 
diff --git a/main/_sources/generated/exportdb/torch.cond.rst.txt b/main/_sources/generated/exportdb/torch.cond.rst.txt
index f1e960be726..c5a2f535dd3 100644
--- a/main/_sources/generated/exportdb/torch.cond.rst.txt
+++ b/main/_sources/generated/exportdb/torch.cond.rst.txt
@@ -251,7 +251,7 @@ cond_closed_over_variable
 
 .. note::
 
-    Tags: :doc:`torch.cond <torch.cond>`, :doc:`python.closure <python.closure>`
+    Tags: :doc:`python.closure <python.closure>`, :doc:`torch.cond <torch.cond>`
 
     Support Level: SUPPORTED
 
diff --git a/main/_sources/generated/exportdb/torch.dynamic-shape.rst.txt b/main/_sources/generated/exportdb/torch.dynamic-shape.rst.txt
index 4994d68d0fb..22024c8b1e9 100644
--- a/main/_sources/generated/exportdb/torch.dynamic-shape.rst.txt
+++ b/main/_sources/generated/exportdb/torch.dynamic-shape.rst.txt
@@ -693,7 +693,7 @@ list_contains
 
 .. note::
 
-    Tags: :doc:`python.assert <python.assert>`, :doc:`torch.dynamic-shape <torch.dynamic-shape>`, :doc:`python.data-structure <python.data-structure>`
+    Tags: :doc:`python.assert <python.assert>`, :doc:`python.data-structure <python.data-structure>`, :doc:`torch.dynamic-shape <torch.dynamic-shape>`
 
     Support Level: SUPPORTED
 
diff --git a/main/_sources/generated/exportdb/torch.dynamic-value.rst.txt b/main/_sources/generated/exportdb/torch.dynamic-value.rst.txt
index 1d3015e10a6..8c272f63d95 100644
--- a/main/_sources/generated/exportdb/torch.dynamic-value.rst.txt
+++ b/main/_sources/generated/exportdb/torch.dynamic-value.rst.txt
@@ -5,7 +5,7 @@ constrain_as_size_example
 
 .. note::
 
-    Tags: :doc:`torch.dynamic-value <torch.dynamic-value>`, :doc:`torch.escape-hatch <torch.escape-hatch>`
+    Tags: :doc:`torch.escape-hatch <torch.escape-hatch>`, :doc:`torch.dynamic-value <torch.dynamic-value>`
 
     Support Level: SUPPORTED
 
@@ -72,7 +72,7 @@ constrain_as_value_example
 
 .. note::
 
-    Tags: :doc:`torch.dynamic-value <torch.dynamic-value>`, :doc:`torch.escape-hatch <torch.escape-hatch>`
+    Tags: :doc:`torch.escape-hatch <torch.escape-hatch>`, :doc:`torch.dynamic-value <torch.dynamic-value>`
 
     Support Level: SUPPORTED
 
diff --git a/main/_sources/generated/exportdb/torch.escape-hatch.rst.txt b/main/_sources/generated/exportdb/torch.escape-hatch.rst.txt
index 00e2ac7d3f1..8b9041104f4 100644
--- a/main/_sources/generated/exportdb/torch.escape-hatch.rst.txt
+++ b/main/_sources/generated/exportdb/torch.escape-hatch.rst.txt
@@ -57,7 +57,7 @@ constrain_as_size_example
 
 .. note::
 
-    Tags: :doc:`torch.dynamic-value <torch.dynamic-value>`, :doc:`torch.escape-hatch <torch.escape-hatch>`
+    Tags: :doc:`torch.escape-hatch <torch.escape-hatch>`, :doc:`torch.dynamic-value <torch.dynamic-value>`
 
     Support Level: SUPPORTED
 
@@ -124,7 +124,7 @@ constrain_as_value_example
 
 .. note::
 
-    Tags: :doc:`torch.dynamic-value <torch.dynamic-value>`, :doc:`torch.escape-hatch <torch.escape-hatch>`
+    Tags: :doc:`torch.escape-hatch <torch.escape-hatch>`, :doc:`torch.dynamic-value <torch.dynamic-value>`
 
     Support Level: SUPPORTED
 
diff --git a/main/_sources/generated/exportdb/torch.operator.rst.txt b/main/_sources/generated/exportdb/torch.operator.rst.txt
index 672d27e8b56..f79391822f0 100644
--- a/main/_sources/generated/exportdb/torch.operator.rst.txt
+++ b/main/_sources/generated/exportdb/torch.operator.rst.txt
@@ -39,4 +39,4 @@ Result:
 
 .. code-block::
 
-    Unsupported: torch.* op returned non-Tensor int call_function <function sym_min at 0x7ff65abf64c0>
+    Unsupported: torch.* op returned non-Tensor int call_function <function sym_min at 0x7f7a79cff3a0>
diff --git a/main/_sources/notes/cuda.rst.txt b/main/_sources/notes/cuda.rst.txt
index 74d0c89387f..e5c13dd924c 100644
--- a/main/_sources/notes/cuda.rst.txt
+++ b/main/_sources/notes/cuda.rst.txt
@@ -148,6 +148,9 @@ For more information about TF32, see:
 Reduced Precision Reduction in FP16 GEMMs
 -----------------------------------------
 
+(Distinct from full FP16 accumulation that is intended for hardware that has higher throughput
+with FP16 accumulation than FP32 accumulation, see :ref:`Full FP16 accumulation<fp16accumulation>`)
+
 fp16 GEMMs are potentially done with some intermediate reduced precision reductions (e.g., in fp16 rather than fp32). These selective reductions in precision can allow for higher performance on certain workloads (particularly those with a large `k` dimension) and GPU architectures at the cost of numerical precision and potential for overflow.
 
 Some example benchmark data on V100:
@@ -206,6 +209,28 @@ To toggle the reduced precision reduction flags in C++, one can do
 
   at::globalContext().setAllowBF16ReductionCuBLAS(true);
 
+.. _fp16accumulation:
+
+Full FP16 Accmumulation in FP16 GEMMs
+-------------------------------------
+
+Certain GPUs have increased performance when doing _all_ FP16 GEMM accumulation
+in FP16, at the cost of numerical precision and greater likelihood of overflow.
+Note that this setting only has an effect on GPUs of compute capability 7.0 (Volta)
+or newer.
+
+This behavior can be enabled via:
+
+.. code:: python
+
+  torch.backends.cuda.matmul.allow_fp16_accumulation = True
+
+To toggle the reduced precision reduction flags in C++, one can do
+
+.. code:: C++
+
+  at::globalContext().setAllowFP16AccumulationCuBLAS(true);
+
 Asynchronous execution
 ----------------------
 
diff --git a/main/accelerator.html b/main/accelerator.html
index 404afde4288..93dafc5db60 100644
--- a/main/accelerator.html
+++ b/main/accelerator.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/amp.html b/main/amp.html
index 203fe24074d..8365e360e99 100644
--- a/main/amp.html
+++ b/main/amp.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -662,7 +662,7 @@ <h1>Automatic Mixed Precision package - torch.amp<a class="headerlink" href="#au
 <span id="id1"></span><h2><a class="toc-backref" href="#id4">Autocasting</a><a class="headerlink" href="#autocasting" title="Permalink to this heading">¶</a></h2>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.amp.autocast_mode.is_autocast_available">
-<span class="sig-prename descclassname"><span class="pre">torch.amp.autocast_mode.</span></span><span class="sig-name descname"><span class="pre">is_autocast_available</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device_type</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/amp/autocast_mode.html#is_autocast_available"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/amp/autocast_mode.py#L28"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.amp.autocast_mode.is_autocast_available" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.amp.autocast_mode.</span></span><span class="sig-name descname"><span class="pre">is_autocast_available</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device_type</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/amp/autocast_mode.html#is_autocast_available"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/amp/autocast_mode.py#L28"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.amp.autocast_mode.is_autocast_available" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return a bool indicating if autocast is available on <code class="xref py py-attr docutils literal notranslate"><span class="pre">device_type</span></code>.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -678,7 +678,7 @@ <h1>Automatic Mixed Precision package - torch.amp<a class="headerlink" href="#au
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.autocast">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">autocast</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device_type</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">enabled</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cache_enabled</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/amp/autocast_mode.html#autocast"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/amp/autocast_mode.py#L50"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autocast" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">autocast</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device_type</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">enabled</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cache_enabled</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/amp/autocast_mode.html#autocast"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/amp/autocast_mode.py#L50"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autocast" title="Permalink to this definition">¶</a></dt>
 <dd><p>Instances of <a class="reference internal" href="#torch.autocast" title="torch.autocast"><code class="xref py py-class docutils literal notranslate"><span class="pre">autocast</span></code></a> serve as context managers or decorators that
 allow regions of your script to run in mixed precision.</p>
 <p>In these regions, ops run in an op-specific dtype chosen by autocast
@@ -844,7 +844,7 @@ <h1>Automatic Mixed Precision package - torch.amp<a class="headerlink" href="#au
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.amp.custom_fwd">
-<span class="sig-prename descclassname"><span class="pre">torch.amp.</span></span><span class="sig-name descname"><span class="pre">custom_fwd</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">fwd</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device_type</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cast_inputs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/amp/autocast_mode.html#custom_fwd"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/amp/autocast_mode.py#L470"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.amp.custom_fwd" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.amp.</span></span><span class="sig-name descname"><span class="pre">custom_fwd</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">fwd</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device_type</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cast_inputs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/amp/autocast_mode.html#custom_fwd"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/amp/autocast_mode.py#L470"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.amp.custom_fwd" title="Permalink to this definition">¶</a></dt>
 <dd><p>Create a helper decorator for <code class="docutils literal notranslate"><span class="pre">forward</span></code> methods of custom autograd functions.</p>
 <p>Autograd functions are subclasses of <a class="reference internal" href="autograd.html#torch.autograd.Function" title="torch.autograd.Function"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.autograd.Function</span></code></a>.
 See the <a class="reference internal" href="notes/amp_examples.html#amp-custom-examples"><span class="std std-ref">example page</span></a> for more detail.</p>
@@ -871,7 +871,7 @@ <h1>Automatic Mixed Precision package - torch.amp<a class="headerlink" href="#au
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.amp.custom_bwd">
-<span class="sig-prename descclassname"><span class="pre">torch.amp.</span></span><span class="sig-name descname"><span class="pre">custom_bwd</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">bwd</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device_type</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/amp/autocast_mode.html#custom_bwd"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/amp/autocast_mode.py#L529"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.amp.custom_bwd" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.amp.</span></span><span class="sig-name descname"><span class="pre">custom_bwd</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">bwd</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device_type</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/amp/autocast_mode.html#custom_bwd"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/amp/autocast_mode.py#L529"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.amp.custom_bwd" title="Permalink to this definition">¶</a></dt>
 <dd><p>Create a helper decorator for backward methods of custom autograd functions.</p>
 <p>Autograd functions are subclasses of <a class="reference internal" href="autograd.html#torch.autograd.Function" title="torch.autograd.Function"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.autograd.Function</span></code></a>.
 Ensures that <code class="docutils literal notranslate"><span class="pre">backward</span></code> executes with the same autocast state as <code class="docutils literal notranslate"><span class="pre">forward</span></code>.
@@ -887,7 +887,7 @@ <h1>Automatic Mixed Precision package - torch.amp<a class="headerlink" href="#au
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.cuda.amp.autocast">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.cuda.amp.</span></span><span class="sig-name descname"><span class="pre">autocast</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">enabled</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.float16</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cache_enabled</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/cuda/amp/autocast_mode.html#autocast"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/amp/autocast_mode.py#L12"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.amp.autocast" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.cuda.amp.</span></span><span class="sig-name descname"><span class="pre">autocast</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">enabled</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.float16</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cache_enabled</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/cuda/amp/autocast_mode.html#autocast"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/cuda/amp/autocast_mode.py#L12"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.amp.autocast" title="Permalink to this definition">¶</a></dt>
 <dd><p>See <a class="reference internal" href="#torch.autocast" title="torch.autocast"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.autocast</span></code></a>.</p>
 <p><code class="docutils literal notranslate"><span class="pre">torch.cuda.amp.autocast(args...)</span></code> is deprecated. Please use <code class="docutils literal notranslate"><span class="pre">torch.amp.autocast(&quot;cuda&quot;,</span> <span class="pre">args...)</span></code> instead.</p>
 <dl class="field-list simple">
@@ -896,21 +896,21 @@ <h1>Automatic Mixed Precision package - torch.amp<a class="headerlink" href="#au
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.cuda.amp.custom_fwd">
-<span class="sig-prename descclassname"><span class="pre">torch.cuda.amp.</span></span><span class="sig-name descname"><span class="pre">custom_fwd</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">fwd</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cast_inputs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/cuda/amp/autocast_mode.html#custom_fwd"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/amp/autocast_mode.py#L65"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.amp.custom_fwd" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.cuda.amp.</span></span><span class="sig-name descname"><span class="pre">custom_fwd</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">fwd</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cast_inputs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/cuda/amp/autocast_mode.html#custom_fwd"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/cuda/amp/autocast_mode.py#L65"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.amp.custom_fwd" title="Permalink to this definition">¶</a></dt>
 <dd><p><code class="docutils literal notranslate"><span class="pre">torch.cuda.amp.custom_fwd(args...)</span></code> is deprecated. Please use
 <code class="docutils literal notranslate"><span class="pre">torch.amp.custom_fwd(args...,</span> <span class="pre">device_type='cuda')</span></code> instead.</p>
 </dd></dl>
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.cuda.amp.custom_bwd">
-<span class="sig-prename descclassname"><span class="pre">torch.cuda.amp.</span></span><span class="sig-name descname"><span class="pre">custom_bwd</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">bwd</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/cuda/amp/autocast_mode.html#custom_bwd"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/amp/autocast_mode.py#L80"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.amp.custom_bwd" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.cuda.amp.</span></span><span class="sig-name descname"><span class="pre">custom_bwd</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">bwd</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/cuda/amp/autocast_mode.html#custom_bwd"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/cuda/amp/autocast_mode.py#L80"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.amp.custom_bwd" title="Permalink to this definition">¶</a></dt>
 <dd><p><code class="docutils literal notranslate"><span class="pre">torch.cuda.amp.custom_bwd(args...)</span></code> is deprecated. Please use
 <code class="docutils literal notranslate"><span class="pre">torch.amp.custom_bwd(args...,</span> <span class="pre">device_type='cuda')</span></code> instead.</p>
 </dd></dl>
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.cpu.amp.autocast">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.cpu.amp.</span></span><span class="sig-name descname"><span class="pre">autocast</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">enabled</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.bfloat16</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cache_enabled</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/cpu/amp/autocast_mode.html#autocast"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cpu/amp/autocast_mode.py#L11"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cpu.amp.autocast" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.cpu.amp.</span></span><span class="sig-name descname"><span class="pre">autocast</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">enabled</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.bfloat16</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cache_enabled</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/cpu/amp/autocast_mode.html#autocast"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/cpu/amp/autocast_mode.py#L11"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cpu.amp.autocast" title="Permalink to this definition">¶</a></dt>
 <dd><p>See <a class="reference internal" href="#torch.autocast" title="torch.autocast"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.autocast</span></code></a>.
 <code class="docutils literal notranslate"><span class="pre">torch.cpu.amp.autocast(args...)</span></code> is deprecated. Please use <code class="docutils literal notranslate"><span class="pre">torch.amp.autocast(&quot;cpu&quot;,</span> <span class="pre">args...)</span></code> instead.</p>
 <dl class="field-list simple">
@@ -941,7 +941,7 @@ <h1>Automatic Mixed Precision package - torch.amp<a class="headerlink" href="#au
 </div>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.cuda.amp.GradScaler">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.cuda.amp.</span></span><span class="sig-name descname"><span class="pre">GradScaler</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">init_scale</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">65536.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">growth_factor</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">backoff_factor</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">growth_interval</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2000</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">enabled</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/cuda/amp/grad_scaler.html#GradScaler"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/amp/grad_scaler.py#L12"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.amp.GradScaler" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.cuda.amp.</span></span><span class="sig-name descname"><span class="pre">GradScaler</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">init_scale</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">65536.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">growth_factor</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">backoff_factor</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">growth_interval</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2000</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">enabled</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/cuda/amp/grad_scaler.html#GradScaler"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/cuda/amp/grad_scaler.py#L12"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.amp.GradScaler" title="Permalink to this definition">¶</a></dt>
 <dd><p>See <code class="xref py py-class docutils literal notranslate"><span class="pre">torch.amp.GradScaler</span></code>.
 <code class="docutils literal notranslate"><span class="pre">torch.cuda.amp.GradScaler(args...)</span></code> is deprecated. Please use <code class="docutils literal notranslate"><span class="pre">torch.amp.GradScaler(&quot;cuda&quot;,</span> <span class="pre">args...)</span></code> instead.</p>
 <dl class="field-list simple">
@@ -950,7 +950,7 @@ <h1>Automatic Mixed Precision package - torch.amp<a class="headerlink" href="#au
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.cpu.amp.GradScaler">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.cpu.amp.</span></span><span class="sig-name descname"><span class="pre">GradScaler</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">init_scale</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">65536.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">growth_factor</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">backoff_factor</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">growth_interval</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2000</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">enabled</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/cpu/amp/grad_scaler.html#GradScaler"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cpu/amp/grad_scaler.py#L9"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cpu.amp.GradScaler" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.cpu.amp.</span></span><span class="sig-name descname"><span class="pre">GradScaler</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">init_scale</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">65536.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">growth_factor</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">backoff_factor</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">growth_interval</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2000</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">enabled</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/cpu/amp/grad_scaler.html#GradScaler"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/cpu/amp/grad_scaler.py#L9"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cpu.amp.GradScaler" title="Permalink to this definition">¶</a></dt>
 <dd><p>See <code class="xref py py-class docutils literal notranslate"><span class="pre">torch.amp.GradScaler</span></code>.
 <code class="docutils literal notranslate"><span class="pre">torch.cpu.amp.GradScaler(args...)</span></code> is deprecated. Please use <code class="docutils literal notranslate"><span class="pre">torch.amp.GradScaler(&quot;cpu&quot;,</span> <span class="pre">args...)</span></code> instead.</p>
 <dl class="field-list simple">
diff --git a/main/autograd.html b/main/autograd.html
index b5567538626..1ef12cc0061 100644
--- a/main/autograd.html
+++ b/main/autograd.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -823,7 +823,7 @@ <h2>Tensor autograd functions<a class="headerlink" href="#tensor-autograd-functi
 <h2><span class="hidden-section">Function</span><a class="headerlink" href="#function" title="Permalink to this heading">¶</a></h2>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.autograd.Function">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.autograd.</span></span><span class="sig-name descname"><span class="pre">Function</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/autograd/function.html#Function"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/autograd/function.py#L467"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.Function" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.autograd.</span></span><span class="sig-name descname"><span class="pre">Function</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/autograd/function.html#Function"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/autograd/function.py#L467"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.Function" title="Permalink to this definition">¶</a></dt>
 <dd><p>Base class to create custom <cite>autograd.Function</cite>.</p>
 <p>To create a custom <cite>autograd.Function</cite>, subclass this class and implement
 the <a class="reference internal" href="generated/torch.autograd.Function.forward.html#torch.autograd.Function.forward" title="torch.autograd.Function.forward"><code class="xref py py-meth docutils literal notranslate"><span class="pre">forward()</span></code></a> and <a class="reference internal" href="generated/torch.autograd.backward.html#torch.autograd.backward" title="torch.autograd.backward"><code class="xref py py-meth docutils literal notranslate"><span class="pre">backward()</span></code></a> static methods. Then, to use your custom
@@ -941,7 +941,7 @@ <h2>Profiler<a class="headerlink" href="#profiler" title="Permalink to this head
 <a class="reference internal" href="#torch.autograd.profiler.emit_itt" title="torch.autograd.profiler.emit_itt"><code class="xref py py-class docutils literal notranslate"><span class="pre">emit_itt</span></code></a>.</p>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.autograd.profiler.profile">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.autograd.profiler.</span></span><span class="sig-name descname"><span class="pre">profile</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">enabled</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_cuda</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">record_shapes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">with_flops</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">profile_memory</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">with_stack</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">with_modules</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_kineto</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_cpu</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">experimental_config</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">acc_events</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">custom_trace_id_callback</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/autograd/profiler.html#profile"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/autograd/profiler.py#L107"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.profiler.profile" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.autograd.profiler.</span></span><span class="sig-name descname"><span class="pre">profile</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">enabled</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_cuda</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">record_shapes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">with_flops</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">profile_memory</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">with_stack</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">with_modules</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_kineto</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_cpu</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">experimental_config</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">acc_events</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">custom_trace_id_callback</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/autograd/profiler.html#profile"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/autograd/profiler.py#L107"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.profiler.profile" title="Permalink to this definition">¶</a></dt>
 <dd><p>Context manager that manages autograd profiler state and holds a summary of results.</p>
 <p>Under the hood it just records events of functions being executed in C++ and
 exposes those events to Python. You can wrap any code into it and it will
@@ -1050,7 +1050,7 @@ <h2>Profiler<a class="headerlink" href="#profiler" title="Permalink to this head
 </table>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.autograd.profiler.emit_nvtx">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.autograd.profiler.</span></span><span class="sig-name descname"><span class="pre">emit_nvtx</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">enabled</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">record_shapes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/autograd/profiler.html#emit_nvtx"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/autograd/profiler.py#L890"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.profiler.emit_nvtx" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.autograd.profiler.</span></span><span class="sig-name descname"><span class="pre">emit_nvtx</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">enabled</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">record_shapes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/autograd/profiler.html#emit_nvtx"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/autograd/profiler.py#L890"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.profiler.emit_nvtx" title="Permalink to this definition">¶</a></dt>
 <dd><p>Context manager that makes every autograd operation emit an NVTX range.</p>
 <p>It is useful when running the program under nvprof:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">nvprof</span> <span class="o">--</span><span class="n">profile</span><span class="o">-</span><span class="n">from</span><span class="o">-</span><span class="n">start</span> <span class="n">off</span> <span class="o">-</span><span class="n">o</span> <span class="n">trace_name</span><span class="o">.</span><span class="n">prof</span> <span class="o">--</span> <span class="o">&lt;</span><span class="n">regular</span> <span class="n">command</span> <span class="n">here</span><span class="o">&gt;</span>
@@ -1119,7 +1119,7 @@ <h2>Profiler<a class="headerlink" href="#profiler" title="Permalink to this head
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.autograd.profiler.emit_itt">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.autograd.profiler.</span></span><span class="sig-name descname"><span class="pre">emit_itt</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">enabled</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">record_shapes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/autograd/profiler.html#emit_itt"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/autograd/profiler.py#L819"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.profiler.emit_itt" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.autograd.profiler.</span></span><span class="sig-name descname"><span class="pre">emit_itt</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">enabled</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">record_shapes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/autograd/profiler.html#emit_itt"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/autograd/profiler.py#L819"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.profiler.emit_itt" title="Permalink to this definition">¶</a></dt>
 <dd><p>Context manager that makes every autograd operation emit an ITT range.</p>
 <p>It is useful when running the program under Intel(R) VTune Profiler:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">vtune</span> <span class="o">&lt;--</span><span class="n">vtune</span><span class="o">-</span><span class="n">flags</span><span class="o">&gt;</span> <span class="o">&lt;</span><span class="n">regular</span> <span class="n">command</span> <span class="n">here</span><span class="o">&gt;</span>
@@ -1165,7 +1165,7 @@ <h2>Profiler<a class="headerlink" href="#profiler" title="Permalink to this head
 <h2>Debugging and anomaly detection<a class="headerlink" href="#debugging-and-anomaly-detection" title="Permalink to this heading">¶</a></h2>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.autograd.detect_anomaly">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.autograd.</span></span><span class="sig-name descname"><span class="pre">detect_anomaly</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">check_nan</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/autograd/anomaly_mode.html#detect_anomaly"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/autograd/anomaly_mode.py#L11"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.detect_anomaly" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.autograd.</span></span><span class="sig-name descname"><span class="pre">detect_anomaly</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">check_nan</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/autograd/anomaly_mode.html#detect_anomaly"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/autograd/anomaly_mode.py#L11"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.detect_anomaly" title="Permalink to this definition">¶</a></dt>
 <dd><p>Context-manager that enable anomaly detection for the autograd engine.</p>
 <p>This does two things:</p>
 <ul class="simple">
@@ -1235,7 +1235,7 @@ <h2>Debugging and anomaly detection<a class="headerlink" href="#debugging-and-an
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.autograd.set_detect_anomaly">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.autograd.</span></span><span class="sig-name descname"><span class="pre">set_detect_anomaly</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mode</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">check_nan</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/autograd/anomaly_mode.html#set_detect_anomaly"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/autograd/anomaly_mode.py#L95"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.set_detect_anomaly" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.autograd.</span></span><span class="sig-name descname"><span class="pre">set_detect_anomaly</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mode</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">check_nan</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/autograd/anomaly_mode.html#set_detect_anomaly"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/autograd/anomaly_mode.py#L95"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.set_detect_anomaly" title="Permalink to this definition">¶</a></dt>
 <dd><p>Context-manager that sets the anomaly detection for the autograd engine on or off.</p>
 <p><code class="docutils literal notranslate"><span class="pre">set_detect_anomaly</span></code> will enable or disable the autograd anomaly detection
 based on its argument <code class="xref py py-attr docutils literal notranslate"><span class="pre">mode</span></code>.
@@ -1311,7 +1311,7 @@ <h2>Autograd graph<a class="headerlink" href="#autograd-graph" title="Permalink
 Also see <a class="reference internal" href="notes/autograd.html#saved-tensors-hooks-doc"><span class="std std-ref">Hooks for saved tensors</span></a>.</p>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.autograd.graph.saved_tensors_hooks">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.autograd.graph.</span></span><span class="sig-name descname"><span class="pre">saved_tensors_hooks</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">pack_hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">unpack_hook</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/autograd/graph.html#saved_tensors_hooks"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/autograd/graph.py#L238"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.graph.saved_tensors_hooks" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.autograd.graph.</span></span><span class="sig-name descname"><span class="pre">saved_tensors_hooks</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">pack_hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">unpack_hook</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/autograd/graph.html#saved_tensors_hooks"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/autograd/graph.py#L238"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.graph.saved_tensors_hooks" title="Permalink to this definition">¶</a></dt>
 <dd><p>Context-manager that sets a pair of pack / unpack hooks for saved tensors.</p>
 <p>Use this context-manager to define how intermediary results of an operation
 should be packed before saving, and unpacked on retrieval.</p>
@@ -1372,7 +1372,7 @@ <h2>Autograd graph<a class="headerlink" href="#autograd-graph" title="Permalink
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.autograd.graph.save_on_cpu">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.autograd.graph.</span></span><span class="sig-name descname"><span class="pre">save_on_cpu</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">pin_memory</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device_type</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'cuda'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/autograd/graph.html#save_on_cpu"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/autograd/graph.py#L317"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.graph.save_on_cpu" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.autograd.graph.</span></span><span class="sig-name descname"><span class="pre">save_on_cpu</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">pin_memory</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device_type</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'cuda'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/autograd/graph.html#save_on_cpu"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/autograd/graph.py#L317"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.graph.save_on_cpu" title="Permalink to this definition">¶</a></dt>
 <dd><p>Context manager under which tensors saved by the forward pass will be stored on cpu, then retrieved for backward.</p>
 <p>When performing operations within this context manager, intermediary
 results saved in the graph during the forward pass will be moved to CPU,
@@ -1412,7 +1412,7 @@ <h2>Autograd graph<a class="headerlink" href="#autograd-graph" title="Permalink
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.autograd.graph.disable_saved_tensors_hooks">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.autograd.graph.</span></span><span class="sig-name descname"><span class="pre">disable_saved_tensors_hooks</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">error_message</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/autograd/graph.html#disable_saved_tensors_hooks"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/autograd/graph.py#L380"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.graph.disable_saved_tensors_hooks" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.autograd.graph.</span></span><span class="sig-name descname"><span class="pre">disable_saved_tensors_hooks</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">error_message</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/autograd/graph.html#disable_saved_tensors_hooks"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/autograd/graph.py#L380"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.graph.disable_saved_tensors_hooks" title="Permalink to this definition">¶</a></dt>
 <dd><p>Context-manager that disables the saved tensors default hooks feature.</p>
 <p>Useful for if you are creating a feature that does not work with saved
 tensors default hooks.</p>
@@ -1438,7 +1438,7 @@ <h2>Autograd graph<a class="headerlink" href="#autograd-graph" title="Permalink
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.autograd.graph.register_multi_grad_hook">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.autograd.graph.</span></span><span class="sig-name descname"><span class="pre">register_multi_grad_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tensors</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fn</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'all'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/autograd/graph.html#register_multi_grad_hook"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/autograd/graph.py#L433"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.graph.register_multi_grad_hook" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.autograd.graph.</span></span><span class="sig-name descname"><span class="pre">register_multi_grad_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tensors</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fn</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'all'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/autograd/graph.html#register_multi_grad_hook"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/autograd/graph.py#L433"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.graph.register_multi_grad_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register a multi-grad backward hook.</p>
 <p>There are two supported modes: <code class="docutils literal notranslate"><span class="pre">&quot;all&quot;</span></code> and <code class="docutils literal notranslate"><span class="pre">&quot;any&quot;</span></code>.</p>
 <p>Under the <code class="docutils literal notranslate"><span class="pre">&quot;all&quot;</span></code> mode, the hook will be called after gradients with respect to every tensor in
@@ -1489,7 +1489,7 @@ <h2>Autograd graph<a class="headerlink" href="#autograd-graph" title="Permalink
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.autograd.graph.allow_mutation_on_saved_tensors">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.autograd.graph.</span></span><span class="sig-name descname"><span class="pre">allow_mutation_on_saved_tensors</span></span><a class="reference internal" href="_modules/torch/autograd/graph.html#allow_mutation_on_saved_tensors"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/autograd/graph.py#L717"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.graph.allow_mutation_on_saved_tensors" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.autograd.graph.</span></span><span class="sig-name descname"><span class="pre">allow_mutation_on_saved_tensors</span></span><a class="reference internal" href="_modules/torch/autograd/graph.html#allow_mutation_on_saved_tensors"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/autograd/graph.py#L717"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.graph.allow_mutation_on_saved_tensors" title="Permalink to this definition">¶</a></dt>
 <dd><p>Context manager under which mutating tensors saved for backward is allowed.</p>
 <p>Under this context manager, tensors saved for backward are cloned on mutation,
 so the original version can still be used during backward. Normally, mutating a tensor
@@ -1525,7 +1525,7 @@ <h2>Autograd graph<a class="headerlink" href="#autograd-graph" title="Permalink
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.autograd.graph.GradientEdge">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.autograd.graph.</span></span><span class="sig-name descname"><span class="pre">GradientEdge</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">node</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_nr</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/autograd/graph.html#GradientEdge"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/autograd/graph.py#L188"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.graph.GradientEdge" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.autograd.graph.</span></span><span class="sig-name descname"><span class="pre">GradientEdge</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">node</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_nr</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/autograd/graph.html#GradientEdge"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/autograd/graph.py#L188"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.graph.GradientEdge" title="Permalink to this definition">¶</a></dt>
 <dd><p>Object representing a given gradient edge within the autograd graph.</p>
 <p>To get the gradient edge where a given Tensor gradient will be computed,
 you can do <code class="docutils literal notranslate"><span class="pre">edge</span> <span class="pre">=</span> <span class="pre">autograd.graph.get_gradient_edge(tensor)</span></code>.</p>
@@ -1535,7 +1535,7 @@ <h2>Autograd graph<a class="headerlink" href="#autograd-graph" title="Permalink
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.autograd.graph.get_gradient_edge">
-<span class="sig-prename descclassname"><span class="pre">torch.autograd.graph.</span></span><span class="sig-name descname"><span class="pre">get_gradient_edge</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tensor</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/autograd/graph.html#get_gradient_edge"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/autograd/graph.py#L199"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.graph.get_gradient_edge" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.autograd.graph.</span></span><span class="sig-name descname"><span class="pre">get_gradient_edge</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tensor</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/autograd/graph.html#get_gradient_edge"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/autograd/graph.py#L199"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.graph.get_gradient_edge" title="Permalink to this definition">¶</a></dt>
 <dd><p>Get the gradient edge for computing the gradient of the given Tensor.</p>
 <p>In particular, it is equivalent to call
 <code class="docutils literal notranslate"><span class="pre">g</span> <span class="pre">=</span> <span class="pre">autograd.grad(loss,</span> <span class="pre">input)</span></code> and <code class="docutils literal notranslate"><span class="pre">g</span> <span class="pre">=</span> <span class="pre">autograd.grad(loss,</span> <span class="pre">get_gradient_edge(input))</span></code>.</p>
diff --git a/main/backends.html b/main/backends.html
index e3f9fb846a9..4ddbaf693fb 100644
--- a/main/backends.html
+++ b/main/backends.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -628,7 +628,7 @@
 <span id="torch-backends-cpu"></span><h2>torch.backends.cpu<a class="headerlink" href="#module-torch.backends.cpu" title="Permalink to this heading">¶</a></h2>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.backends.cpu.get_cpu_capability">
-<span class="sig-prename descclassname"><span class="pre">torch.backends.cpu.</span></span><span class="sig-name descname"><span class="pre">get_cpu_capability</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/backends/cpu.html#get_cpu_capability"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/backends/cpu/__init__.py#L9"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.backends.cpu.get_cpu_capability" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.backends.cpu.</span></span><span class="sig-name descname"><span class="pre">get_cpu_capability</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/backends/cpu.html#get_cpu_capability"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/backends/cpu/__init__.py#L9"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.backends.cpu.get_cpu_capability" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return cpu capability as a string value.</p>
 <p>Possible values:
 - “DEFAULT”
@@ -650,7 +650,7 @@
 <span id="torch-backends-cuda"></span><h2>torch.backends.cuda<a class="headerlink" href="#module-torch.backends.cuda" title="Permalink to this heading">¶</a></h2>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.backends.cuda.is_built">
-<span class="sig-prename descclassname"><span class="pre">torch.backends.cuda.</span></span><span class="sig-name descname"><span class="pre">is_built</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/backends/cuda.html#is_built"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/backends/cuda/__init__.py#L39"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.backends.cuda.is_built" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.backends.cuda.</span></span><span class="sig-name descname"><span class="pre">is_built</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/backends/cuda.html#is_built"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/backends/cuda/__init__.py#L39"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.backends.cuda.is_built" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return whether PyTorch is built with CUDA support.</p>
 <p>Note that this doesn’t necessarily mean CUDA is available; just that if this PyTorch
 binary were run on a machine with working CUDA drivers and devices, we would be able to use it.</p>
@@ -702,7 +702,7 @@
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.backends.cuda.preferred_blas_library">
-<span class="sig-prename descclassname"><span class="pre">torch.backends.cuda.</span></span><span class="sig-name descname"><span class="pre">preferred_blas_library</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">backend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/backends/cuda.html#preferred_blas_library"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/backends/cuda/__init__.py#L225"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.backends.cuda.preferred_blas_library" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.backends.cuda.</span></span><span class="sig-name descname"><span class="pre">preferred_blas_library</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">backend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/backends/cuda.html#preferred_blas_library"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/backends/cuda/__init__.py#L229"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.backends.cuda.preferred_blas_library" title="Permalink to this definition">¶</a></dt>
 <dd><p>Override the library PyTorch uses for BLAS operations. Choose between cuBLAS, cuBLASLt, and CK [ROCm-only].</p>
 <div class="admonition warning">
 <p class="admonition-title">Warning</p>
@@ -734,7 +734,7 @@
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.backends.cuda.preferred_rocm_fa_library">
-<span class="sig-prename descclassname"><span class="pre">torch.backends.cuda.</span></span><span class="sig-name descname"><span class="pre">preferred_rocm_fa_library</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">backend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/backends/cuda.html#preferred_rocm_fa_library"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/backends/cuda/__init__.py#L279"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.backends.cuda.preferred_rocm_fa_library" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.backends.cuda.</span></span><span class="sig-name descname"><span class="pre">preferred_rocm_fa_library</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">backend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/backends/cuda.html#preferred_rocm_fa_library"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/backends/cuda/__init__.py#L283"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.backends.cuda.preferred_rocm_fa_library" title="Permalink to this definition">¶</a></dt>
 <dd><p>[ROCm-only]
 Override the backend PyTorch uses in ROCm environments for Flash Attention. Choose between AOTriton and CK</p>
 <div class="admonition warning">
@@ -764,7 +764,7 @@
 
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 <dt class="sig sig-object py" id="torch.backends.cuda.preferred_linalg_library">
-<span class="sig-prename descclassname"><span class="pre">torch.backends.cuda.</span></span><span class="sig-name descname"><span class="pre">preferred_linalg_library</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">backend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/backends/cuda.html#preferred_linalg_library"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/backends/cuda/__init__.py#L156"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.backends.cuda.preferred_linalg_library" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.backends.cuda.</span></span><span class="sig-name descname"><span class="pre">preferred_linalg_library</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">backend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/backends/cuda.html#preferred_linalg_library"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/backends/cuda/__init__.py#L160"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.backends.cuda.preferred_linalg_library" title="Permalink to this definition">¶</a></dt>
 <dd><p>Override the heuristic PyTorch uses to choose between cuSOLVER and MAGMA for CUDA linear algebra operations.</p>
 <div class="admonition warning">
 <p class="admonition-title">Warning</p>
@@ -819,7 +819,7 @@
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.backends.cuda.flash_sdp_enabled">
-<span class="sig-prename descclassname"><span class="pre">torch.backends.cuda.</span></span><span class="sig-name descname"><span class="pre">flash_sdp_enabled</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/backends/cuda.html#flash_sdp_enabled"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/backends/cuda/__init__.py#L324"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.backends.cuda.flash_sdp_enabled" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.backends.cuda.</span></span><span class="sig-name descname"><span class="pre">flash_sdp_enabled</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/backends/cuda.html#flash_sdp_enabled"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/backends/cuda/__init__.py#L328"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.backends.cuda.flash_sdp_enabled" title="Permalink to this definition">¶</a></dt>
 <dd><div class="admonition warning">
 <p class="admonition-title">Warning</p>
 <p>This flag is beta and subject to change.</p>
@@ -829,7 +829,7 @@
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.backends.cuda.enable_mem_efficient_sdp">
-<span class="sig-prename descclassname"><span class="pre">torch.backends.cuda.</span></span><span class="sig-name descname"><span class="pre">enable_mem_efficient_sdp</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">enabled</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/backends/cuda.html#enable_mem_efficient_sdp"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/backends/cuda/__init__.py#L351"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.backends.cuda.enable_mem_efficient_sdp" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.backends.cuda.</span></span><span class="sig-name descname"><span class="pre">enable_mem_efficient_sdp</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">enabled</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/backends/cuda.html#enable_mem_efficient_sdp"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/backends/cuda/__init__.py#L355"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.backends.cuda.enable_mem_efficient_sdp" title="Permalink to this definition">¶</a></dt>
 <dd><div class="admonition warning">
 <p class="admonition-title">Warning</p>
 <p>This flag is beta and subject to change.</p>
@@ -841,7 +841,7 @@
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.backends.cuda.mem_efficient_sdp_enabled">
-<span class="sig-prename descclassname"><span class="pre">torch.backends.cuda.</span></span><span class="sig-name descname"><span class="pre">mem_efficient_sdp_enabled</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/backends/cuda.html#mem_efficient_sdp_enabled"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/backends/cuda/__init__.py#L342"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.backends.cuda.mem_efficient_sdp_enabled" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.backends.cuda.</span></span><span class="sig-name descname"><span class="pre">mem_efficient_sdp_enabled</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/backends/cuda.html#mem_efficient_sdp_enabled"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/backends/cuda/__init__.py#L346"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.backends.cuda.mem_efficient_sdp_enabled" title="Permalink to this definition">¶</a></dt>
 <dd><div class="admonition warning">
 <p class="admonition-title">Warning</p>
 <p>This flag is beta and subject to change.</p>
@@ -851,7 +851,7 @@
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.backends.cuda.enable_flash_sdp">
-<span class="sig-prename descclassname"><span class="pre">torch.backends.cuda.</span></span><span class="sig-name descname"><span class="pre">enable_flash_sdp</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">enabled</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/backends/cuda.html#enable_flash_sdp"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/backends/cuda/__init__.py#L333"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.backends.cuda.enable_flash_sdp" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.backends.cuda.</span></span><span class="sig-name descname"><span class="pre">enable_flash_sdp</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">enabled</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/backends/cuda.html#enable_flash_sdp"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/backends/cuda/__init__.py#L337"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.backends.cuda.enable_flash_sdp" title="Permalink to this definition">¶</a></dt>
 <dd><div class="admonition warning">
 <p class="admonition-title">Warning</p>
 <p>This flag is beta and subject to change.</p>
@@ -863,7 +863,7 @@
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.backends.cuda.math_sdp_enabled">
-<span class="sig-prename descclassname"><span class="pre">torch.backends.cuda.</span></span><span class="sig-name descname"><span class="pre">math_sdp_enabled</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/backends/cuda.html#math_sdp_enabled"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/backends/cuda/__init__.py#L360"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.backends.cuda.math_sdp_enabled" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.backends.cuda.</span></span><span class="sig-name descname"><span class="pre">math_sdp_enabled</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/backends/cuda.html#math_sdp_enabled"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/backends/cuda/__init__.py#L364"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.backends.cuda.math_sdp_enabled" title="Permalink to this definition">¶</a></dt>
 <dd><div class="admonition warning">
 <p class="admonition-title">Warning</p>
 <p>This flag is beta and subject to change.</p>
@@ -873,7 +873,7 @@
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.backends.cuda.enable_math_sdp">
-<span class="sig-prename descclassname"><span class="pre">torch.backends.cuda.</span></span><span class="sig-name descname"><span class="pre">enable_math_sdp</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">enabled</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/backends/cuda.html#enable_math_sdp"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/backends/cuda/__init__.py#L369"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.backends.cuda.enable_math_sdp" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.backends.cuda.</span></span><span class="sig-name descname"><span class="pre">enable_math_sdp</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">enabled</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/backends/cuda.html#enable_math_sdp"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/backends/cuda/__init__.py#L373"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.backends.cuda.enable_math_sdp" title="Permalink to this definition">¶</a></dt>
 <dd><div class="admonition warning">
 <p class="admonition-title">Warning</p>
 <p>This flag is beta and subject to change.</p>
@@ -885,7 +885,7 @@
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.backends.cuda.fp16_bf16_reduction_math_sdp_allowed">
-<span class="sig-prename descclassname"><span class="pre">torch.backends.cuda.</span></span><span class="sig-name descname"><span class="pre">fp16_bf16_reduction_math_sdp_allowed</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/backends/cuda.html#fp16_bf16_reduction_math_sdp_allowed"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/backends/cuda/__init__.py#L387"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.backends.cuda.fp16_bf16_reduction_math_sdp_allowed" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.backends.cuda.</span></span><span class="sig-name descname"><span class="pre">fp16_bf16_reduction_math_sdp_allowed</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/backends/cuda.html#fp16_bf16_reduction_math_sdp_allowed"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/backends/cuda/__init__.py#L391"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.backends.cuda.fp16_bf16_reduction_math_sdp_allowed" title="Permalink to this definition">¶</a></dt>
 <dd><div class="admonition warning">
 <p class="admonition-title">Warning</p>
 <p>This flag is beta and subject to change.</p>
@@ -895,7 +895,7 @@
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.backends.cuda.allow_fp16_bf16_reduction_math_sdp">
-<span class="sig-prename descclassname"><span class="pre">torch.backends.cuda.</span></span><span class="sig-name descname"><span class="pre">allow_fp16_bf16_reduction_math_sdp</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">enabled</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/backends/cuda.html#allow_fp16_bf16_reduction_math_sdp"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/backends/cuda/__init__.py#L378"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.backends.cuda.allow_fp16_bf16_reduction_math_sdp" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.backends.cuda.</span></span><span class="sig-name descname"><span class="pre">allow_fp16_bf16_reduction_math_sdp</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">enabled</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/backends/cuda.html#allow_fp16_bf16_reduction_math_sdp"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/backends/cuda/__init__.py#L382"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.backends.cuda.allow_fp16_bf16_reduction_math_sdp" title="Permalink to this definition">¶</a></dt>
 <dd><div class="admonition warning">
 <p class="admonition-title">Warning</p>
 <p>This flag is beta and subject to change.</p>
@@ -907,7 +907,7 @@
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.backends.cuda.cudnn_sdp_enabled">
-<span class="sig-prename descclassname"><span class="pre">torch.backends.cuda.</span></span><span class="sig-name descname"><span class="pre">cudnn_sdp_enabled</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/backends/cuda.html#cudnn_sdp_enabled"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/backends/cuda/__init__.py#L469"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.backends.cuda.cudnn_sdp_enabled" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.backends.cuda.</span></span><span class="sig-name descname"><span class="pre">cudnn_sdp_enabled</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/backends/cuda.html#cudnn_sdp_enabled"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/backends/cuda/__init__.py#L473"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.backends.cuda.cudnn_sdp_enabled" title="Permalink to this definition">¶</a></dt>
 <dd><div class="admonition warning">
 <p class="admonition-title">Warning</p>
 <p>This flag is beta and subject to change.</p>
@@ -917,7 +917,7 @@
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.backends.cuda.enable_cudnn_sdp">
-<span class="sig-prename descclassname"><span class="pre">torch.backends.cuda.</span></span><span class="sig-name descname"><span class="pre">enable_cudnn_sdp</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">enabled</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/backends/cuda.html#enable_cudnn_sdp"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/backends/cuda/__init__.py#L478"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.backends.cuda.enable_cudnn_sdp" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.backends.cuda.</span></span><span class="sig-name descname"><span class="pre">enable_cudnn_sdp</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">enabled</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/backends/cuda.html#enable_cudnn_sdp"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/backends/cuda/__init__.py#L482"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.backends.cuda.enable_cudnn_sdp" title="Permalink to this definition">¶</a></dt>
 <dd><div class="admonition warning">
 <p class="admonition-title">Warning</p>
 <p>This flag is beta and subject to change.</p>
@@ -929,7 +929,7 @@
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.backends.cuda.is_flash_attention_available">
-<span class="sig-prename descclassname"><span class="pre">torch.backends.cuda.</span></span><span class="sig-name descname"><span class="pre">is_flash_attention_available</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/backends/cuda.html#is_flash_attention_available"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/backends/cuda/__init__.py#L396"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.backends.cuda.is_flash_attention_available" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.backends.cuda.</span></span><span class="sig-name descname"><span class="pre">is_flash_attention_available</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/backends/cuda.html#is_flash_attention_available"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/backends/cuda/__init__.py#L400"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.backends.cuda.is_flash_attention_available" title="Permalink to this definition">¶</a></dt>
 <dd><p>Check if PyTorch was built with FlashAttention for scaled_dot_product_attention.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Returns</dt>
@@ -948,7 +948,7 @@
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.backends.cuda.can_use_flash_attention">
-<span class="sig-prename descclassname"><span class="pre">torch.backends.cuda.</span></span><span class="sig-name descname"><span class="pre">can_use_flash_attention</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">params</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">debug</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/backends/cuda.html#can_use_flash_attention"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/backends/cuda/__init__.py#L409"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.backends.cuda.can_use_flash_attention" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.backends.cuda.</span></span><span class="sig-name descname"><span class="pre">can_use_flash_attention</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">params</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">debug</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/backends/cuda.html#can_use_flash_attention"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/backends/cuda/__init__.py#L413"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.backends.cuda.can_use_flash_attention" title="Permalink to this definition">¶</a></dt>
 <dd><p>Check if FlashAttention can be utilized in scaled_dot_product_attention.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -976,7 +976,7 @@
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.backends.cuda.can_use_efficient_attention">
-<span class="sig-prename descclassname"><span class="pre">torch.backends.cuda.</span></span><span class="sig-name descname"><span class="pre">can_use_efficient_attention</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">params</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">debug</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/backends/cuda.html#can_use_efficient_attention"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/backends/cuda/__init__.py#L429"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.backends.cuda.can_use_efficient_attention" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.backends.cuda.</span></span><span class="sig-name descname"><span class="pre">can_use_efficient_attention</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">params</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">debug</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/backends/cuda.html#can_use_efficient_attention"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/backends/cuda/__init__.py#L433"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.backends.cuda.can_use_efficient_attention" title="Permalink to this definition">¶</a></dt>
 <dd><p>Check if efficient_attention can be utilized in scaled_dot_product_attention.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -1004,7 +1004,7 @@
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.backends.cuda.can_use_cudnn_attention">
-<span class="sig-prename descclassname"><span class="pre">torch.backends.cuda.</span></span><span class="sig-name descname"><span class="pre">can_use_cudnn_attention</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">params</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">debug</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/backends/cuda.html#can_use_cudnn_attention"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/backends/cuda/__init__.py#L449"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.backends.cuda.can_use_cudnn_attention" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.backends.cuda.</span></span><span class="sig-name descname"><span class="pre">can_use_cudnn_attention</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">params</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">debug</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/backends/cuda.html#can_use_cudnn_attention"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/backends/cuda/__init__.py#L453"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.backends.cuda.can_use_cudnn_attention" title="Permalink to this definition">¶</a></dt>
 <dd><p>Check if cudnn_attention can be utilized in scaled_dot_product_attention.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -1032,7 +1032,7 @@
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.backends.cuda.sdp_kernel">
-<span class="sig-prename descclassname"><span class="pre">torch.backends.cuda.</span></span><span class="sig-name descname"><span class="pre">sdp_kernel</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">enable_flash</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">enable_math</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">enable_mem_efficient</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">enable_cudnn</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/backends/cuda.html#sdp_kernel"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/backends/cuda/__init__.py#L487"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.backends.cuda.sdp_kernel" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.backends.cuda.</span></span><span class="sig-name descname"><span class="pre">sdp_kernel</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">enable_flash</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">enable_math</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">enable_mem_efficient</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">enable_cudnn</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/backends/cuda.html#sdp_kernel"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/backends/cuda/__init__.py#L491"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.backends.cuda.sdp_kernel" title="Permalink to this definition">¶</a></dt>
 <dd><div class="admonition warning">
 <p class="admonition-title">Warning</p>
 <p>This flag is beta and subject to change.</p>
@@ -1048,13 +1048,13 @@
 <span id="torch-backends-cudnn"></span><h2>torch.backends.cudnn<a class="headerlink" href="#module-torch.backends.cudnn" title="Permalink to this heading">¶</a></h2>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.backends.cudnn.version">
-<span class="sig-prename descclassname"><span class="pre">torch.backends.cudnn.</span></span><span class="sig-name descname"><span class="pre">version</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/backends/cudnn.html#version"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/backends/cudnn/__init__.py#L83"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.backends.cudnn.version" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.backends.cudnn.</span></span><span class="sig-name descname"><span class="pre">version</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/backends/cudnn.html#version"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/backends/cudnn/__init__.py#L83"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.backends.cudnn.version" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the version of cuDNN.</p>
 </dd></dl>
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.backends.cudnn.is_available">
-<span class="sig-prename descclassname"><span class="pre">torch.backends.cudnn.</span></span><span class="sig-name descname"><span class="pre">is_available</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/backends/cudnn.html#is_available"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/backends/cudnn/__init__.py#L97"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.backends.cudnn.is_available" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.backends.cudnn.</span></span><span class="sig-name descname"><span class="pre">is_available</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/backends/cudnn.html#is_available"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/backends/cudnn/__init__.py#L97"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.backends.cudnn.is_available" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return a bool indicating if CUDNN is currently available.</p>
 </dd></dl>
 
@@ -1100,7 +1100,7 @@
 <span id="torch-backends-cusparselt"></span><h2>torch.backends.cusparselt<a class="headerlink" href="#module-torch.backends.cusparselt" title="Permalink to this heading">¶</a></h2>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.backends.cusparselt.version">
-<span class="sig-prename descclassname"><span class="pre">torch.backends.cusparselt.</span></span><span class="sig-name descname"><span class="pre">version</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/backends/cusparselt.html#version"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/backends/cusparselt/__init__.py#L42"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.backends.cusparselt.version" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.backends.cusparselt.</span></span><span class="sig-name descname"><span class="pre">version</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/backends/cusparselt.html#version"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/backends/cusparselt/__init__.py#L42"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.backends.cusparselt.version" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the version of cuSPARSELt</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -1111,7 +1111,7 @@
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.backends.cusparselt.is_available">
-<span class="sig-prename descclassname"><span class="pre">torch.backends.cusparselt.</span></span><span class="sig-name descname"><span class="pre">is_available</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/backends/cusparselt.html#is_available"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/backends/cusparselt/__init__.py#L49"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.backends.cusparselt.is_available" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.backends.cusparselt.</span></span><span class="sig-name descname"><span class="pre">is_available</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/backends/cusparselt.html#is_available"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/backends/cusparselt/__init__.py#L49"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.backends.cusparselt.is_available" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return a bool indicating if cuSPARSELt is currently available.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -1125,7 +1125,7 @@
 <span id="torch-backends-mha"></span><h2>torch.backends.mha<a class="headerlink" href="#module-torch.backends.mha" title="Permalink to this heading">¶</a></h2>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.backends.mha.get_fastpath_enabled">
-<span class="sig-prename descclassname"><span class="pre">torch.backends.mha.</span></span><span class="sig-name descname"><span class="pre">get_fastpath_enabled</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/backends/mha.html#get_fastpath_enabled"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/backends/mha/__init__.py#L9"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.backends.mha.get_fastpath_enabled" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.backends.mha.</span></span><span class="sig-name descname"><span class="pre">get_fastpath_enabled</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/backends/mha.html#get_fastpath_enabled"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/backends/mha/__init__.py#L9"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.backends.mha.get_fastpath_enabled" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns whether fast path for TransformerEncoder and MultiHeadAttention
 is enabled, or <code class="docutils literal notranslate"><span class="pre">True</span></code> if jit is scripting.</p>
 <div class="admonition note">
@@ -1142,7 +1142,7 @@
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.backends.mha.set_fastpath_enabled">
-<span class="sig-prename descclassname"><span class="pre">torch.backends.mha.</span></span><span class="sig-name descname"><span class="pre">set_fastpath_enabled</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/backends/mha.html#set_fastpath_enabled"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/backends/mha/__init__.py#L22"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.backends.mha.set_fastpath_enabled" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.backends.mha.</span></span><span class="sig-name descname"><span class="pre">set_fastpath_enabled</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/backends/mha.html#set_fastpath_enabled"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/backends/mha/__init__.py#L22"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.backends.mha.set_fastpath_enabled" title="Permalink to this definition">¶</a></dt>
 <dd><p>Sets whether fast path is enabled</p>
 <dl class="field-list simple">
 </dl>
@@ -1153,7 +1153,7 @@
 <span id="torch-backends-mps"></span><h2>torch.backends.mps<a class="headerlink" href="#module-torch.backends.mps" title="Permalink to this heading">¶</a></h2>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.backends.mps.is_available">
-<span class="sig-prename descclassname"><span class="pre">torch.backends.mps.</span></span><span class="sig-name descname"><span class="pre">is_available</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/backends/mps.html#is_available"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/backends/mps/__init__.py#L22"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.backends.mps.is_available" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.backends.mps.</span></span><span class="sig-name descname"><span class="pre">is_available</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/backends/mps.html#is_available"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/backends/mps/__init__.py#L22"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.backends.mps.is_available" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return a bool indicating if MPS is currently available.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -1164,7 +1164,7 @@
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.backends.mps.is_built">
-<span class="sig-prename descclassname"><span class="pre">torch.backends.mps.</span></span><span class="sig-name descname"><span class="pre">is_built</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/backends/mps.html#is_built"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/backends/mps/__init__.py#L12"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.backends.mps.is_built" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.backends.mps.</span></span><span class="sig-name descname"><span class="pre">is_built</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/backends/mps.html#is_built"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/backends/mps/__init__.py#L12"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.backends.mps.is_built" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return whether PyTorch is built with MPS support.</p>
 <p>Note that this doesn’t necessarily mean MPS is available; just that
 if this PyTorch binary were run a machine with working MPS drivers
@@ -1181,13 +1181,13 @@
 <span id="torch-backends-mkl"></span><h2>torch.backends.mkl<a class="headerlink" href="#module-torch.backends.mkl" title="Permalink to this heading">¶</a></h2>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.backends.mkl.is_available">
-<span class="sig-prename descclassname"><span class="pre">torch.backends.mkl.</span></span><span class="sig-name descname"><span class="pre">is_available</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/backends/mkl.html#is_available"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/backends/mkl/__init__.py#L5"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.backends.mkl.is_available" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.backends.mkl.</span></span><span class="sig-name descname"><span class="pre">is_available</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/backends/mkl.html#is_available"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/backends/mkl/__init__.py#L5"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.backends.mkl.is_available" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return whether PyTorch is built with MKL support.</p>
 </dd></dl>
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.backends.mkl.verbose">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.backends.mkl.</span></span><span class="sig-name descname"><span class="pre">verbose</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">enable</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/backends/mkl.html#verbose"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/backends/mkl/__init__.py#L14"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.backends.mkl.verbose" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.backends.mkl.</span></span><span class="sig-name descname"><span class="pre">verbose</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">enable</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/backends/mkl.html#verbose"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/backends/mkl/__init__.py#L14"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.backends.mkl.verbose" title="Permalink to this definition">¶</a></dt>
 <dd><p>On-demand oneMKL verbosing functionality.</p>
 <p>To make it easier to debug performance issues, oneMKL can dump verbose
 messages containing execution information like duration while executing
@@ -1219,13 +1219,13 @@
 <span id="torch-backends-mkldnn"></span><h2>torch.backends.mkldnn<a class="headerlink" href="#module-torch.backends.mkldnn" title="Permalink to this heading">¶</a></h2>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.backends.mkldnn.is_available">
-<span class="sig-prename descclassname"><span class="pre">torch.backends.mkldnn.</span></span><span class="sig-name descname"><span class="pre">is_available</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/backends/mkldnn.html#is_available"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/backends/mkldnn/__init__.py#L10"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.backends.mkldnn.is_available" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.backends.mkldnn.</span></span><span class="sig-name descname"><span class="pre">is_available</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/backends/mkldnn.html#is_available"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/backends/mkldnn/__init__.py#L10"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.backends.mkldnn.is_available" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return whether PyTorch is built with MKL-DNN support.</p>
 </dd></dl>
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.backends.mkldnn.verbose">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.backends.mkldnn.</span></span><span class="sig-name descname"><span class="pre">verbose</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">level</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/backends/mkldnn.html#verbose"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/backends/mkldnn/__init__.py#L20"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.backends.mkldnn.verbose" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.backends.mkldnn.</span></span><span class="sig-name descname"><span class="pre">verbose</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">level</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/backends/mkldnn.html#verbose"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/backends/mkldnn/__init__.py#L20"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.backends.mkldnn.verbose" title="Permalink to this definition">¶</a></dt>
 <dd><p>On-demand oneDNN (former MKL-DNN) verbosing functionality.</p>
 <p>To make it easier to debug performance issues, oneDNN can dump verbose
 messages containing information like kernel size, input data size and
@@ -1258,19 +1258,19 @@
 <span id="torch-backends-nnpack"></span><h2>torch.backends.nnpack<a class="headerlink" href="#module-torch.backends.nnpack" title="Permalink to this heading">¶</a></h2>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.backends.nnpack.is_available">
-<span class="sig-prename descclassname"><span class="pre">torch.backends.nnpack.</span></span><span class="sig-name descname"><span class="pre">is_available</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/backends/nnpack.html#is_available"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/backends/nnpack/__init__.py#L11"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.backends.nnpack.is_available" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.backends.nnpack.</span></span><span class="sig-name descname"><span class="pre">is_available</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/backends/nnpack.html#is_available"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/backends/nnpack/__init__.py#L11"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.backends.nnpack.is_available" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return whether PyTorch is built with NNPACK support.</p>
 </dd></dl>
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.backends.nnpack.flags">
-<span class="sig-prename descclassname"><span class="pre">torch.backends.nnpack.</span></span><span class="sig-name descname"><span class="pre">flags</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">enabled</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/backends/nnpack.html#flags"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/backends/nnpack/__init__.py#L23"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.backends.nnpack.flags" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.backends.nnpack.</span></span><span class="sig-name descname"><span class="pre">flags</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">enabled</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/backends/nnpack.html#flags"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/backends/nnpack/__init__.py#L23"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.backends.nnpack.flags" title="Permalink to this definition">¶</a></dt>
 <dd><p>Context manager for setting if nnpack is enabled globally</p>
 </dd></dl>
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.backends.nnpack.set_flags">
-<span class="sig-prename descclassname"><span class="pre">torch.backends.nnpack.</span></span><span class="sig-name descname"><span class="pre">set_flags</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">_enabled</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/backends/nnpack.html#set_flags"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/backends/nnpack/__init__.py#L16"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.backends.nnpack.set_flags" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.backends.nnpack.</span></span><span class="sig-name descname"><span class="pre">set_flags</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">_enabled</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/backends/nnpack.html#set_flags"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/backends/nnpack/__init__.py#L16"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.backends.nnpack.set_flags" title="Permalink to this definition">¶</a></dt>
 <dd><p>Set if nnpack is enabled globally</p>
 </dd></dl>
 
@@ -1279,7 +1279,7 @@
 <span id="torch-backends-openmp"></span><h2>torch.backends.openmp<a class="headerlink" href="#module-torch.backends.openmp" title="Permalink to this heading">¶</a></h2>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.backends.openmp.is_available">
-<span class="sig-prename descclassname"><span class="pre">torch.backends.openmp.</span></span><span class="sig-name descname"><span class="pre">is_available</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/backends/openmp.html#is_available"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/backends/openmp/__init__.py#L5"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.backends.openmp.is_available" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.backends.openmp.</span></span><span class="sig-name descname"><span class="pre">is_available</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/backends/openmp.html#is_available"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/backends/openmp/__init__.py#L5"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.backends.openmp.is_available" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return whether PyTorch is built with OpenMP support.</p>
 </dd></dl>
 
@@ -1288,7 +1288,7 @@
 <span id="torch-backends-opt-einsum"></span><h2>torch.backends.opt_einsum<a class="headerlink" href="#module-torch.backends.opt_einsum" title="Permalink to this heading">¶</a></h2>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.backends.opt_einsum.is_available">
-<span class="sig-prename descclassname"><span class="pre">torch.backends.opt_einsum.</span></span><span class="sig-name descname"><span class="pre">is_available</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/backends/opt_einsum.html#is_available"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/backends/opt_einsum/__init__.py#L17"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.backends.opt_einsum.is_available" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.backends.opt_einsum.</span></span><span class="sig-name descname"><span class="pre">is_available</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/backends/opt_einsum.html#is_available"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/backends/opt_einsum/__init__.py#L17"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.backends.opt_einsum.is_available" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return a bool indicating if opt_einsum is currently available.</p>
 <p>You must install opt-einsum in order for torch to automatically optimize einsum. To
 make opt-einsum available, you can install it along with torch: <code class="docutils literal notranslate"><span class="pre">pip</span> <span class="pre">install</span> <span class="pre">torch[opt-einsum]</span></code>
@@ -1304,7 +1304,7 @@
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.backends.opt_einsum.get_opt_einsum">
-<span class="sig-prename descclassname"><span class="pre">torch.backends.opt_einsum.</span></span><span class="sig-name descname"><span class="pre">get_opt_einsum</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/backends/opt_einsum.html#get_opt_einsum"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/backends/opt_einsum/__init__.py#L30"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.backends.opt_einsum.get_opt_einsum" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.backends.opt_einsum.</span></span><span class="sig-name descname"><span class="pre">get_opt_einsum</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/backends/opt_einsum.html#get_opt_einsum"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/backends/opt_einsum/__init__.py#L30"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.backends.opt_einsum.get_opt_einsum" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the opt_einsum package if opt_einsum is currently available, else None.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
diff --git a/main/benchmark_utils.html b/main/benchmark_utils.html
index 608bfd2c745..d22f433bb60 100644
--- a/main/benchmark_utils.html
+++ b/main/benchmark_utils.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -610,7 +610,7 @@
 <span id="benchmark-utils-torch-utils-benchmark"></span><h1>Benchmark Utils - torch.utils.benchmark<a class="headerlink" href="#module-torch.utils.benchmark" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.utils.benchmark.Timer">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.utils.benchmark.</span></span><span class="sig-name descname"><span class="pre">Timer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">stmt='pass'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">setup='pass'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">global_setup=''</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">timer=&lt;built-in</span> <span class="pre">function</span> <span class="pre">perf_counter&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">globals=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">label=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sub_label=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">description=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">env=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_threads=1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">language=Language.PYTHON</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/benchmark/utils/timer.html#Timer"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/utils/benchmark/utils/timer.py#L75"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.benchmark.Timer" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.utils.benchmark.</span></span><span class="sig-name descname"><span class="pre">Timer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">stmt='pass'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">setup='pass'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">global_setup=''</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">timer=&lt;built-in</span> <span class="pre">function</span> <span class="pre">perf_counter&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">globals=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">label=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sub_label=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">description=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">env=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_threads=1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">language=Language.PYTHON</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/benchmark/utils/timer.html#Timer"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/utils/benchmark/utils/timer.py#L75"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.benchmark.Timer" title="Permalink to this definition">¶</a></dt>
 <dd><p>Helper class for measuring execution time of PyTorch statements.</p>
 <p>For a full tutorial on how to use this class, see:
 <a class="reference external" href="https://pytorch.org/tutorials/recipes/recipes/benchmark.html">https://pytorch.org/tutorials/recipes/recipes/benchmark.html</a></p>
@@ -714,7 +714,7 @@
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.utils.benchmark.Timer.adaptive_autorange">
-<span class="sig-name descname"><span class="pre">adaptive_autorange</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">threshold</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_run_time</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.01</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_run_time</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">callback</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/benchmark/utils/timer.html#Timer.adaptive_autorange"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/utils/benchmark/utils/timer.py#L391"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.benchmark.Timer.adaptive_autorange" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">adaptive_autorange</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">threshold</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_run_time</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.01</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_run_time</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">callback</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/benchmark/utils/timer.html#Timer.adaptive_autorange"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/utils/benchmark/utils/timer.py#L391"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.benchmark.Timer.adaptive_autorange" title="Permalink to this definition">¶</a></dt>
 <dd><p>Similar to <cite>blocked_autorange</cite> but also checks for variablility in measurements
 and repeats until iqr/median is smaller than <cite>threshold</cite> or <cite>max_run_time</cite> is reached.</p>
 <p>At a high level, adaptive_autorange executes the following pseudo-code:</p>
@@ -755,7 +755,7 @@
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.utils.benchmark.Timer.blocked_autorange">
-<span class="sig-name descname"><span class="pre">blocked_autorange</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">callback</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_run_time</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.2</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/benchmark/utils/timer.html#Timer.blocked_autorange"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/utils/benchmark/utils/timer.py#L331"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.benchmark.Timer.blocked_autorange" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">blocked_autorange</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">callback</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_run_time</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.2</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/benchmark/utils/timer.html#Timer.blocked_autorange"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/utils/benchmark/utils/timer.py#L331"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.benchmark.Timer.blocked_autorange" title="Permalink to this definition">¶</a></dt>
 <dd><p>Measure many replicates while keeping timer overhead to a minimum.</p>
 <p>At a high level, blocked_autorange executes the following pseudo-code:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span>`setup`
@@ -800,7 +800,7 @@
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.utils.benchmark.Timer.collect_callgrind">
-<span class="sig-name descname"><span class="pre">collect_callgrind</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">number</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><span class="pre">int</span></a></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">repeats</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">collect_baseline</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">retain_out_file</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#torch.utils.benchmark.CallgrindStats" title="torch.utils.benchmark.utils.valgrind_wrapper.timer_interface.CallgrindStats"><span class="pre">CallgrindStats</span></a></span></span><a class="reference internal" href="_modules/torch/utils/benchmark/utils/timer.html#Timer.collect_callgrind"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/utils/benchmark/utils/timer.py#L476"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.benchmark.Timer.collect_callgrind" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">collect_callgrind</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">number</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><span class="pre">int</span></a></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">repeats</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">collect_baseline</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">retain_out_file</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#torch.utils.benchmark.CallgrindStats" title="torch.utils.benchmark.utils.valgrind_wrapper.timer_interface.CallgrindStats"><span class="pre">CallgrindStats</span></a></span></span><a class="reference internal" href="_modules/torch/utils/benchmark/utils/timer.html#Timer.collect_callgrind"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/utils/benchmark/utils/timer.py#L476"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.benchmark.Timer.collect_callgrind" title="Permalink to this definition">¶</a></dt>
 <dt class="sig sig-object py">
 <span class="sig-name descname"><span class="pre">collect_callgrind</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">number</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><span class="pre">int</span></a></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">repeats</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><span class="pre">int</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">collect_baseline</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">retain_out_file</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.13)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference internal" href="#torch.utils.benchmark.CallgrindStats" title="torch.utils.benchmark.utils.valgrind_wrapper.timer_interface.CallgrindStats"><span class="pre">torch.utils.benchmark.utils.valgrind_wrapper.timer_interface.CallgrindStats</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span></span></span></dt>
 <dd><p>Collect instruction counts using Callgrind.</p>
@@ -835,7 +835,7 @@
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.utils.benchmark.Timer.timeit">
-<span class="sig-name descname"><span class="pre">timeit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">number</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1000000</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/benchmark/utils/timer.html#Timer.timeit"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/utils/benchmark/utils/timer.py#L266"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.benchmark.Timer.timeit" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">timeit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">number</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1000000</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/benchmark/utils/timer.html#Timer.timeit"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/utils/benchmark/utils/timer.py#L266"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.benchmark.Timer.timeit" title="Permalink to this definition">¶</a></dt>
 <dd><p>Mirrors the semantics of timeit.Timer.timeit().</p>
 <p>Execute the main statement (<cite>stmt</cite>) <cite>number</cite> times.
 <a class="reference external" href="https://docs.python.org/3/library/timeit.html#timeit.Timer.timeit">https://docs.python.org/3/library/timeit.html#timeit.Timer.timeit</a></p>
@@ -850,7 +850,7 @@
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.utils.benchmark.Measurement">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.utils.benchmark.</span></span><span class="sig-name descname"><span class="pre">Measurement</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">number_per_run</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">raw_times</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">task_spec</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">metadata</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/benchmark/utils/common.html#Measurement"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/utils/benchmark/utils/common.py#L74"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.benchmark.Measurement" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.utils.benchmark.</span></span><span class="sig-name descname"><span class="pre">Measurement</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">number_per_run</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">raw_times</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">task_spec</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">metadata</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/benchmark/utils/common.html#Measurement"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/utils/benchmark/utils/common.py#L74"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.benchmark.Measurement" title="Permalink to this definition">¶</a></dt>
 <dd><p>The result of a Timer measurement.</p>
 <p>This class stores one or more measurements of a given statement. It is
 serializable and provides several convenience methods
@@ -859,7 +859,7 @@
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.utils.benchmark.Measurement.merge">
-<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">merge</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">measurements</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/benchmark/utils/common.html#Measurement.merge"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/utils/benchmark/utils/common.py#L230"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.benchmark.Measurement.merge" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">merge</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">measurements</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/benchmark/utils/common.html#Measurement.merge"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/utils/benchmark/utils/common.py#L230"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.benchmark.Measurement.merge" title="Permalink to this definition">¶</a></dt>
 <dd><p>Convenience method for merging replicates.</p>
 <p>Merge will extrapolate times to <cite>number_per_run=1</cite> and will not
 transfer any metadata. (Since it might differ between replicates)</p>
@@ -889,7 +889,7 @@
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.utils.benchmark.CallgrindStats">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.utils.benchmark.</span></span><span class="sig-name descname"><span class="pre">CallgrindStats</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">task_spec</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">number_per_run</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">built_with_debug_symbols</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">baseline_inclusive_stats</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">baseline_exclusive_stats</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stmt_inclusive_stats</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stmt_exclusive_stats</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stmt_callgrind_out</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/benchmark/utils/valgrind_wrapper/timer_interface.html#CallgrindStats"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/utils/benchmark/utils/valgrind_wrapper/timer_interface.py#L163"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.benchmark.CallgrindStats" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.utils.benchmark.</span></span><span class="sig-name descname"><span class="pre">CallgrindStats</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">task_spec</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">number_per_run</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">built_with_debug_symbols</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">baseline_inclusive_stats</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">baseline_exclusive_stats</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stmt_inclusive_stats</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stmt_exclusive_stats</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stmt_callgrind_out</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/benchmark/utils/valgrind_wrapper/timer_interface.html#CallgrindStats"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/utils/benchmark/utils/valgrind_wrapper/timer_interface.py#L163"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.benchmark.CallgrindStats" title="Permalink to this definition">¶</a></dt>
 <dd><p>Top level container for Callgrind results collected by Timer.</p>
 <p>Manipulation is generally done using the FunctionCounts class, which is
 obtained by calling <cite>CallgrindStats.stats(…)</cite>. Several convenience
@@ -899,7 +899,7 @@
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.utils.benchmark.CallgrindStats.as_standardized">
-<span class="sig-name descname"><span class="pre">as_standardized</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/benchmark/utils/valgrind_wrapper/timer_interface.html#CallgrindStats.as_standardized"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/utils/benchmark/utils/valgrind_wrapper/timer_interface.py#L238"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.benchmark.CallgrindStats.as_standardized" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">as_standardized</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/benchmark/utils/valgrind_wrapper/timer_interface.html#CallgrindStats.as_standardized"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/utils/benchmark/utils/valgrind_wrapper/timer_interface.py#L238"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.benchmark.CallgrindStats.as_standardized" title="Permalink to this definition">¶</a></dt>
 <dd><p>Strip library names and some prefixes from function strings.</p>
 <p>When comparing two different sets of instruction counts, on stumbling
 block can be path prefixes. Callgrind includes the full filepath
@@ -928,7 +928,7 @@
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.utils.benchmark.CallgrindStats.counts">
-<span class="sig-name descname"><span class="pre">counts</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">denoise</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/benchmark/utils/valgrind_wrapper/timer_interface.html#CallgrindStats.counts"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/utils/benchmark/utils/valgrind_wrapper/timer_interface.py#L212"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.benchmark.CallgrindStats.counts" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">counts</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">denoise</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/benchmark/utils/valgrind_wrapper/timer_interface.html#CallgrindStats.counts"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/utils/benchmark/utils/valgrind_wrapper/timer_interface.py#L212"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.benchmark.CallgrindStats.counts" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns the total number of instructions executed.</p>
 <p>See <cite>FunctionCounts.denoise()</cite> for an explanation of the <cite>denoise</cite> arg.</p>
 <dl class="field-list simple">
@@ -940,7 +940,7 @@
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.utils.benchmark.CallgrindStats.delta">
-<span class="sig-name descname"><span class="pre">delta</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">other</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inclusive</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/benchmark/utils/valgrind_wrapper/timer_interface.html#CallgrindStats.delta"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/utils/benchmark/utils/valgrind_wrapper/timer_interface.py#L221"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.benchmark.CallgrindStats.delta" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">delta</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">other</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inclusive</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/benchmark/utils/valgrind_wrapper/timer_interface.html#CallgrindStats.delta"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/utils/benchmark/utils/valgrind_wrapper/timer_interface.py#L221"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.benchmark.CallgrindStats.delta" title="Permalink to this definition">¶</a></dt>
 <dd><p>Diff two sets of counts.</p>
 <p>One common reason to collect instruction counts is to determine the
 the effect that a particular change will have on the number of instructions
@@ -958,7 +958,7 @@
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.utils.benchmark.CallgrindStats.stats">
-<span class="sig-name descname"><span class="pre">stats</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">inclusive</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/benchmark/utils/valgrind_wrapper/timer_interface.html#CallgrindStats.stats"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/utils/benchmark/utils/valgrind_wrapper/timer_interface.py#L198"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.benchmark.CallgrindStats.stats" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">stats</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">inclusive</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/benchmark/utils/valgrind_wrapper/timer_interface.html#CallgrindStats.stats"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/utils/benchmark/utils/valgrind_wrapper/timer_interface.py#L198"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.benchmark.CallgrindStats.stats" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns detailed function counts.</p>
 <p>Conceptually, the FunctionCounts returned can be thought of as a tuple
 of (count, path_and_function_name) tuples.</p>
@@ -978,7 +978,7 @@
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.utils.benchmark.FunctionCounts">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.utils.benchmark.</span></span><span class="sig-name descname"><span class="pre">FunctionCounts</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">_data</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inclusive</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">truncate_rows</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_linewidth</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/benchmark/utils/valgrind_wrapper/timer_interface.html#FunctionCounts"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/utils/benchmark/utils/valgrind_wrapper/timer_interface.py#L39"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.benchmark.FunctionCounts" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.utils.benchmark.</span></span><span class="sig-name descname"><span class="pre">FunctionCounts</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">_data</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inclusive</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">truncate_rows</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_linewidth</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/benchmark/utils/valgrind_wrapper/timer_interface.html#FunctionCounts"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/utils/benchmark/utils/valgrind_wrapper/timer_interface.py#L39"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.benchmark.FunctionCounts" title="Permalink to this definition">¶</a></dt>
 <dd><p>Container for manipulating Callgrind results.</p>
 <dl class="simple">
 <dt>It supports:</dt><dd><ol class="arabic simple">
@@ -995,7 +995,7 @@
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.utils.benchmark.FunctionCounts.denoise">
-<span class="sig-name descname"><span class="pre">denoise</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/benchmark/utils/valgrind_wrapper/timer_interface.html#FunctionCounts.denoise"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/utils/benchmark/utils/valgrind_wrapper/timer_interface.py#L132"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.benchmark.FunctionCounts.denoise" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">denoise</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/benchmark/utils/valgrind_wrapper/timer_interface.html#FunctionCounts.denoise"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/utils/benchmark/utils/valgrind_wrapper/timer_interface.py#L132"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.benchmark.FunctionCounts.denoise" title="Permalink to this definition">¶</a></dt>
 <dd><p>Remove known noisy instructions.</p>
 <p>Several instructions in the CPython interpreter are rather noisy. These
 instructions involve unicode to dictionary lookups which Python uses to
@@ -1011,7 +1011,7 @@
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.utils.benchmark.FunctionCounts.filter">
-<span class="sig-name descname"><span class="pre">filter</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">filter_fn</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/benchmark/utils/valgrind_wrapper/timer_interface.html#FunctionCounts.filter"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/utils/benchmark/utils/valgrind_wrapper/timer_interface.py#L125"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.benchmark.FunctionCounts.filter" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">filter</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">filter_fn</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/benchmark/utils/valgrind_wrapper/timer_interface.html#FunctionCounts.filter"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/utils/benchmark/utils/valgrind_wrapper/timer_interface.py#L125"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.benchmark.FunctionCounts.filter" title="Permalink to this definition">¶</a></dt>
 <dd><p>Keep only the elements where <cite>filter_fn</cite> applied to function name returns True.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -1022,7 +1022,7 @@
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.utils.benchmark.FunctionCounts.transform">
-<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">map_fn</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/benchmark/utils/valgrind_wrapper/timer_interface.html#FunctionCounts.transform"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/utils/benchmark/utils/valgrind_wrapper/timer_interface.py#L112"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.benchmark.FunctionCounts.transform" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">map_fn</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/benchmark/utils/valgrind_wrapper/timer_interface.html#FunctionCounts.transform"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/utils/benchmark/utils/valgrind_wrapper/timer_interface.py#L112"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.benchmark.FunctionCounts.transform" title="Permalink to this definition">¶</a></dt>
 <dd><p>Apply <cite>map_fn</cite> to all of the function names.</p>
 <p>This can be used to regularize function names (e.g. stripping irrelevant
 parts of the file path), coalesce entries by mapping multiple functions
@@ -1038,7 +1038,7 @@
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.utils.benchmark.Compare">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.utils.benchmark.</span></span><span class="sig-name descname"><span class="pre">Compare</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">results</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/benchmark/utils/compare.html#Compare"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/utils/benchmark/utils/compare.py#L269"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.benchmark.Compare" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.utils.benchmark.</span></span><span class="sig-name descname"><span class="pre">Compare</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">results</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/benchmark/utils/compare.html#Compare"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/utils/benchmark/utils/compare.py#L269"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.benchmark.Compare" title="Permalink to this definition">¶</a></dt>
 <dd><p>Helper class for displaying the results of many measurements in a
 formatted table.</p>
 <p>The table format is based on the information fields provided in
@@ -1054,33 +1054,33 @@
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.utils.benchmark.Compare.colorize">
-<span class="sig-name descname"><span class="pre">colorize</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">rowwise</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/benchmark/utils/compare.html#Compare.colorize"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/utils/benchmark/utils/compare.py#L311"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.benchmark.Compare.colorize" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">colorize</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">rowwise</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/benchmark/utils/compare.html#Compare.colorize"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/utils/benchmark/utils/compare.py#L311"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.benchmark.Compare.colorize" title="Permalink to this definition">¶</a></dt>
 <dd><p>Colorize formatted table.</p>
 <p>Colorize columnwise by default.</p>
 </dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.utils.benchmark.Compare.extend_results">
-<span class="sig-name descname"><span class="pre">extend_results</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">results</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/benchmark/utils/compare.html#Compare.extend_results"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/utils/benchmark/utils/compare.py#L295"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.benchmark.Compare.extend_results" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">extend_results</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">results</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/benchmark/utils/compare.html#Compare.extend_results"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/utils/benchmark/utils/compare.py#L295"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.benchmark.Compare.extend_results" title="Permalink to this definition">¶</a></dt>
 <dd><p>Append results to already stored ones.</p>
 <p>All added results must be instances of <code class="docutils literal notranslate"><span class="pre">Measurement</span></code>.</p>
 </dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.utils.benchmark.Compare.highlight_warnings">
-<span class="sig-name descname"><span class="pre">highlight_warnings</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/benchmark/utils/compare.html#Compare.highlight_warnings"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/utils/benchmark/utils/compare.py#L318"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.benchmark.Compare.highlight_warnings" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">highlight_warnings</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/benchmark/utils/compare.html#Compare.highlight_warnings"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/utils/benchmark/utils/compare.py#L318"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.benchmark.Compare.highlight_warnings" title="Permalink to this definition">¶</a></dt>
 <dd><p>Enables warning highlighting when building formatted table.</p>
 </dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.utils.benchmark.Compare.print">
-<span class="sig-name descname"><span class="pre">print</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/benchmark/utils/compare.html#Compare.print"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/utils/benchmark/utils/compare.py#L322"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.benchmark.Compare.print" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">print</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/benchmark/utils/compare.html#Compare.print"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/utils/benchmark/utils/compare.py#L322"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.benchmark.Compare.print" title="Permalink to this definition">¶</a></dt>
 <dd><p>Print formatted table</p>
 </dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.utils.benchmark.Compare.trim_significant_figures">
-<span class="sig-name descname"><span class="pre">trim_significant_figures</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/benchmark/utils/compare.html#Compare.trim_significant_figures"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/utils/benchmark/utils/compare.py#L307"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.benchmark.Compare.trim_significant_figures" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">trim_significant_figures</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/benchmark/utils/compare.html#Compare.trim_significant_figures"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/utils/benchmark/utils/compare.py#L307"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.benchmark.Compare.trim_significant_figures" title="Permalink to this definition">¶</a></dt>
 <dd><p>Enables trimming of significant figures when building the formatted table.</p>
 </dd></dl>
 
diff --git a/main/bottleneck.html b/main/bottleneck.html
index 6317eb4492d..c52bc721b65 100644
--- a/main/bottleneck.html
+++ b/main/bottleneck.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/checkpoint.html b/main/checkpoint.html
index 2befd75e762..6dad7103c83 100644
--- a/main/checkpoint.html
+++ b/main/checkpoint.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -640,7 +640,7 @@ <h1>torch.utils.checkpoint<a class="headerlink" href="#torch-utils-checkpoint" t
 </div>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.utils.checkpoint.checkpoint">
-<span class="sig-prename descclassname"><span class="pre">torch.utils.checkpoint.</span></span><span class="sig-name descname"><span class="pre">checkpoint</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">function</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">*args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_reentrant=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">context_fn=&lt;function</span> <span class="pre">noop_context_fn&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">determinism_check='default'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">debug=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">**kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/checkpoint.html#checkpoint"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/utils/checkpoint.py#L342"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.checkpoint.checkpoint" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.utils.checkpoint.</span></span><span class="sig-name descname"><span class="pre">checkpoint</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">function</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">*args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_reentrant=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">context_fn=&lt;function</span> <span class="pre">noop_context_fn&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">determinism_check='default'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">debug=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">**kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/checkpoint.html#checkpoint"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/utils/checkpoint.py#L342"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.checkpoint.checkpoint" title="Permalink to this definition">¶</a></dt>
 <dd><p>Checkpoint a model or part of the model.</p>
 <p>Activation checkpointing is a technique that trades compute for memory.
 Instead of keeping tensors needed for backward alive until they are used in
@@ -752,7 +752,7 @@ <h1>torch.utils.checkpoint<a class="headerlink" href="#torch-utils-checkpoint" t
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.utils.checkpoint.checkpoint_sequential">
-<span class="sig-prename descclassname"><span class="pre">torch.utils.checkpoint.</span></span><span class="sig-name descname"><span class="pre">checkpoint_sequential</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">functions</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">segments</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_reentrant</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/checkpoint.html#checkpoint_sequential"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/utils/checkpoint.py#L503"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.checkpoint.checkpoint_sequential" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.utils.checkpoint.</span></span><span class="sig-name descname"><span class="pre">checkpoint_sequential</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">functions</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">segments</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_reentrant</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/checkpoint.html#checkpoint_sequential"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/utils/checkpoint.py#L503"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.checkpoint.checkpoint_sequential" title="Permalink to this definition">¶</a></dt>
 <dd><p>Checkpoint a sequential model to save memory.</p>
 <p>Sequential models execute a list of modules/functions in order
 (sequentially). Therefore, we can divide such a model in various segments
@@ -802,7 +802,7 @@ <h1>torch.utils.checkpoint<a class="headerlink" href="#torch-utils-checkpoint" t
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.utils.checkpoint.set_checkpoint_debug_enabled">
-<span class="sig-prename descclassname"><span class="pre">torch.utils.checkpoint.</span></span><span class="sig-name descname"><span class="pre">set_checkpoint_debug_enabled</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">enabled</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/checkpoint.html#set_checkpoint_debug_enabled"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/utils/checkpoint.py#L43"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.checkpoint.set_checkpoint_debug_enabled" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.utils.checkpoint.</span></span><span class="sig-name descname"><span class="pre">set_checkpoint_debug_enabled</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">enabled</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/checkpoint.html#set_checkpoint_debug_enabled"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/utils/checkpoint.py#L43"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.checkpoint.set_checkpoint_debug_enabled" title="Permalink to this definition">¶</a></dt>
 <dd><p>Context manager that sets whether checkpoint should print additional debug
 information when running. See the <code class="docutils literal notranslate"><span class="pre">debug</span></code> flag for
 <a class="reference internal" href="#torch.utils.checkpoint.checkpoint" title="torch.utils.checkpoint.checkpoint"><code class="xref py py-func docutils literal notranslate"><span class="pre">checkpoint()</span></code></a> for more information. Note that
@@ -818,7 +818,7 @@ <h1>torch.utils.checkpoint<a class="headerlink" href="#torch-utils-checkpoint" t
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.utils.checkpoint.CheckpointPolicy">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.utils.checkpoint.</span></span><span class="sig-name descname"><span class="pre">CheckpointPolicy</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/checkpoint.html#CheckpointPolicy"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/utils/checkpoint.py#L1225"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.checkpoint.CheckpointPolicy" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.utils.checkpoint.</span></span><span class="sig-name descname"><span class="pre">CheckpointPolicy</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/checkpoint.html#CheckpointPolicy"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/utils/checkpoint.py#L1225"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.checkpoint.CheckpointPolicy" title="Permalink to this definition">¶</a></dt>
 <dd><p>Enum for specifying the policy for checkpointing during backpropagation.</p>
 <p>The following policies are supported:</p>
 <ul class="simple">
@@ -842,7 +842,7 @@ <h1>torch.utils.checkpoint<a class="headerlink" href="#torch-utils-checkpoint" t
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.utils.checkpoint.SelectiveCheckpointContext">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.utils.checkpoint.</span></span><span class="sig-name descname"><span class="pre">SelectiveCheckpointContext</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">is_recompute</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/checkpoint.html#SelectiveCheckpointContext"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/utils/checkpoint.py#L1199"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.checkpoint.SelectiveCheckpointContext" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.utils.checkpoint.</span></span><span class="sig-name descname"><span class="pre">SelectiveCheckpointContext</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">is_recompute</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/checkpoint.html#SelectiveCheckpointContext"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/utils/checkpoint.py#L1199"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.checkpoint.SelectiveCheckpointContext" title="Permalink to this definition">¶</a></dt>
 <dd><p>Context passed to policy function during selective checkpointing.</p>
 <p>This class is used to pass relevant metadata to the policy function during
 selective checkpointing. The metadata includes whether the current invocation
@@ -865,7 +865,7 @@ <h1>torch.utils.checkpoint<a class="headerlink" href="#torch-utils-checkpoint" t
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.utils.checkpoint.create_selective_checkpoint_contexts">
-<span class="sig-prename descclassname"><span class="pre">torch.utils.checkpoint.</span></span><span class="sig-name descname"><span class="pre">create_selective_checkpoint_contexts</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">policy_fn_or_list</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">allow_cache_entry_mutation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/checkpoint.html#create_selective_checkpoint_contexts"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/utils/checkpoint.py#L1333"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.checkpoint.create_selective_checkpoint_contexts" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.utils.checkpoint.</span></span><span class="sig-name descname"><span class="pre">create_selective_checkpoint_contexts</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">policy_fn_or_list</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">allow_cache_entry_mutation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/checkpoint.html#create_selective_checkpoint_contexts"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/utils/checkpoint.py#L1333"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.checkpoint.create_selective_checkpoint_contexts" title="Permalink to this definition">¶</a></dt>
 <dd><p>Helper to avoid recomputing certain ops during activation checkpointing.</p>
 <p>Use this with <cite>torch.utils.checkpoint.checkpoint</cite> to control which
 operations are recomputed during the backward pass.</p>
diff --git a/main/community/build_ci_governance.html b/main/community/build_ci_governance.html
index 2aaff1f63f3..adbe5ec1bc1 100644
--- a/main/community/build_ci_governance.html
+++ b/main/community/build_ci_governance.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/community/contribution_guide.html b/main/community/contribution_guide.html
index 46e81fb47c6..7ccc8f8a826 100644
--- a/main/community/contribution_guide.html
+++ b/main/community/contribution_guide.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/community/design.html b/main/community/design.html
index 80f8e80a975..59a12f466d3 100644
--- a/main/community/design.html
+++ b/main/community/design.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/community/governance.html b/main/community/governance.html
index 0e842728e3a..ba06549a737 100644
--- a/main/community/governance.html
+++ b/main/community/governance.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/community/persons_of_interest.html b/main/community/persons_of_interest.html
index 9eaa9144a01..f84877cf2e0 100644
--- a/main/community/persons_of_interest.html
+++ b/main/community/persons_of_interest.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/complex_numbers.html b/main/complex_numbers.html
index c64d80ba49a..125ea9a10bf 100644
--- a/main/complex_numbers.html
+++ b/main/complex_numbers.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/cond.html b/main/cond.html
index ddf111cb863..edad396d3c7 100644
--- a/main/cond.html
+++ b/main/cond.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -776,7 +776,7 @@ <h2>Invariants of torch.ops.higher_order.cond<a class="headerlink" href="#invari
 <h3>API Reference<a class="headerlink" href="#api-reference" title="Permalink to this heading">¶</a></h3>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch._higher_order_ops.cond.cond">
-<span class="sig-prename descclassname"><span class="pre">torch._higher_order_ops.cond.</span></span><span class="sig-name descname"><span class="pre">cond</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">pred</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">true_fn</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">false_fn</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">operands</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">()</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/_higher_order_ops/cond.py#L69"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch._higher_order_ops.cond.cond" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch._higher_order_ops.cond.</span></span><span class="sig-name descname"><span class="pre">cond</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">pred</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">true_fn</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">false_fn</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">operands</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">()</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/_higher_order_ops/cond.py#L69"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch._higher_order_ops.cond.cond" title="Permalink to this definition">¶</a></dt>
 <dd><p>Conditionally applies <cite>true_fn</cite> or <cite>false_fn</cite>.</p>
 <div class="admonition warning">
 <p class="admonition-title">Warning</p>
diff --git a/main/config_mod.html b/main/config_mod.html
index dab7cdd7cb9..2347dbb1efd 100644
--- a/main/config_mod.html
+++ b/main/config_mod.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -610,7 +610,7 @@
 <span id="torch-config"></span><h1>torch.__config__<a class="headerlink" href="#module-torch.__config__" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.__config__.show">
-<span class="sig-prename descclassname"><span class="pre">torch.__config__.</span></span><span class="sig-name descname"><span class="pre">show</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/__config__.html#show"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/__config__.py#L4"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.__config__.show" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.__config__.</span></span><span class="sig-name descname"><span class="pre">show</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/__config__.html#show"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/__config__.py#L4"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.__config__.show" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return a human-readable string with descriptions of the
 configuration of PyTorch.</p>
 <dl class="field-list simple">
@@ -622,7 +622,7 @@
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.__config__.parallel_info">
-<span class="sig-prename descclassname"><span class="pre">torch.__config__.</span></span><span class="sig-name descname"><span class="pre">parallel_info</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/__config__.html#parallel_info"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/__config__.py#L20"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.__config__.parallel_info" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.__config__.</span></span><span class="sig-name descname"><span class="pre">parallel_info</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/__config__.html#parallel_info"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/__config__.py#L20"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.__config__.parallel_info" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns detailed string with parallelization settings</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
diff --git a/main/cpp_extension.html b/main/cpp_extension.html
index f444640c4f1..2c198aa43c8 100644
--- a/main/cpp_extension.html
+++ b/main/cpp_extension.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -610,7 +610,7 @@
 <h1>torch.utils.cpp_extension<a class="headerlink" href="#torch-utils-cpp-extension" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.utils.cpp_extension.CppExtension">
-<span class="sig-prename descclassname"><span class="pre">torch.utils.cpp_extension.</span></span><span class="sig-name descname"><span class="pre">CppExtension</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sources</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/cpp_extension.html#CppExtension"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/utils/cpp_extension.py#L977"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.cpp_extension.CppExtension" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.utils.cpp_extension.</span></span><span class="sig-name descname"><span class="pre">CppExtension</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sources</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/cpp_extension.html#CppExtension"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/utils/cpp_extension.py#L977"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.cpp_extension.CppExtension" title="Permalink to this definition">¶</a></dt>
 <dd><p>Create a <code class="xref py py-class docutils literal notranslate"><span class="pre">setuptools.Extension</span></code> for C++.</p>
 <p>Convenience method that creates a <code class="xref py py-class docutils literal notranslate"><span class="pre">setuptools.Extension</span></code> with the
 bare minimum (but often sufficient) arguments to build a C++ extension.</p>
@@ -655,7 +655,7 @@ <h1>torch.utils.cpp_extension<a class="headerlink" href="#torch-utils-cpp-extens
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.utils.cpp_extension.CUDAExtension">
-<span class="sig-prename descclassname"><span class="pre">torch.utils.cpp_extension.</span></span><span class="sig-name descname"><span class="pre">CUDAExtension</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sources</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/cpp_extension.html#CUDAExtension"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/utils/cpp_extension.py#L1047"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.cpp_extension.CUDAExtension" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.utils.cpp_extension.</span></span><span class="sig-name descname"><span class="pre">CUDAExtension</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sources</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/cpp_extension.html#CUDAExtension"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/utils/cpp_extension.py#L1047"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.cpp_extension.CUDAExtension" title="Permalink to this definition">¶</a></dt>
 <dd><p>Create a <code class="xref py py-class docutils literal notranslate"><span class="pre">setuptools.Extension</span></code> for CUDA/C++.</p>
 <p>Convenience method that creates a <code class="xref py py-class docutils literal notranslate"><span class="pre">setuptools.Extension</span></code> with the
 bare minimum (but often sufficient) arguments to build a CUDA/C++
@@ -760,7 +760,7 @@ <h1>torch.utils.cpp_extension<a class="headerlink" href="#torch-utils-cpp-extens
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.utils.cpp_extension.BuildExtension">
-<span class="sig-prename descclassname"><span class="pre">torch.utils.cpp_extension.</span></span><span class="sig-name descname"><span class="pre">BuildExtension</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/cpp_extension.html#BuildExtension"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/utils/cpp_extension.py#L493"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.cpp_extension.BuildExtension" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.utils.cpp_extension.</span></span><span class="sig-name descname"><span class="pre">BuildExtension</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/cpp_extension.html#BuildExtension"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/utils/cpp_extension.py#L493"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.cpp_extension.BuildExtension" title="Permalink to this definition">¶</a></dt>
 <dd><p>A custom <code class="xref py py-mod docutils literal notranslate"><span class="pre">setuptools</span></code> build extension .</p>
 <p>This <code class="xref py py-class docutils literal notranslate"><span class="pre">setuptools.build_ext</span></code> subclass takes care of passing the
 minimum required compiler flags (e.g. <code class="docutils literal notranslate"><span class="pre">-std=c++17</span></code>) as well as mixed
@@ -787,7 +787,7 @@ <h1>torch.utils.cpp_extension<a class="headerlink" href="#torch-utils-cpp-extens
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.utils.cpp_extension.load">
-<span class="sig-prename descclassname"><span class="pre">torch.utils.cpp_extension.</span></span><span class="sig-name descname"><span class="pre">load</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sources</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">extra_cflags</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">extra_cuda_cflags</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">extra_ldflags</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">extra_include_paths</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">build_directory</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">with_cuda</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">is_python_module</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">is_standalone</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">keep_intermediates</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/cpp_extension.html#load"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/utils/cpp_extension.py#L1322"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.cpp_extension.load" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.utils.cpp_extension.</span></span><span class="sig-name descname"><span class="pre">load</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sources</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">extra_cflags</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">extra_cuda_cflags</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">extra_ldflags</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">extra_include_paths</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">build_directory</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">with_cuda</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">is_python_module</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">is_standalone</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">keep_intermediates</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/cpp_extension.html#load"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/utils/cpp_extension.py#L1322"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.cpp_extension.load" title="Permalink to this definition">¶</a></dt>
 <dd><p>Load a PyTorch C++ extension just-in-time (JIT).</p>
 <p>To load an extension, a Ninja build file is emitted, which is used to
 compile the given sources into a dynamic library. This library is
@@ -873,7 +873,7 @@ <h1>torch.utils.cpp_extension<a class="headerlink" href="#torch-utils-cpp-extens
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.utils.cpp_extension.load_inline">
-<span class="sig-prename descclassname"><span class="pre">torch.utils.cpp_extension.</span></span><span class="sig-name descname"><span class="pre">load_inline</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cpp_sources</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cuda_sources</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">functions</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">extra_cflags</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">extra_cuda_cflags</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">extra_ldflags</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">extra_include_paths</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">build_directory</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">with_cuda</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">is_python_module</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">with_pytorch_error_handling</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">keep_intermediates</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_pch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/cpp_extension.html#load_inline"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/utils/cpp_extension.py#L1608"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.cpp_extension.load_inline" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.utils.cpp_extension.</span></span><span class="sig-name descname"><span class="pre">load_inline</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cpp_sources</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cuda_sources</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">functions</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">extra_cflags</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">extra_cuda_cflags</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">extra_ldflags</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">extra_include_paths</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">build_directory</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">with_cuda</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">is_python_module</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">with_pytorch_error_handling</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">keep_intermediates</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_pch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/cpp_extension.html#load_inline"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/utils/cpp_extension.py#L1608"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.cpp_extension.load_inline" title="Permalink to this definition">¶</a></dt>
 <dd><p>Load a PyTorch C++ extension just-in-time (JIT) from string sources.</p>
 <p>This function behaves exactly like <a class="reference internal" href="#torch.utils.cpp_extension.load" title="torch.utils.cpp_extension.load"><code class="xref py py-func docutils literal notranslate"><span class="pre">load()</span></code></a>, but takes its sources as
 strings rather than filenames. These strings are stored to files in the
@@ -952,7 +952,7 @@ <h1>torch.utils.cpp_extension<a class="headerlink" href="#torch-utils-cpp-extens
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.utils.cpp_extension.include_paths">
-<span class="sig-prename descclassname"><span class="pre">torch.utils.cpp_extension.</span></span><span class="sig-name descname"><span class="pre">include_paths</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device_type</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'cpu'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/cpp_extension.html#include_paths"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/utils/cpp_extension.py#L1238"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.cpp_extension.include_paths" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.utils.cpp_extension.</span></span><span class="sig-name descname"><span class="pre">include_paths</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device_type</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'cpu'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/cpp_extension.html#include_paths"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/utils/cpp_extension.py#L1238"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.cpp_extension.include_paths" title="Permalink to this definition">¶</a></dt>
 <dd><p>Get the include paths required to build a C++ or CUDA or SYCL extension.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -969,7 +969,7 @@ <h1>torch.utils.cpp_extension<a class="headerlink" href="#torch-utils-cpp-extens
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.utils.cpp_extension.get_compiler_abi_compatibility_and_version">
-<span class="sig-prename descclassname"><span class="pre">torch.utils.cpp_extension.</span></span><span class="sig-name descname"><span class="pre">get_compiler_abi_compatibility_and_version</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">compiler</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/cpp_extension.html#get_compiler_abi_compatibility_and_version"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/utils/cpp_extension.py#L384"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.cpp_extension.get_compiler_abi_compatibility_and_version" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.utils.cpp_extension.</span></span><span class="sig-name descname"><span class="pre">get_compiler_abi_compatibility_and_version</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">compiler</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/cpp_extension.html#get_compiler_abi_compatibility_and_version"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/utils/cpp_extension.py#L384"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.cpp_extension.get_compiler_abi_compatibility_and_version" title="Permalink to this definition">¶</a></dt>
 <dd><p>Determine if the given compiler is ABI-compatible with PyTorch alongside its version.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -988,13 +988,13 @@ <h1>torch.utils.cpp_extension<a class="headerlink" href="#torch-utils-cpp-extens
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.utils.cpp_extension.verify_ninja_availability">
-<span class="sig-prename descclassname"><span class="pre">torch.utils.cpp_extension.</span></span><span class="sig-name descname"><span class="pre">verify_ninja_availability</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/cpp_extension.html#verify_ninja_availability"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/utils/cpp_extension.py#L1972"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.cpp_extension.verify_ninja_availability" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.utils.cpp_extension.</span></span><span class="sig-name descname"><span class="pre">verify_ninja_availability</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/cpp_extension.html#verify_ninja_availability"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/utils/cpp_extension.py#L1972"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.cpp_extension.verify_ninja_availability" title="Permalink to this definition">¶</a></dt>
 <dd><p>Raise <code class="docutils literal notranslate"><span class="pre">RuntimeError</span></code> if <a class="reference external" href="https://ninja-build.org/">ninja</a> build system is not available on the system, does nothing otherwise.</p>
 </dd></dl>
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.utils.cpp_extension.is_ninja_available">
-<span class="sig-prename descclassname"><span class="pre">torch.utils.cpp_extension.</span></span><span class="sig-name descname"><span class="pre">is_ninja_available</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/cpp_extension.html#is_ninja_available"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/utils/cpp_extension.py#L1962"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.cpp_extension.is_ninja_available" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.utils.cpp_extension.</span></span><span class="sig-name descname"><span class="pre">is_ninja_available</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/cpp_extension.html#is_ninja_available"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/utils/cpp_extension.py#L1962"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.cpp_extension.is_ninja_available" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return <code class="docutils literal notranslate"><span class="pre">True</span></code> if the <a class="reference external" href="https://ninja-build.org/">ninja</a> build system is available on the system, <code class="docutils literal notranslate"><span class="pre">False</span></code> otherwise.</p>
 </dd></dl>
 
diff --git a/main/cpp_index.html b/main/cpp_index.html
index 77509bb115f..cadfd3a7098 100644
--- a/main/cpp_index.html
+++ b/main/cpp_index.html
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diff --git a/main/cpu.html b/main/cpu.html
index bac36fabcb4..ea8e1f9584d 100644
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+++ b/main/cpu.html
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     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
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diff --git a/main/cuda._sanitizer.html b/main/cuda._sanitizer.html
index fbaf0b61301..ec8165ad921 100644
--- a/main/cuda._sanitizer.html
+++ b/main/cuda._sanitizer.html
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     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
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@@ -712,7 +712,7 @@ <h2>Usage<a class="headerlink" href="#usage" title="Permalink to this heading">
 <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to this heading">¶</a></h2>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.cuda._sanitizer.enable_cuda_sanitizer">
-<span class="sig-prename descclassname"><span class="pre">torch.cuda._sanitizer.</span></span><span class="sig-name descname"><span class="pre">enable_cuda_sanitizer</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/cuda/_sanitizer.html#enable_cuda_sanitizer"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/_sanitizer.py#L651"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda._sanitizer.enable_cuda_sanitizer" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.cuda._sanitizer.</span></span><span class="sig-name descname"><span class="pre">enable_cuda_sanitizer</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/cuda/_sanitizer.html#enable_cuda_sanitizer"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/cuda/_sanitizer.py#L651"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda._sanitizer.enable_cuda_sanitizer" title="Permalink to this definition">¶</a></dt>
 <dd><p>Enable CUDA Sanitizer.</p>
 <p>The sanitizer will begin to analyze low-level CUDA calls invoked by torch functions
 for synchronization errors. All data races found will be printed to the standard
diff --git a/main/cuda.html b/main/cuda.html
index 8a7bcf1be40..3fc3cc89645 100644
--- a/main/cuda.html
+++ b/main/cuda.html
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-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
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@@ -901,7 +901,7 @@ <h2>Graphs (beta)<a class="headerlink" href="#graphs-beta" title="Permalink to t
 </table>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.cuda.use_mem_pool">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">use_mem_pool</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">pool</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/cuda/memory.html#use_mem_pool"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/memory.py#L1070"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.use_mem_pool" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">use_mem_pool</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">pool</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/cuda/memory.html#use_mem_pool"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/cuda/memory.py#L1070"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.use_mem_pool" title="Permalink to this definition">¶</a></dt>
 <dd><p>A context manager that routes allocations to a given pool.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/cuda.tunable.html b/main/cuda.tunable.html
index a27ccbcdca0..4b33c4a9a5d 100644
--- a/main/cuda.tunable.html
+++ b/main/cuda.tunable.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -722,7 +722,7 @@ <h3>Tuning Context<a class="headerlink" href="#tuning-context" title="Permalink
 <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to this heading">¶</a></h2>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.cuda.tunable.enable">
-<span class="sig-prename descclassname"><span class="pre">torch.cuda.tunable.</span></span><span class="sig-name descname"><span class="pre">enable</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">val</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/cuda/tunable.html#enable"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/tunable.py#L151"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.tunable.enable" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.cuda.tunable.</span></span><span class="sig-name descname"><span class="pre">enable</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">val</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/cuda/tunable.html#enable"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/cuda/tunable.py#L151"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.tunable.enable" title="Permalink to this definition">¶</a></dt>
 <dd><p>This is the big on/off switch for all TunableOp implementations.</p>
 <dl class="field-list simple">
 </dl>
@@ -730,7 +730,7 @@ <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.cuda.tunable.is_enabled">
-<span class="sig-prename descclassname"><span class="pre">torch.cuda.tunable.</span></span><span class="sig-name descname"><span class="pre">is_enabled</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/cuda/tunable.html#is_enabled"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/tunable.py#L156"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.tunable.is_enabled" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.cuda.tunable.</span></span><span class="sig-name descname"><span class="pre">is_enabled</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/cuda/tunable.html#is_enabled"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/cuda/tunable.py#L156"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.tunable.is_enabled" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns whether the TunableOp feature is enabled.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -741,7 +741,7 @@ <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.cuda.tunable.tuning_enable">
-<span class="sig-prename descclassname"><span class="pre">torch.cuda.tunable.</span></span><span class="sig-name descname"><span class="pre">tuning_enable</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">val</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/cuda/tunable.html#tuning_enable"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/tunable.py#L161"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.tunable.tuning_enable" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.cuda.tunable.</span></span><span class="sig-name descname"><span class="pre">tuning_enable</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">val</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/cuda/tunable.html#tuning_enable"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/cuda/tunable.py#L161"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.tunable.tuning_enable" title="Permalink to this definition">¶</a></dt>
 <dd><p>Enable tuning of TunableOp implementations.</p>
 <p>When enabled, if a tuned entry isn’t found, run the tuning step and record
 the entry.</p>
@@ -751,7 +751,7 @@ <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.cuda.tunable.tuning_is_enabled">
-<span class="sig-prename descclassname"><span class="pre">torch.cuda.tunable.</span></span><span class="sig-name descname"><span class="pre">tuning_is_enabled</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/cuda/tunable.html#tuning_is_enabled"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/tunable.py#L170"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.tunable.tuning_is_enabled" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.cuda.tunable.</span></span><span class="sig-name descname"><span class="pre">tuning_is_enabled</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/cuda/tunable.html#tuning_is_enabled"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/cuda/tunable.py#L170"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.tunable.tuning_is_enabled" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns whether TunableOp implementations can be tuned.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -762,7 +762,7 @@ <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.cuda.tunable.record_untuned_enable">
-<span class="sig-prename descclassname"><span class="pre">torch.cuda.tunable.</span></span><span class="sig-name descname"><span class="pre">record_untuned_enable</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">val</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/cuda/tunable.html#record_untuned_enable"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/tunable.py#L175"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.tunable.record_untuned_enable" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.cuda.tunable.</span></span><span class="sig-name descname"><span class="pre">record_untuned_enable</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">val</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/cuda/tunable.html#record_untuned_enable"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/cuda/tunable.py#L175"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.tunable.record_untuned_enable" title="Permalink to this definition">¶</a></dt>
 <dd><p>Enable recording untuned of TunableOp perations for offline tuning.</p>
 <p>When enabled, if a tuned entry isn’t found, write it to the untuned file.</p>
 <dl class="field-list simple">
@@ -771,7 +771,7 @@ <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.cuda.tunable.record_untuned_is_enabled">
-<span class="sig-prename descclassname"><span class="pre">torch.cuda.tunable.</span></span><span class="sig-name descname"><span class="pre">record_untuned_is_enabled</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/cuda/tunable.html#record_untuned_is_enabled"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/tunable.py#L183"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.tunable.record_untuned_is_enabled" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.cuda.tunable.</span></span><span class="sig-name descname"><span class="pre">record_untuned_is_enabled</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/cuda/tunable.html#record_untuned_is_enabled"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/cuda/tunable.py#L183"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.tunable.record_untuned_is_enabled" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns whether TunableOp operations are recorded for offline tuning.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -782,7 +782,7 @@ <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.cuda.tunable.set_max_tuning_duration">
-<span class="sig-prename descclassname"><span class="pre">torch.cuda.tunable.</span></span><span class="sig-name descname"><span class="pre">set_max_tuning_duration</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">duration</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/cuda/tunable.html#set_max_tuning_duration"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/tunable.py#L188"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.tunable.set_max_tuning_duration" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.cuda.tunable.</span></span><span class="sig-name descname"><span class="pre">set_max_tuning_duration</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">duration</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/cuda/tunable.html#set_max_tuning_duration"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/cuda/tunable.py#L188"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.tunable.set_max_tuning_duration" title="Permalink to this definition">¶</a></dt>
 <dd><p>Set max time in milliseconds to spend tuning a given solution.</p>
 <p>If both max tuning duration and iterations are set, the smaller of the two
 will be honored. At minimum 1 tuning iteration will always be run.</p>
@@ -792,7 +792,7 @@ <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.cuda.tunable.get_max_tuning_duration">
-<span class="sig-prename descclassname"><span class="pre">torch.cuda.tunable.</span></span><span class="sig-name descname"><span class="pre">get_max_tuning_duration</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/cuda/tunable.html#get_max_tuning_duration"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/tunable.py#L197"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.tunable.get_max_tuning_duration" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.cuda.tunable.</span></span><span class="sig-name descname"><span class="pre">get_max_tuning_duration</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/cuda/tunable.html#get_max_tuning_duration"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/cuda/tunable.py#L197"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.tunable.get_max_tuning_duration" title="Permalink to this definition">¶</a></dt>
 <dd><p>Get max time to spend tuning a given solution.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -803,7 +803,7 @@ <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.cuda.tunable.set_max_tuning_iterations">
-<span class="sig-prename descclassname"><span class="pre">torch.cuda.tunable.</span></span><span class="sig-name descname"><span class="pre">set_max_tuning_iterations</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">iterations</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/cuda/tunable.html#set_max_tuning_iterations"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/tunable.py#L202"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.tunable.set_max_tuning_iterations" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.cuda.tunable.</span></span><span class="sig-name descname"><span class="pre">set_max_tuning_iterations</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">iterations</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/cuda/tunable.html#set_max_tuning_iterations"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/cuda/tunable.py#L202"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.tunable.set_max_tuning_iterations" title="Permalink to this definition">¶</a></dt>
 <dd><p>Set max number of iterations to spend tuning a given solution.</p>
 <p>If both max tuning duration and iterations are set, the smaller of the two
 will be honored. At minimum 1 tuning iteration will always be run.</p>
@@ -813,7 +813,7 @@ <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.cuda.tunable.get_max_tuning_iterations">
-<span class="sig-prename descclassname"><span class="pre">torch.cuda.tunable.</span></span><span class="sig-name descname"><span class="pre">get_max_tuning_iterations</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/cuda/tunable.html#get_max_tuning_iterations"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/tunable.py#L211"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.tunable.get_max_tuning_iterations" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.cuda.tunable.</span></span><span class="sig-name descname"><span class="pre">get_max_tuning_iterations</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/cuda/tunable.html#get_max_tuning_iterations"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/cuda/tunable.py#L211"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.tunable.get_max_tuning_iterations" title="Permalink to this definition">¶</a></dt>
 <dd><p>Get max iterations to spend tuning a given solution.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -824,7 +824,7 @@ <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.cuda.tunable.set_filename">
-<span class="sig-prename descclassname"><span class="pre">torch.cuda.tunable.</span></span><span class="sig-name descname"><span class="pre">set_filename</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">filename</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">insert_device_ordinal</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/cuda/tunable.html#set_filename"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/tunable.py#L216"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.tunable.set_filename" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.cuda.tunable.</span></span><span class="sig-name descname"><span class="pre">set_filename</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">filename</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">insert_device_ordinal</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/cuda/tunable.html#set_filename"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/cuda/tunable.py#L216"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.tunable.set_filename" title="Permalink to this definition">¶</a></dt>
 <dd><p>Set the filename to use for input/output of tuning results.</p>
 <p>If <code class="xref py py-attr docutils literal notranslate"><span class="pre">insert_device_ordinal</span></code> is <code class="docutils literal notranslate"><span class="pre">True</span></code> then the current device ordinal
 will be added to the given filename automatically. This can be used in a
@@ -835,7 +835,7 @@ <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.cuda.tunable.get_filename">
-<span class="sig-prename descclassname"><span class="pre">torch.cuda.tunable.</span></span><span class="sig-name descname"><span class="pre">get_filename</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/cuda/tunable.html#get_filename"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/tunable.py#L226"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.tunable.get_filename" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.cuda.tunable.</span></span><span class="sig-name descname"><span class="pre">get_filename</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/cuda/tunable.html#get_filename"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/cuda/tunable.py#L226"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.tunable.get_filename" title="Permalink to this definition">¶</a></dt>
 <dd><p>Get the results filename.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -846,7 +846,7 @@ <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.cuda.tunable.get_results">
-<span class="sig-prename descclassname"><span class="pre">torch.cuda.tunable.</span></span><span class="sig-name descname"><span class="pre">get_results</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/cuda/tunable.html#get_results"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/tunable.py#L231"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.tunable.get_results" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.cuda.tunable.</span></span><span class="sig-name descname"><span class="pre">get_results</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/cuda/tunable.html#get_results"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/cuda/tunable.py#L231"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.tunable.get_results" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return all TunableOp results.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -857,7 +857,7 @@ <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.cuda.tunable.get_validators">
-<span class="sig-prename descclassname"><span class="pre">torch.cuda.tunable.</span></span><span class="sig-name descname"><span class="pre">get_validators</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/cuda/tunable.html#get_validators"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/tunable.py#L236"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.tunable.get_validators" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.cuda.tunable.</span></span><span class="sig-name descname"><span class="pre">get_validators</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/cuda/tunable.html#get_validators"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/cuda/tunable.py#L236"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.tunable.get_validators" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the TunableOp validators.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -868,7 +868,7 @@ <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.cuda.tunable.write_file_on_exit">
-<span class="sig-prename descclassname"><span class="pre">torch.cuda.tunable.</span></span><span class="sig-name descname"><span class="pre">write_file_on_exit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">val</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/cuda/tunable.html#write_file_on_exit"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/tunable.py#L241"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.tunable.write_file_on_exit" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.cuda.tunable.</span></span><span class="sig-name descname"><span class="pre">write_file_on_exit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">val</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/cuda/tunable.html#write_file_on_exit"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/cuda/tunable.py#L241"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.tunable.write_file_on_exit" title="Permalink to this definition">¶</a></dt>
 <dd><p>During Tuning Context destruction, write file to disk.</p>
 <p>This is useful as a final flush of your results to disk if your application
 terminates as result of normal operation or an error. Manual flushing of
@@ -879,7 +879,7 @@ <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.cuda.tunable.write_file">
-<span class="sig-prename descclassname"><span class="pre">torch.cuda.tunable.</span></span><span class="sig-name descname"><span class="pre">write_file</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">filename</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/cuda/tunable.html#write_file"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/tunable.py#L250"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.tunable.write_file" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.cuda.tunable.</span></span><span class="sig-name descname"><span class="pre">write_file</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">filename</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/cuda/tunable.html#write_file"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/cuda/tunable.py#L250"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.tunable.write_file" title="Permalink to this definition">¶</a></dt>
 <dd><p>Write results to a CSV file.</p>
 <p>If <code class="xref py py-attr docutils literal notranslate"><span class="pre">filename</span></code> is not given, <code class="docutils literal notranslate"><span class="pre">get_filename()</span></code> is called.</p>
 <dl class="field-list simple">
@@ -891,7 +891,7 @@ <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.cuda.tunable.read_file">
-<span class="sig-prename descclassname"><span class="pre">torch.cuda.tunable.</span></span><span class="sig-name descname"><span class="pre">read_file</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">filename</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/cuda/tunable.html#read_file"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/tunable.py#L260"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.tunable.read_file" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.cuda.tunable.</span></span><span class="sig-name descname"><span class="pre">read_file</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">filename</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/cuda/tunable.html#read_file"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/cuda/tunable.py#L260"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.tunable.read_file" title="Permalink to this definition">¶</a></dt>
 <dd><p>Read results from a TunableOp CSV file.</p>
 <p>If <code class="xref py py-attr docutils literal notranslate"><span class="pre">filename</span></code> is not given, <code class="docutils literal notranslate"><span class="pre">get_filename()</span></code> is called.</p>
 <dl class="field-list simple">
@@ -903,7 +903,7 @@ <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.cuda.tunable.tune_gemm_in_file">
-<span class="sig-prename descclassname"><span class="pre">torch.cuda.tunable.</span></span><span class="sig-name descname"><span class="pre">tune_gemm_in_file</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">filename</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/cuda/tunable.html#tune_gemm_in_file"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/tunable.py#L283"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.tunable.tune_gemm_in_file" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.cuda.tunable.</span></span><span class="sig-name descname"><span class="pre">tune_gemm_in_file</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">filename</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/cuda/tunable.html#tune_gemm_in_file"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/cuda/tunable.py#L283"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.tunable.tune_gemm_in_file" title="Permalink to this definition">¶</a></dt>
 <dd><p>tune GEMM in file.</p>
 <dl class="field-list simple">
 </dl>
@@ -911,7 +911,7 @@ <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.cuda.tunable.mgpu_tune_gemm_in_file">
-<span class="sig-prename descclassname"><span class="pre">torch.cuda.tunable.</span></span><span class="sig-name descname"><span class="pre">mgpu_tune_gemm_in_file</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">filename_pattern</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_gpus</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/cuda/tunable.html#mgpu_tune_gemm_in_file"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/tunable.py#L499"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.tunable.mgpu_tune_gemm_in_file" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.cuda.tunable.</span></span><span class="sig-name descname"><span class="pre">mgpu_tune_gemm_in_file</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">filename_pattern</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_gpus</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/cuda/tunable.html#mgpu_tune_gemm_in_file"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/cuda/tunable.py#L499"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.tunable.mgpu_tune_gemm_in_file" title="Permalink to this definition">¶</a></dt>
 <dd><p>Process one or more files and distribute work over one or more GPUs.</p>
 <dl class="field-list simple">
 </dl>
@@ -919,7 +919,7 @@ <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.cuda.tunable.set_rotating_buffer_size">
-<span class="sig-prename descclassname"><span class="pre">torch.cuda.tunable.</span></span><span class="sig-name descname"><span class="pre">set_rotating_buffer_size</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">buffer_size</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/cuda/tunable.html#set_rotating_buffer_size"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/tunable.py#L270"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.tunable.set_rotating_buffer_size" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.cuda.tunable.</span></span><span class="sig-name descname"><span class="pre">set_rotating_buffer_size</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">buffer_size</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/cuda/tunable.html#set_rotating_buffer_size"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/cuda/tunable.py#L270"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.tunable.set_rotating_buffer_size" title="Permalink to this definition">¶</a></dt>
 <dd><p>Set rotating buffer size to this value in MB, if the buffer size is greater than zero.</p>
 <p>If less than zero, query L2 cache size. If equal to zero, means deactivate rotating buffer.</p>
 <dl class="field-list simple">
@@ -928,7 +928,7 @@ <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.cuda.tunable.get_rotating_buffer_size">
-<span class="sig-prename descclassname"><span class="pre">torch.cuda.tunable.</span></span><span class="sig-name descname"><span class="pre">get_rotating_buffer_size</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/cuda/tunable.html#get_rotating_buffer_size"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/tunable.py#L278"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.tunable.get_rotating_buffer_size" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.cuda.tunable.</span></span><span class="sig-name descname"><span class="pre">get_rotating_buffer_size</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/cuda/tunable.html#get_rotating_buffer_size"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/cuda/tunable.py#L278"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.tunable.get_rotating_buffer_size" title="Permalink to this definition">¶</a></dt>
 <dd><p>Get the rotating buffer size in kilobytes.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
diff --git a/main/cuda_environment_variables.html b/main/cuda_environment_variables.html
index 33a5407859a..bc9c75454bf 100644
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diff --git a/main/cudnn_rnn_determinism.html b/main/cudnn_rnn_determinism.html
index 60f4a03997f..7179d01158b 100644
--- a/main/cudnn_rnn_determinism.html
+++ b/main/cudnn_rnn_determinism.html
@@ -298,7 +298,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
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diff --git a/main/data.html b/main/data.html
index ee82459311d..5e24b5478fb 100644
--- a/main/data.html
+++ b/main/data.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
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@@ -976,7 +976,7 @@ <h2>Memory Pinning<a class="headerlink" href="#memory-pinning" title="Permalink
 </div>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.utils.data.DataLoader">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.utils.data.</span></span><span class="sig-name descname"><span class="pre">DataLoader</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dataset</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">batch_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shuffle</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sampler</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">batch_sampler</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_workers</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">collate_fn</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pin_memory</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">drop_last</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">timeout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">worker_init_fn</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">multiprocessing_context</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">generator</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prefetch_factor</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">persistent_workers</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pin_memory_device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">in_order</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/data/dataloader.html#DataLoader"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/utils/data/dataloader.py#L131"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.data.DataLoader" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.utils.data.</span></span><span class="sig-name descname"><span class="pre">DataLoader</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dataset</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">batch_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shuffle</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sampler</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">batch_sampler</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_workers</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">collate_fn</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pin_memory</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">drop_last</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">timeout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">worker_init_fn</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">multiprocessing_context</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">generator</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prefetch_factor</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">persistent_workers</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pin_memory_device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">in_order</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/data/dataloader.html#DataLoader"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/utils/data/dataloader.py#L131"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.data.DataLoader" title="Permalink to this definition">¶</a></dt>
 <dd><p>Data loader combines a dataset and a sampler, and provides an iterable over the given dataset.</p>
 <p>The <a class="reference internal" href="#torch.utils.data.DataLoader" title="torch.utils.data.DataLoader"><code class="xref py py-class docutils literal notranslate"><span class="pre">DataLoader</span></code></a> supports both map-style and
 iterable-style datasets with single- or multi-process loading, customizing
@@ -1077,7 +1077,7 @@ <h2>Memory Pinning<a class="headerlink" href="#memory-pinning" title="Permalink
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.utils.data.Dataset">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.utils.data.</span></span><span class="sig-name descname"><span class="pre">Dataset</span></span><a class="reference internal" href="_modules/torch/utils/data/dataset.html#Dataset"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/utils/data/dataset.py#L33"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.data.Dataset" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.utils.data.</span></span><span class="sig-name descname"><span class="pre">Dataset</span></span><a class="reference internal" href="_modules/torch/utils/data/dataset.html#Dataset"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/utils/data/dataset.py#L33"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.data.Dataset" title="Permalink to this definition">¶</a></dt>
 <dd><p>An abstract class representing a <a class="reference internal" href="#torch.utils.data.Dataset" title="torch.utils.data.Dataset"><code class="xref py py-class docutils literal notranslate"><span class="pre">Dataset</span></code></a>.</p>
 <p>All datasets that represent a map from keys to data samples should subclass
 it. All subclasses should overwrite <code class="xref py py-meth docutils literal notranslate"><span class="pre">__getitem__()</span></code>, supporting fetching a
@@ -1098,7 +1098,7 @@ <h2>Memory Pinning<a class="headerlink" href="#memory-pinning" title="Permalink
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.utils.data.IterableDataset">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.utils.data.</span></span><span class="sig-name descname"><span class="pre">IterableDataset</span></span><a class="reference internal" href="_modules/torch/utils/data/dataset.html#IterableDataset"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/utils/data/dataset.py#L67"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.data.IterableDataset" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.utils.data.</span></span><span class="sig-name descname"><span class="pre">IterableDataset</span></span><a class="reference internal" href="_modules/torch/utils/data/dataset.html#IterableDataset"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/utils/data/dataset.py#L67"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.data.IterableDataset" title="Permalink to this definition">¶</a></dt>
 <dd><p>An iterable Dataset.</p>
 <p>All datasets that represent an iterable of data samples should subclass it.
 Such form of datasets is particularly useful when data come from a stream.</p>
@@ -1200,7 +1200,7 @@ <h2>Memory Pinning<a class="headerlink" href="#memory-pinning" title="Permalink
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.utils.data.TensorDataset">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.utils.data.</span></span><span class="sig-name descname"><span class="pre">TensorDataset</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">tensors</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/data/dataset.html#TensorDataset"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/utils/data/dataset.py#L183"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.data.TensorDataset" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.utils.data.</span></span><span class="sig-name descname"><span class="pre">TensorDataset</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">tensors</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/data/dataset.html#TensorDataset"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/utils/data/dataset.py#L183"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.data.TensorDataset" title="Permalink to this definition">¶</a></dt>
 <dd><p>Dataset wrapping tensors.</p>
 <p>Each sample will be retrieved by indexing tensors along the first dimension.</p>
 <dl class="field-list simple">
@@ -1212,7 +1212,7 @@ <h2>Memory Pinning<a class="headerlink" href="#memory-pinning" title="Permalink
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.utils.data.StackDataset">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.utils.data.</span></span><span class="sig-name descname"><span class="pre">StackDataset</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/data/dataset.html#StackDataset"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/utils/data/dataset.py#L207"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.data.StackDataset" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.utils.data.</span></span><span class="sig-name descname"><span class="pre">StackDataset</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/data/dataset.html#StackDataset"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/utils/data/dataset.py#L207"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.data.StackDataset" title="Permalink to this definition">¶</a></dt>
 <dd><p>Dataset as a stacking of multiple datasets.</p>
 <p>This class is useful to assemble different parts of complex input data, given as datasets.</p>
 <p class="rubric">Example</p>
@@ -1236,7 +1236,7 @@ <h2>Memory Pinning<a class="headerlink" href="#memory-pinning" title="Permalink
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.utils.data.ConcatDataset">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.utils.data.</span></span><span class="sig-name descname"><span class="pre">ConcatDataset</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">datasets</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/data/dataset.html#ConcatDataset"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/utils/data/dataset.py#L294"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.data.ConcatDataset" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.utils.data.</span></span><span class="sig-name descname"><span class="pre">ConcatDataset</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">datasets</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/data/dataset.html#ConcatDataset"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/utils/data/dataset.py#L294"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.data.ConcatDataset" title="Permalink to this definition">¶</a></dt>
 <dd><p>Dataset as a concatenation of multiple datasets.</p>
 <p>This class is useful to assemble different existing datasets.</p>
 <dl class="field-list simple">
@@ -1248,7 +1248,7 @@ <h2>Memory Pinning<a class="headerlink" href="#memory-pinning" title="Permalink
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.utils.data.ChainDataset">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.utils.data.</span></span><span class="sig-name descname"><span class="pre">ChainDataset</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">datasets</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/data/dataset.html#ChainDataset"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/utils/data/dataset.py#L351"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.data.ChainDataset" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.utils.data.</span></span><span class="sig-name descname"><span class="pre">ChainDataset</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">datasets</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/data/dataset.html#ChainDataset"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/utils/data/dataset.py#L351"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.data.ChainDataset" title="Permalink to this definition">¶</a></dt>
 <dd><p>Dataset for chaining multiple <a class="reference internal" href="#torch.utils.data.IterableDataset" title="torch.utils.data.IterableDataset"><code class="xref py py-class docutils literal notranslate"><span class="pre">IterableDataset</span></code></a> s.</p>
 <p>This class is useful to assemble different existing dataset streams. The
 chaining operation is done on-the-fly, so concatenating large-scale
@@ -1262,7 +1262,7 @@ <h2>Memory Pinning<a class="headerlink" href="#memory-pinning" title="Permalink
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.utils.data.Subset">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.utils.data.</span></span><span class="sig-name descname"><span class="pre">Subset</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dataset</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">indices</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/data/dataset.html#Subset"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/utils/data/dataset.py#L383"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.data.Subset" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.utils.data.</span></span><span class="sig-name descname"><span class="pre">Subset</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dataset</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">indices</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/data/dataset.html#Subset"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/utils/data/dataset.py#L383"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.data.Subset" title="Permalink to this definition">¶</a></dt>
 <dd><p>Subset of a dataset at specified indices.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -1276,7 +1276,7 @@ <h2>Memory Pinning<a class="headerlink" href="#memory-pinning" title="Permalink
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.utils.data._utils.collate.collate">
-<span class="sig-prename descclassname"><span class="pre">torch.utils.data._utils.collate.</span></span><span class="sig-name descname"><span class="pre">collate</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">batch</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">collate_fn_map</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/data/_utils/collate.html#collate"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/utils/data/_utils/collate.py#L118"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.data._utils.collate.collate" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.utils.data._utils.collate.</span></span><span class="sig-name descname"><span class="pre">collate</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">batch</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">collate_fn_map</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/data/_utils/collate.html#collate"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/utils/data/_utils/collate.py#L118"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.data._utils.collate.collate" title="Permalink to this definition">¶</a></dt>
 <dd><p>General collate function that handles collection type of element within each batch.</p>
 <p>The function also opens function registry to deal with specific element types. <cite>default_collate_fn_map</cite>
 provides default collate functions for tensors, numpy arrays, numbers and strings.</p>
@@ -1311,7 +1311,7 @@ <h2>Memory Pinning<a class="headerlink" href="#memory-pinning" title="Permalink
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.utils.data.default_collate">
-<span class="sig-prename descclassname"><span class="pre">torch.utils.data.</span></span><span class="sig-name descname"><span class="pre">default_collate</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">batch</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/data/_utils/collate.html#default_collate"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/utils/data/_utils/collate.py#L337"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.data.default_collate" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.utils.data.</span></span><span class="sig-name descname"><span class="pre">default_collate</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">batch</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/data/_utils/collate.html#default_collate"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/utils/data/_utils/collate.py#L337"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.data.default_collate" title="Permalink to this definition">¶</a></dt>
 <dd><p>Take in a batch of data and put the elements within the batch into a tensor with an additional outer dimension - batch size.</p>
 <p>The exact output type can be a <a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.Tensor</span></code></a>, a <cite>Sequence</cite> of <a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.Tensor</span></code></a>, a
 Collection of <a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.Tensor</span></code></a>, or left unchanged, depending on the input type.
@@ -1377,7 +1377,7 @@ <h2>Memory Pinning<a class="headerlink" href="#memory-pinning" title="Permalink
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.utils.data.default_convert">
-<span class="sig-prename descclassname"><span class="pre">torch.utils.data.</span></span><span class="sig-name descname"><span class="pre">default_convert</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">data</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/data/_utils/collate.html#default_convert"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/utils/data/_utils/collate.py#L23"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.data.default_convert" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.utils.data.</span></span><span class="sig-name descname"><span class="pre">default_convert</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">data</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/data/_utils/collate.html#default_convert"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/utils/data/_utils/collate.py#L23"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.data.default_convert" title="Permalink to this definition">¶</a></dt>
 <dd><p>Convert each NumPy array element into a <a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.Tensor</span></code></a>.</p>
 <p>If the input is a <cite>Sequence</cite>, <cite>Collection</cite>, or <cite>Mapping</cite>, it tries to convert each element inside to a <a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.Tensor</span></code></a>.
 If the input is not an NumPy array, it is left unchanged.
@@ -1412,7 +1412,7 @@ <h2>Memory Pinning<a class="headerlink" href="#memory-pinning" title="Permalink
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.utils.data.get_worker_info">
-<span class="sig-prename descclassname"><span class="pre">torch.utils.data.</span></span><span class="sig-name descname"><span class="pre">get_worker_info</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/data/_utils/worker.html#get_worker_info"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/utils/data/_utils/worker.py#L101"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.data.get_worker_info" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.utils.data.</span></span><span class="sig-name descname"><span class="pre">get_worker_info</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/data/_utils/worker.html#get_worker_info"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/utils/data/_utils/worker.py#L101"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.data.get_worker_info" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns the information about the current
 <a class="reference internal" href="#torch.utils.data.DataLoader" title="torch.utils.data.DataLoader"><code class="xref py py-class docutils literal notranslate"><span class="pre">DataLoader</span></code></a> iterator worker process.</p>
 <p>When called in a worker, this returns an object guaranteed to have the
@@ -1446,7 +1446,7 @@ <h2>Memory Pinning<a class="headerlink" href="#memory-pinning" title="Permalink
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.utils.data.random_split">
-<span class="sig-prename descclassname"><span class="pre">torch.utils.data.</span></span><span class="sig-name descname"><span class="pre">random_split</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dataset</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lengths</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">generator=&lt;torch._C.Generator</span> <span class="pre">object&gt;</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/data/dataset.html#random_split"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/utils/data/dataset.py#L416"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.data.random_split" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.utils.data.</span></span><span class="sig-name descname"><span class="pre">random_split</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dataset</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lengths</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">generator=&lt;torch._C.Generator</span> <span class="pre">object&gt;</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/data/dataset.html#random_split"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/utils/data/dataset.py#L416"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.data.random_split" title="Permalink to this definition">¶</a></dt>
 <dd><p>Randomly split a dataset into non-overlapping new datasets of given lengths.</p>
 <p>If a list of fractions that sum up to 1 is given,
 the lengths will be computed automatically as
@@ -1478,7 +1478,7 @@ <h2>Memory Pinning<a class="headerlink" href="#memory-pinning" title="Permalink
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.utils.data.Sampler">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.utils.data.</span></span><span class="sig-name descname"><span class="pre">Sampler</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">data_source</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/data/sampler.html#Sampler"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/utils/data/sampler.py#L22"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.data.Sampler" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.utils.data.</span></span><span class="sig-name descname"><span class="pre">Sampler</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">data_source</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/data/sampler.html#Sampler"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/utils/data/sampler.py#L22"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.data.Sampler" title="Permalink to this definition">¶</a></dt>
 <dd><p>Base class for all Samplers.</p>
 <p>Every Sampler subclass has to provide an <code class="xref py py-meth docutils literal notranslate"><span class="pre">__iter__()</span></code> method, providing a
 way to iterate over indices or lists of indices (batches) of dataset elements,
@@ -1525,7 +1525,7 @@ <h2>Memory Pinning<a class="headerlink" href="#memory-pinning" title="Permalink
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.utils.data.SequentialSampler">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.utils.data.</span></span><span class="sig-name descname"><span class="pre">SequentialSampler</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">data_source</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/data/sampler.html#SequentialSampler"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/utils/data/sampler.py#L104"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.data.SequentialSampler" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.utils.data.</span></span><span class="sig-name descname"><span class="pre">SequentialSampler</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">data_source</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/data/sampler.html#SequentialSampler"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/utils/data/sampler.py#L104"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.data.SequentialSampler" title="Permalink to this definition">¶</a></dt>
 <dd><p>Samples elements sequentially, always in the same order.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -1536,7 +1536,7 @@ <h2>Memory Pinning<a class="headerlink" href="#memory-pinning" title="Permalink
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.utils.data.RandomSampler">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.utils.data.</span></span><span class="sig-name descname"><span class="pre">RandomSampler</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">data_source</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">replacement</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">generator</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/data/sampler.html#RandomSampler"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/utils/data/sampler.py#L123"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.data.RandomSampler" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.utils.data.</span></span><span class="sig-name descname"><span class="pre">RandomSampler</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">data_source</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">replacement</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">generator</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/data/sampler.html#RandomSampler"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/utils/data/sampler.py#L123"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.data.RandomSampler" title="Permalink to this definition">¶</a></dt>
 <dd><p>Samples elements randomly. If without replacement, then sample from a shuffled dataset.</p>
 <p>If with replacement, then user can specify <code class="xref py py-attr docutils literal notranslate"><span class="pre">num_samples</span></code> to draw.</p>
 <dl class="field-list simple">
@@ -1553,7 +1553,7 @@ <h2>Memory Pinning<a class="headerlink" href="#memory-pinning" title="Permalink
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.utils.data.SubsetRandomSampler">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.utils.data.</span></span><span class="sig-name descname"><span class="pre">SubsetRandomSampler</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">indices</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">generator</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/data/sampler.html#SubsetRandomSampler"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/utils/data/sampler.py#L198"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.data.SubsetRandomSampler" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.utils.data.</span></span><span class="sig-name descname"><span class="pre">SubsetRandomSampler</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">indices</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">generator</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/data/sampler.html#SubsetRandomSampler"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/utils/data/sampler.py#L198"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.data.SubsetRandomSampler" title="Permalink to this definition">¶</a></dt>
 <dd><p>Samples elements randomly from a given list of indices, without replacement.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -1567,7 +1567,7 @@ <h2>Memory Pinning<a class="headerlink" href="#memory-pinning" title="Permalink
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.utils.data.WeightedRandomSampler">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.utils.data.</span></span><span class="sig-name descname"><span class="pre">WeightedRandomSampler</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">weights</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_samples</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">replacement</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">generator</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/data/sampler.html#WeightedRandomSampler"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/utils/data/sampler.py#L220"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.data.WeightedRandomSampler" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.utils.data.</span></span><span class="sig-name descname"><span class="pre">WeightedRandomSampler</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">weights</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_samples</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">replacement</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">generator</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/data/sampler.html#WeightedRandomSampler"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/utils/data/sampler.py#L220"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.data.WeightedRandomSampler" title="Permalink to this definition">¶</a></dt>
 <dd><p>Samples elements from <code class="docutils literal notranslate"><span class="pre">[0,..,len(weights)-1]</span></code> with given probabilities (weights).</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -1592,7 +1592,7 @@ <h2>Memory Pinning<a class="headerlink" href="#memory-pinning" title="Permalink
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.utils.data.BatchSampler">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.utils.data.</span></span><span class="sig-name descname"><span class="pre">BatchSampler</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sampler</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">batch_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">drop_last</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/data/sampler.html#BatchSampler"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/utils/data/sampler.py#L285"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.data.BatchSampler" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.utils.data.</span></span><span class="sig-name descname"><span class="pre">BatchSampler</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sampler</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">batch_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">drop_last</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/data/sampler.html#BatchSampler"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/utils/data/sampler.py#L285"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.data.BatchSampler" title="Permalink to this definition">¶</a></dt>
 <dd><p>Wraps another sampler to yield a mini-batch of indices.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -1615,7 +1615,7 @@ <h2>Memory Pinning<a class="headerlink" href="#memory-pinning" title="Permalink
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.utils.data.distributed.DistributedSampler">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.utils.data.distributed.</span></span><span class="sig-name descname"><span class="pre">DistributedSampler</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dataset</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_replicas</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">rank</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shuffle</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">seed</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">drop_last</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/data/distributed.html#DistributedSampler"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/utils/data/distributed.py#L17"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.data.distributed.DistributedSampler" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.utils.data.distributed.</span></span><span class="sig-name descname"><span class="pre">DistributedSampler</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dataset</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_replicas</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">rank</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shuffle</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">seed</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">drop_last</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/data/distributed.html#DistributedSampler"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/utils/data/distributed.py#L17"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.data.distributed.DistributedSampler" title="Permalink to this definition">¶</a></dt>
 <dd><p>Sampler that restricts data loading to a subset of the dataset.</p>
 <p>It is especially useful in conjunction with
 <a class="reference internal" href="generated/torch.nn.parallel.DistributedDataParallel.html#torch.nn.parallel.DistributedDataParallel" title="torch.nn.parallel.DistributedDataParallel"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.nn.parallel.DistributedDataParallel</span></code></a>. In such a case, each
diff --git a/main/ddp_comm_hooks.html b/main/ddp_comm_hooks.html
index c8c4999dd0a..2aa4cede580 100644
--- a/main/ddp_comm_hooks.html
+++ b/main/ddp_comm_hooks.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -710,7 +710,7 @@ <h2>Default Communication Hooks<a class="headerlink" href="#default-communicatio
 The input <code class="docutils literal notranslate"><span class="pre">bucket</span></code> is a <a class="reference internal" href="#torch.distributed.GradBucket" title="torch.distributed.GradBucket"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.distributed.GradBucket</span></code></a> object.</p>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.distributed.algorithms.ddp_comm_hooks.default_hooks.allreduce_hook">
-<span class="sig-prename descclassname"><span class="pre">torch.distributed.algorithms.ddp_comm_hooks.default_hooks.</span></span><span class="sig-name descname"><span class="pre">allreduce_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">process_group</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bucket</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/algorithms/ddp_comm_hooks/default_hooks.html#allreduce_hook"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/algorithms/ddp_comm_hooks/default_hooks.py#L33"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.algorithms.ddp_comm_hooks.default_hooks.allreduce_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributed.algorithms.ddp_comm_hooks.default_hooks.</span></span><span class="sig-name descname"><span class="pre">allreduce_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">process_group</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bucket</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/algorithms/ddp_comm_hooks/default_hooks.html#allreduce_hook"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/algorithms/ddp_comm_hooks/default_hooks.py#L33"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.algorithms.ddp_comm_hooks.default_hooks.allreduce_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Call <code class="docutils literal notranslate"><span class="pre">allreduce</span></code> using <code class="docutils literal notranslate"><span class="pre">GradBucket</span></code> tensors.</p>
 <p>Once gradient tensors are aggregated across all workers, its <code class="docutils literal notranslate"><span class="pre">then</span></code>
 callback takes the mean and returns the result.</p>
@@ -734,7 +734,7 @@ <h2>Default Communication Hooks<a class="headerlink" href="#default-communicatio
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.distributed.algorithms.ddp_comm_hooks.default_hooks.fp16_compress_hook">
-<span class="sig-prename descclassname"><span class="pre">torch.distributed.algorithms.ddp_comm_hooks.default_hooks.</span></span><span class="sig-name descname"><span class="pre">fp16_compress_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">process_group</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bucket</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/algorithms/ddp_comm_hooks/default_hooks.html#fp16_compress_hook"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/algorithms/ddp_comm_hooks/default_hooks.py#L90"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.algorithms.ddp_comm_hooks.default_hooks.fp16_compress_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributed.algorithms.ddp_comm_hooks.default_hooks.</span></span><span class="sig-name descname"><span class="pre">fp16_compress_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">process_group</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bucket</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/algorithms/ddp_comm_hooks/default_hooks.html#fp16_compress_hook"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/algorithms/ddp_comm_hooks/default_hooks.py#L90"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.algorithms.ddp_comm_hooks.default_hooks.fp16_compress_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Compress by casting <code class="docutils literal notranslate"><span class="pre">GradBucket</span></code> to <code class="docutils literal notranslate"><span class="pre">torch.float16</span></code> divided by process group size.</p>
 <p>This DDP communication hook implements a simple gradient compression
 approach that casts <code class="docutils literal notranslate"><span class="pre">GradBucket</span></code> tensor to half-precision floating-point format (<code class="docutils literal notranslate"><span class="pre">torch.float16</span></code>)
@@ -756,7 +756,7 @@ <h2>Default Communication Hooks<a class="headerlink" href="#default-communicatio
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.distributed.algorithms.ddp_comm_hooks.default_hooks.bf16_compress_hook">
-<span class="sig-prename descclassname"><span class="pre">torch.distributed.algorithms.ddp_comm_hooks.default_hooks.</span></span><span class="sig-name descname"><span class="pre">bf16_compress_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">process_group</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bucket</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/algorithms/ddp_comm_hooks/default_hooks.html#bf16_compress_hook"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/algorithms/ddp_comm_hooks/default_hooks.py#L110"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.algorithms.ddp_comm_hooks.default_hooks.bf16_compress_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributed.algorithms.ddp_comm_hooks.default_hooks.</span></span><span class="sig-name descname"><span class="pre">bf16_compress_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">process_group</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bucket</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/algorithms/ddp_comm_hooks/default_hooks.html#bf16_compress_hook"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/algorithms/ddp_comm_hooks/default_hooks.py#L110"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.algorithms.ddp_comm_hooks.default_hooks.bf16_compress_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Warning: This API is experimental, and it requires NCCL version later than 2.9.6.</p>
 <p>This DDP communication hook implements a simple gradient compression
 approach that casts <code class="docutils literal notranslate"><span class="pre">GradBucket</span></code> tensor to half-precision
@@ -781,7 +781,7 @@ <h2>Default Communication Hooks<a class="headerlink" href="#default-communicatio
 which can be combined with other communication hooks.</p>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.distributed.algorithms.ddp_comm_hooks.default_hooks.fp16_compress_wrapper">
-<span class="sig-prename descclassname"><span class="pre">torch.distributed.algorithms.ddp_comm_hooks.default_hooks.</span></span><span class="sig-name descname"><span class="pre">fp16_compress_wrapper</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/algorithms/ddp_comm_hooks/default_hooks.html#fp16_compress_wrapper"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/algorithms/ddp_comm_hooks/default_hooks.py#L131"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.algorithms.ddp_comm_hooks.default_hooks.fp16_compress_wrapper" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributed.algorithms.ddp_comm_hooks.default_hooks.</span></span><span class="sig-name descname"><span class="pre">fp16_compress_wrapper</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/algorithms/ddp_comm_hooks/default_hooks.html#fp16_compress_wrapper"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/algorithms/ddp_comm_hooks/default_hooks.py#L131"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.algorithms.ddp_comm_hooks.default_hooks.fp16_compress_wrapper" title="Permalink to this definition">¶</a></dt>
 <dd><p>Cast input tensor to <code class="docutils literal notranslate"><span class="pre">torch.float16</span></code>, cast result of hook back to input dtype.</p>
 <p>This wrapper casts the input gradient tensor of a given DDP communication hook to half-precision
 floating point format (<code class="docutils literal notranslate"><span class="pre">torch.float16</span></code>), and casts the resulting tensor of the given hook back to
@@ -803,7 +803,7 @@ <h2>Default Communication Hooks<a class="headerlink" href="#default-communicatio
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.distributed.algorithms.ddp_comm_hooks.default_hooks.bf16_compress_wrapper">
-<span class="sig-prename descclassname"><span class="pre">torch.distributed.algorithms.ddp_comm_hooks.default_hooks.</span></span><span class="sig-name descname"><span class="pre">bf16_compress_wrapper</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/algorithms/ddp_comm_hooks/default_hooks.html#bf16_compress_wrapper"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/algorithms/ddp_comm_hooks/default_hooks.py#L169"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.algorithms.ddp_comm_hooks.default_hooks.bf16_compress_wrapper" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributed.algorithms.ddp_comm_hooks.default_hooks.</span></span><span class="sig-name descname"><span class="pre">bf16_compress_wrapper</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/algorithms/ddp_comm_hooks/default_hooks.html#bf16_compress_wrapper"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/algorithms/ddp_comm_hooks/default_hooks.py#L169"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.algorithms.ddp_comm_hooks.default_hooks.bf16_compress_wrapper" title="Permalink to this definition">¶</a></dt>
 <dd><p>Warning: This API is experimental, and it requires NCCL version later than 2.9.6.</p>
 <p>This wrapper casts the input gradient tensor of a given DDP communication hook to half-precision
 <cite>Brain floating point format &lt;https://en.wikipedia.org/wiki/Bfloat16_floating-point_format&gt; `_  (``torch.bfloat16`</cite>),
@@ -836,7 +836,7 @@ <h2>PowerSGD Communication Hook<a class="headerlink" href="#powersgd-communicati
 <h3>PowerSGD State<a class="headerlink" href="#powersgd-state" title="Permalink to this heading">¶</a></h3>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributed.algorithms.ddp_comm_hooks.powerSGD_hook.PowerSGDState">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.algorithms.ddp_comm_hooks.powerSGD_hook.</span></span><span class="sig-name descname"><span class="pre">PowerSGDState</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">process_group</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">matrix_approximation_rank</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">start_powerSGD_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1000</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_compression_rate</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_error_feedback</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">warm_start</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">orthogonalization_epsilon</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_seed</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">compression_stats_logging_frequency</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10000</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">batch_tensors_with_same_shape</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/algorithms/ddp_comm_hooks/powerSGD_hook.html#PowerSGDState"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/algorithms/ddp_comm_hooks/powerSGD_hook.py#L121"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.algorithms.ddp_comm_hooks.powerSGD_hook.PowerSGDState" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.algorithms.ddp_comm_hooks.powerSGD_hook.</span></span><span class="sig-name descname"><span class="pre">PowerSGDState</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">process_group</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">matrix_approximation_rank</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">start_powerSGD_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1000</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_compression_rate</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_error_feedback</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">warm_start</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">orthogonalization_epsilon</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_seed</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">compression_stats_logging_frequency</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10000</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">batch_tensors_with_same_shape</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/algorithms/ddp_comm_hooks/powerSGD_hook.html#PowerSGDState"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/algorithms/ddp_comm_hooks/powerSGD_hook.py#L121"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.algorithms.ddp_comm_hooks.powerSGD_hook.PowerSGDState" title="Permalink to this definition">¶</a></dt>
 <dd><p>Store both the algorithm’s hyperparameters and internal state for all gradients during training.</p>
 <p>Particularly, <code class="docutils literal notranslate"><span class="pre">matrix_approximation_rank</span></code> and <code class="docutils literal notranslate"><span class="pre">start_powerSGD_iter</span></code> are the main hyperparameters that should be tuned by the user.
 For performance, we suggest to keep binary hyperparameters <code class="docutils literal notranslate"><span class="pre">use_error_feedback</span></code> and <code class="docutils literal notranslate"><span class="pre">warm_start</span></code> on.</p>
@@ -888,7 +888,7 @@ <h3>PowerSGD Hooks<a class="headerlink" href="#powersgd-hooks" title="Permalink
 </div>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.distributed.algorithms.ddp_comm_hooks.powerSGD_hook.powerSGD_hook">
-<span class="sig-prename descclassname"><span class="pre">torch.distributed.algorithms.ddp_comm_hooks.powerSGD_hook.</span></span><span class="sig-name descname"><span class="pre">powerSGD_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bucket</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/algorithms/ddp_comm_hooks/powerSGD_hook.html#powerSGD_hook"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/algorithms/ddp_comm_hooks/powerSGD_hook.py#L339"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.algorithms.ddp_comm_hooks.powerSGD_hook.powerSGD_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributed.algorithms.ddp_comm_hooks.powerSGD_hook.</span></span><span class="sig-name descname"><span class="pre">powerSGD_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bucket</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/algorithms/ddp_comm_hooks/powerSGD_hook.html#powerSGD_hook"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/algorithms/ddp_comm_hooks/powerSGD_hook.py#L339"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.algorithms.ddp_comm_hooks.powerSGD_hook.powerSGD_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Implement PowerSGD algorithm.</p>
 <p>This DDP communication hook implements PowerSGD gradient compression
 algorithm described in the <a class="reference external" href="https://arxiv.org/abs/1905.13727">paper</a>.
@@ -953,7 +953,7 @@ <h3>PowerSGD Hooks<a class="headerlink" href="#powersgd-hooks" title="Permalink
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.distributed.algorithms.ddp_comm_hooks.powerSGD_hook.batched_powerSGD_hook">
-<span class="sig-prename descclassname"><span class="pre">torch.distributed.algorithms.ddp_comm_hooks.powerSGD_hook.</span></span><span class="sig-name descname"><span class="pre">batched_powerSGD_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bucket</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/algorithms/ddp_comm_hooks/powerSGD_hook.html#batched_powerSGD_hook"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/algorithms/ddp_comm_hooks/powerSGD_hook.py#L652"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.algorithms.ddp_comm_hooks.powerSGD_hook.batched_powerSGD_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributed.algorithms.ddp_comm_hooks.powerSGD_hook.</span></span><span class="sig-name descname"><span class="pre">batched_powerSGD_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bucket</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/algorithms/ddp_comm_hooks/powerSGD_hook.html#batched_powerSGD_hook"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/algorithms/ddp_comm_hooks/powerSGD_hook.py#L652"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.algorithms.ddp_comm_hooks.powerSGD_hook.batched_powerSGD_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Implement simplified PowerSGD algorithm.</p>
 <p>This DDP communication hook implements a simplified PowerSGD gradient compression
 algorithm described in the <a class="reference external" href="https://arxiv.org/abs/1905.13727">paper</a>.
@@ -1021,7 +1021,7 @@ <h2>Debugging Communication Hooks<a class="headerlink" href="#debugging-communic
 </div>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.distributed.algorithms.ddp_comm_hooks.debugging_hooks.noop_hook">
-<span class="sig-prename descclassname"><span class="pre">torch.distributed.algorithms.ddp_comm_hooks.debugging_hooks.</span></span><span class="sig-name descname"><span class="pre">noop_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">_</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bucket</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/algorithms/ddp_comm_hooks/debugging_hooks.html#noop_hook"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/algorithms/ddp_comm_hooks/debugging_hooks.py#L10"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.algorithms.ddp_comm_hooks.debugging_hooks.noop_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributed.algorithms.ddp_comm_hooks.debugging_hooks.</span></span><span class="sig-name descname"><span class="pre">noop_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">_</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bucket</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/algorithms/ddp_comm_hooks/debugging_hooks.html#noop_hook"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/algorithms/ddp_comm_hooks/debugging_hooks.py#L10"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.algorithms.ddp_comm_hooks.debugging_hooks.noop_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return a future that wraps the input, so it is a no-op that does not incur any communication overheads.</p>
 <p>This hook should <strong>only</strong> be used for headroom analysis of allreduce optimization,
 instead of the normal gradient synchronization.
@@ -1059,12 +1059,12 @@ <h2>Checkpointing of Communication Hooks<a class="headerlink" href="#checkpointi
 <p><a class="reference internal" href="#torch.distributed.algorithms.ddp_comm_hooks.powerSGD_hook.PowerSGDState" title="torch.distributed.algorithms.ddp_comm_hooks.powerSGD_hook.PowerSGDState"><code class="xref py py-class docutils literal notranslate"><span class="pre">PowerSGDState</span></code></a> has <code class="docutils literal notranslate"><span class="pre">__setstate__</span></code> and <code class="docutils literal notranslate"><span class="pre">__getstate__</span></code> implemented and can be used as a reference.</p>
 <dl class="py class">
 <dt class="sig sig-object py">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.algorithms.ddp_comm_hooks.powerSGD_hook.</span></span><span class="sig-name descname"><span class="pre">PowerSGDState</span></span><a class="reference internal" href="_modules/torch/distributed/algorithms/ddp_comm_hooks/powerSGD_hook.html#PowerSGDState"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/algorithms/ddp_comm_hooks/powerSGD_hook.py#L121"><span class="viewcode-link"><span class="pre">[source]</span></span></a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.algorithms.ddp_comm_hooks.powerSGD_hook.</span></span><span class="sig-name descname"><span class="pre">PowerSGDState</span></span><a class="reference internal" href="_modules/torch/distributed/algorithms/ddp_comm_hooks/powerSGD_hook.html#PowerSGDState"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/algorithms/ddp_comm_hooks/powerSGD_hook.py#L121"><span class="viewcode-link"><span class="pre">[source]</span></span></a></dt>
 <dd><dl class="field-list simple">
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.algorithms.ddp_comm_hooks.powerSGD_hook.PowerSGDState.__getstate__">
-<span class="sig-name descname"><span class="pre">__getstate__</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/algorithms/ddp_comm_hooks/powerSGD_hook.html#PowerSGDState.__getstate__"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/algorithms/ddp_comm_hooks/powerSGD_hook.py#L275"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.algorithms.ddp_comm_hooks.powerSGD_hook.PowerSGDState.__getstate__" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">__getstate__</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/algorithms/ddp_comm_hooks/powerSGD_hook.html#PowerSGDState.__getstate__"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/algorithms/ddp_comm_hooks/powerSGD_hook.py#L275"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.algorithms.ddp_comm_hooks.powerSGD_hook.PowerSGDState.__getstate__" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return a <code class="docutils literal notranslate"><span class="pre">Dict[str,</span> <span class="pre">Any]</span></code> which will be pickled and saved.</p>
 <p><code class="docutils literal notranslate"><span class="pre">process_group</span></code> is not serializable and excluded from
 a returned state.</p>
@@ -1072,7 +1072,7 @@ <h2>Checkpointing of Communication Hooks<a class="headerlink" href="#checkpointi
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.algorithms.ddp_comm_hooks.powerSGD_hook.PowerSGDState.__setstate__">
-<span class="sig-name descname"><span class="pre">__setstate__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/algorithms/ddp_comm_hooks/powerSGD_hook.html#PowerSGDState.__setstate__"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/algorithms/ddp_comm_hooks/powerSGD_hook.py#L291"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.algorithms.ddp_comm_hooks.powerSGD_hook.PowerSGDState.__setstate__" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">__setstate__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/algorithms/ddp_comm_hooks/powerSGD_hook.html#PowerSGDState.__setstate__"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/algorithms/ddp_comm_hooks/powerSGD_hook.py#L291"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.algorithms.ddp_comm_hooks.powerSGD_hook.PowerSGDState.__setstate__" title="Permalink to this definition">¶</a></dt>
 <dd><p>Take a provided <code class="docutils literal notranslate"><span class="pre">state</span></code> and set to this <code class="docutils literal notranslate"><span class="pre">PowerSGDState</span></code> instance.</p>
 <p><code class="docutils literal notranslate"><span class="pre">process_group</span></code> is set to default.</p>
 </dd></dl>
diff --git a/main/debugging_environment_variables.html b/main/debugging_environment_variables.html
index 9fdffc96691..5fd5252ad8b 100644
--- a/main/debugging_environment_variables.html
+++ b/main/debugging_environment_variables.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/deploy.html b/main/deploy.html
index a75622e175e..a5bc99a4f0d 100644
--- a/main/deploy.html
+++ b/main/deploy.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/deterministic.html b/main/deterministic.html
index 29c50724b70..5838d46ce1e 100644
--- a/main/deterministic.html
+++ b/main/deterministic.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/distributed.algorithms.join.html b/main/distributed.algorithms.join.html
index f8974231836..c057e27f961 100644
--- a/main/distributed.algorithms.join.html
+++ b/main/distributed.algorithms.join.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@ <h1>Generic Join Context Manager<a class="headerlink" href="#generic-join-contex
 <a class="reference external" href="https://pytorch.org/tutorials/advanced/generic_join.html">Distributed Training with Uneven Inputs Using the Join Context Manager</a>.</p>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributed.algorithms.Join">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.algorithms.</span></span><span class="sig-name descname"><span class="pre">Join</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">joinables</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">enable</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">throw_on_early_termination</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/algorithms/join.html#Join"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/algorithms/join.py#L104"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.algorithms.Join" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.algorithms.</span></span><span class="sig-name descname"><span class="pre">Join</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">joinables</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">enable</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">throw_on_early_termination</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/algorithms/join.html#Join"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/algorithms/join.py#L104"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.algorithms.Join" title="Permalink to this definition">¶</a></dt>
 <dd><p>This class defines the generic join context manager, which allows custom hooks to be called after a process joins.</p>
 <p>These hooks should shadow the
 collective communications of non-joined processes to prevent hanging and
@@ -677,7 +677,7 @@ <h1>Generic Join Context Manager<a class="headerlink" href="#generic-join-contex
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.algorithms.Join.notify_join_context">
-<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">notify_join_context</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">joinable</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/algorithms/join.html#Join.notify_join_context"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/algorithms/join.py#L296"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.algorithms.Join.notify_join_context" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">notify_join_context</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">joinable</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/algorithms/join.html#Join.notify_join_context"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/algorithms/join.py#L296"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.algorithms.Join.notify_join_context" title="Permalink to this definition">¶</a></dt>
 <dd><p>Notifies the join context manager that the calling process has not yet joined.</p>
 <p>Then, if <code class="docutils literal notranslate"><span class="pre">throw_on_early_termination=True</span></code>, checks if uneven inputs have been detected
 (i.e. if one process has already joined) and throws an exception if so.</p>
@@ -705,7 +705,7 @@ <h1>Generic Join Context Manager<a class="headerlink" href="#generic-join-contex
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributed.algorithms.Joinable">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.algorithms.</span></span><span class="sig-name descname"><span class="pre">Joinable</span></span><a class="reference internal" href="_modules/torch/distributed/algorithms/join.html#Joinable"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/algorithms/join.py#L44"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.algorithms.Joinable" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.algorithms.</span></span><span class="sig-name descname"><span class="pre">Joinable</span></span><a class="reference internal" href="_modules/torch/distributed/algorithms/join.html#Joinable"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/algorithms/join.py#L44"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.algorithms.Joinable" title="Permalink to this definition">¶</a></dt>
 <dd><p>This defines an abstract base class for joinable classes.</p>
 <p>A joinable class
 (inheriting from <a class="reference internal" href="#torch.distributed.algorithms.Joinable" title="torch.distributed.algorithms.Joinable"><code class="xref py py-class docutils literal notranslate"><span class="pre">Joinable</span></code></a>) should implement <a class="reference internal" href="#torch.distributed.algorithms.Joinable.join_hook" title="torch.distributed.algorithms.Joinable.join_hook"><code class="xref py py-meth docutils literal notranslate"><span class="pre">join_hook()</span></code></a>,
@@ -722,7 +722,7 @@ <h1>Generic Join Context Manager<a class="headerlink" href="#generic-join-contex
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.algorithms.Joinable.join_hook">
-<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">join_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/algorithms/join.html#Joinable.join_hook"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/algorithms/join.py#L60"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.algorithms.Joinable.join_hook" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">join_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/algorithms/join.html#Joinable.join_hook"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/algorithms/join.py#L60"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.algorithms.Joinable.join_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return a <a class="reference internal" href="#torch.distributed.algorithms.JoinHook" title="torch.distributed.algorithms.JoinHook"><code class="xref py py-class docutils literal notranslate"><span class="pre">JoinHook</span></code></a> instance for the given <a class="reference internal" href="#torch.distributed.algorithms.Joinable" title="torch.distributed.algorithms.Joinable"><code class="xref py py-class docutils literal notranslate"><span class="pre">Joinable</span></code></a>.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -747,7 +747,7 @@ <h1>Generic Join Context Manager<a class="headerlink" href="#generic-join-contex
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributed.algorithms.JoinHook">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.algorithms.</span></span><span class="sig-name descname"><span class="pre">JoinHook</span></span><a class="reference internal" href="_modules/torch/distributed/algorithms/join.html#JoinHook"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/algorithms/join.py#L14"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.algorithms.JoinHook" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.algorithms.</span></span><span class="sig-name descname"><span class="pre">JoinHook</span></span><a class="reference internal" href="_modules/torch/distributed/algorithms/join.html#JoinHook"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/algorithms/join.py#L14"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.algorithms.JoinHook" title="Permalink to this definition">¶</a></dt>
 <dd><p>This defines a join hook, which provides two entry points in the join context manager.</p>
 <p>Entry points : a main hook, which is called repeatedly while there exists a non-joined
 process, and a post-hook, which is called once all processes have joined.</p>
@@ -756,7 +756,7 @@ <h1>Generic Join Context Manager<a class="headerlink" href="#generic-join-contex
 <code class="docutils literal notranslate"><span class="pre">post_hook()</span></code> as appropriate.</p>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.algorithms.JoinHook.main_hook">
-<span class="sig-name descname"><span class="pre">main_hook</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/algorithms/join.html#JoinHook.main_hook"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/algorithms/join.py#L26"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.algorithms.JoinHook.main_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">main_hook</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/algorithms/join.html#JoinHook.main_hook"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/algorithms/join.py#L26"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.algorithms.JoinHook.main_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Call this hook while there exists a non-joined process to shadow collective communications in a training iteration.</p>
 <p>Training iteration i.e., in one forward pass, backward pass, and optimizer step.</p>
 <dl class="field-list simple">
@@ -765,7 +765,7 @@ <h1>Generic Join Context Manager<a class="headerlink" href="#generic-join-contex
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.algorithms.JoinHook.post_hook">
-<span class="sig-name descname"><span class="pre">post_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">is_last_joiner</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/algorithms/join.html#JoinHook.post_hook"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/algorithms/join.py#L32"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.algorithms.JoinHook.post_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">post_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">is_last_joiner</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/algorithms/join.html#JoinHook.post_hook"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/algorithms/join.py#L32"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.algorithms.JoinHook.post_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Call hook after all processes have joined.</p>
 <p>It is passed an additional <code class="docutils literal notranslate"><span class="pre">bool</span></code> argument <code class="docutils literal notranslate"><span class="pre">is_last_joiner</span></code>, which indicates if the rank is one of the last to join.</p>
 <dl class="field-list simple">
diff --git a/main/distributed.checkpoint.html b/main/distributed.checkpoint.html
index 12308a7bda3..8fcd5f141d3 100644
--- a/main/distributed.checkpoint.html
+++ b/main/distributed.checkpoint.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -626,7 +626,7 @@ <h2>Additional resources:<a class="headerlink" href="#additional-resources" titl
 </ul>
 <span class="target" id="module-torch.distributed.checkpoint"></span><dl class="py function">
 <dt class="sig sig-object py" id="torch.distributed.checkpoint.state_dict_saver.save">
-<span class="sig-prename descclassname"><span class="pre">torch.distributed.checkpoint.state_dict_saver.</span></span><span class="sig-name descname"><span class="pre">save</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state_dict</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">checkpoint_id</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">storage_writer</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">planner</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">process_group</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">no_dist</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/state_dict_saver.html#save"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/checkpoint/state_dict_saver.py#L57"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.state_dict_saver.save" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributed.checkpoint.state_dict_saver.</span></span><span class="sig-name descname"><span class="pre">save</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state_dict</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">checkpoint_id</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">storage_writer</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">planner</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">process_group</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">no_dist</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/state_dict_saver.html#save"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/checkpoint/state_dict_saver.py#L57"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.state_dict_saver.save" title="Permalink to this definition">¶</a></dt>
 <dd><p>Save a distributed model in SPMD style.</p>
 <p>This function is different from <code class="docutils literal notranslate"><span class="pre">torch.save()</span></code> as it handles
 <code class="docutils literal notranslate"><span class="pre">ShardedTensor</span></code> , and <code class="docutils literal notranslate"><span class="pre">DTensor</span></code> by having each rank only save their local shards.</p>
@@ -710,7 +710,7 @@ <h2>Additional resources:<a class="headerlink" href="#additional-resources" titl
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.distributed.checkpoint.state_dict_saver.async_save">
-<span class="sig-prename descclassname"><span class="pre">torch.distributed.checkpoint.state_dict_saver.</span></span><span class="sig-name descname"><span class="pre">async_save</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state_dict</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">checkpoint_id</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">storage_writer</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">planner</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">process_group</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/state_dict_saver.html#async_save"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/checkpoint/state_dict_saver.py#L166"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.state_dict_saver.async_save" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributed.checkpoint.state_dict_saver.</span></span><span class="sig-name descname"><span class="pre">async_save</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state_dict</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">checkpoint_id</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">storage_writer</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">planner</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">process_group</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/state_dict_saver.html#async_save"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/checkpoint/state_dict_saver.py#L166"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.state_dict_saver.async_save" title="Permalink to this definition">¶</a></dt>
 <dd><p>Asynchronous version of <code class="docutils literal notranslate"><span class="pre">save</span></code>. This code first de-stages the state_dict on to the
 staging storage (defaults to CPU memory), and then calls the <cite>save</cite> in a separate thread.</p>
 <div class="admonition warning">
@@ -764,7 +764,7 @@ <h2>Additional resources:<a class="headerlink" href="#additional-resources" titl
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.distributed.checkpoint.state_dict_saver.save_state_dict">
-<span class="sig-prename descclassname"><span class="pre">torch.distributed.checkpoint.state_dict_saver.</span></span><span class="sig-name descname"><span class="pre">save_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state_dict</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">storage_writer</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">process_group</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">coordinator_rank</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">no_dist</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">planner</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/state_dict_saver.html#save_state_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/checkpoint/state_dict_saver.py#L29"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.state_dict_saver.save_state_dict" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributed.checkpoint.state_dict_saver.</span></span><span class="sig-name descname"><span class="pre">save_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state_dict</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">storage_writer</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">process_group</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">coordinator_rank</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">no_dist</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">planner</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/state_dict_saver.html#save_state_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/checkpoint/state_dict_saver.py#L29"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.state_dict_saver.save_state_dict" title="Permalink to this definition">¶</a></dt>
 <dd><p>This method is deprecated. Please switch to ‘save’.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -775,7 +775,7 @@ <h2>Additional resources:<a class="headerlink" href="#additional-resources" titl
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.distributed.checkpoint.state_dict_loader.load">
-<span class="sig-prename descclassname"><span class="pre">torch.distributed.checkpoint.state_dict_loader.</span></span><span class="sig-name descname"><span class="pre">load</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state_dict</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">checkpoint_id</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">storage_reader</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">planner</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">process_group</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">no_dist</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/state_dict_loader.html#load"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/checkpoint/state_dict_loader.py#L51"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.state_dict_loader.load" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributed.checkpoint.state_dict_loader.</span></span><span class="sig-name descname"><span class="pre">load</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state_dict</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">checkpoint_id</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">storage_reader</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">planner</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">process_group</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">no_dist</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/state_dict_loader.html#load"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/checkpoint/state_dict_loader.py#L51"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.state_dict_loader.load" title="Permalink to this definition">¶</a></dt>
 <dd><p>Load a distributed <code class="docutils literal notranslate"><span class="pre">state_dict</span></code> in SPMD style.</p>
 <p>Each rank will try to read the least amount of data necessary
 to fullfill the requested <cite>state_dict</cite>. When loading <code class="xref py py-class docutils literal notranslate"><span class="pre">ShardedTensor</span></code>
@@ -858,7 +858,7 @@ <h2>Additional resources:<a class="headerlink" href="#additional-resources" titl
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.distributed.checkpoint.state_dict_loader.load_state_dict">
-<span class="sig-prename descclassname"><span class="pre">torch.distributed.checkpoint.state_dict_loader.</span></span><span class="sig-name descname"><span class="pre">load_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state_dict</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">storage_reader</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">process_group</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">coordinator_rank</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">no_dist</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">planner</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/state_dict_loader.html#load_state_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/checkpoint/state_dict_loader.py#L24"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.state_dict_loader.load_state_dict" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributed.checkpoint.state_dict_loader.</span></span><span class="sig-name descname"><span class="pre">load_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state_dict</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">storage_reader</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">process_group</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">coordinator_rank</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">no_dist</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">planner</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/state_dict_loader.html#load_state_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/checkpoint/state_dict_loader.py#L24"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.state_dict_loader.load_state_dict" title="Permalink to this definition">¶</a></dt>
 <dd><p>This method is deprecated. Please switch to ‘load’.</p>
 <dl class="field-list simple">
 </dl>
@@ -867,7 +867,7 @@ <h2>Additional resources:<a class="headerlink" href="#additional-resources" titl
 <p>The following module is also useful for additional customization of the staging mechanisms used for asynchronous checkpointing (<cite>torch.distributed.checkpoint.async_save</cite>):</p>
 <span class="target" id="module-torch.distributed.checkpoint.staging"></span><dl class="py class">
 <dt class="sig sig-object py" id="torch.distributed.checkpoint.staging.AsyncStager">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.checkpoint.staging.</span></span><span class="sig-name descname"><span class="pre">AsyncStager</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/staging.html#AsyncStager"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/checkpoint/staging.py#L11"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.staging.AsyncStager" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.checkpoint.staging.</span></span><span class="sig-name descname"><span class="pre">AsyncStager</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/staging.html#AsyncStager"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/checkpoint/staging.py#L11"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.staging.AsyncStager" title="Permalink to this definition">¶</a></dt>
 <dd><p>This protocol is meant to provide customization and extensibility for dcp.async_save, allowing users
 to customize how data is staged previous to executing the usual dcp.save path in parallel.
 The expected order of operations (concretely defined in <cite>torch.distributed.state_dict_saver.async_save</cite>)
@@ -906,7 +906,7 @@ <h2>Additional resources:<a class="headerlink" href="#additional-resources" titl
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.checkpoint.staging.AsyncStager.stage">
-<span class="sig-name descname"><span class="pre">stage</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state_dict</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/staging.html#AsyncStager.stage"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/checkpoint/staging.py#L50"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.staging.AsyncStager.stage" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">stage</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state_dict</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/staging.html#AsyncStager.stage"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/checkpoint/staging.py#L50"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.staging.AsyncStager.stage" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns a “staged” copy of <cite>state_dict</cite>. The expectation of the staged copy is that it is
 innoculated from any updates incurred after the stage call is complete.</p>
 <dl class="field-list simple">
@@ -918,7 +918,7 @@ <h2>Additional resources:<a class="headerlink" href="#additional-resources" titl
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.checkpoint.staging.AsyncStager.synchronize_staging">
-<span class="sig-name descname"><span class="pre">synchronize_staging</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/staging.html#AsyncStager.synchronize_staging"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/checkpoint/staging.py#L59"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.staging.AsyncStager.synchronize_staging" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">synchronize_staging</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/staging.html#AsyncStager.synchronize_staging"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/checkpoint/staging.py#L59"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.staging.AsyncStager.synchronize_staging" title="Permalink to this definition">¶</a></dt>
 <dd><p>In the case <cite>stage</cite> is async in some way, this method should be called to ensure staging
 is complete and it is safe to begin modifying the original <cite>state_dict</cite></p>
 <dl class="field-list simple">
@@ -929,7 +929,7 @@ <h2>Additional resources:<a class="headerlink" href="#additional-resources" titl
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributed.checkpoint.staging.BlockingAsyncStager">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.checkpoint.staging.</span></span><span class="sig-name descname"><span class="pre">BlockingAsyncStager</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">cache_staged_state_dict</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">type_check</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/staging.html#BlockingAsyncStager"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/checkpoint/staging.py#L66"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.staging.BlockingAsyncStager" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.checkpoint.staging.</span></span><span class="sig-name descname"><span class="pre">BlockingAsyncStager</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">cache_staged_state_dict</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">type_check</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/staging.html#BlockingAsyncStager"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/checkpoint/staging.py#L66"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.staging.BlockingAsyncStager" title="Permalink to this definition">¶</a></dt>
 <dd><p>An implementation of AsyncStager which stages the state_dict on CPU RAM and blocks until the copy is complete.
 This implementation also provides an option to optimize stage latency using pinned memory.</p>
 <p>N.B. synchronize_staging is a no-op in this case.</p>
@@ -937,7 +937,7 @@ <h2>Additional resources:<a class="headerlink" href="#additional-resources" titl
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.checkpoint.staging.BlockingAsyncStager.stage">
-<span class="sig-name descname"><span class="pre">stage</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state_dict</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/staging.html#BlockingAsyncStager.stage"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/checkpoint/staging.py#L98"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.staging.BlockingAsyncStager.stage" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">stage</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state_dict</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/staging.html#BlockingAsyncStager.stage"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/checkpoint/staging.py#L98"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.staging.BlockingAsyncStager.stage" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns a copy of <cite>state_dict</cite> on the CPU.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -948,7 +948,7 @@ <h2>Additional resources:<a class="headerlink" href="#additional-resources" titl
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.checkpoint.staging.BlockingAsyncStager.synchronize_staging">
-<span class="sig-name descname"><span class="pre">synchronize_staging</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/staging.html#BlockingAsyncStager.synchronize_staging"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/checkpoint/staging.py#L112"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.staging.BlockingAsyncStager.synchronize_staging" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">synchronize_staging</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/staging.html#BlockingAsyncStager.synchronize_staging"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/checkpoint/staging.py#L112"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.staging.BlockingAsyncStager.synchronize_staging" title="Permalink to this definition">¶</a></dt>
 <dd><p>No-op function, since staging is blocking.</p>
 <dl class="field-list simple">
 </dl>
@@ -960,11 +960,11 @@ <h2>Additional resources:<a class="headerlink" href="#additional-resources" titl
 .. automodule:: torch.distributed.checkpoint.stateful</p>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributed.checkpoint.stateful.Stateful">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.checkpoint.stateful.</span></span><span class="sig-name descname"><span class="pre">Stateful</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/stateful.html#Stateful"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/checkpoint/stateful.py#L8"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.stateful.Stateful" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.checkpoint.stateful.</span></span><span class="sig-name descname"><span class="pre">Stateful</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/stateful.html#Stateful"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/checkpoint/stateful.py#L8"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.stateful.Stateful" title="Permalink to this definition">¶</a></dt>
 <dd><p>Stateful protocol for objects that can be checkpointed and restored.</p>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.checkpoint.stateful.Stateful.load_state_dict">
-<span class="sig-name descname"><span class="pre">load_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state_dict</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/stateful.html#Stateful.load_state_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/checkpoint/stateful.py#L31"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.stateful.Stateful.load_state_dict" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">load_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state_dict</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/stateful.html#Stateful.load_state_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/checkpoint/stateful.py#L31"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.stateful.Stateful.load_state_dict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Restore the object’s state from the provided state_dict.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -975,7 +975,7 @@ <h2>Additional resources:<a class="headerlink" href="#additional-resources" titl
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.checkpoint.stateful.Stateful.state_dict">
-<span class="sig-name descname"><span class="pre">state_dict</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/stateful.html#Stateful.state_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/checkpoint/stateful.py#L14"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.stateful.Stateful.state_dict" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">state_dict</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/stateful.html#Stateful.state_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/checkpoint/stateful.py#L14"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.stateful.Stateful.state_dict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Objects should return their state_dict representation as a dictionary.
 The output of this function will be checkpointed, and later restored in
 <cite>load_state_dict()</cite>.</p>
@@ -1000,7 +1000,7 @@ <h2>Additional resources:<a class="headerlink" href="#additional-resources" titl
 <p>The following types define the IO interface used during checkpoint:</p>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributed.checkpoint.StorageReader">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.checkpoint.</span></span><span class="sig-name descname"><span class="pre">StorageReader</span></span><a class="reference internal" href="_modules/torch/distributed/checkpoint/storage.html#StorageReader"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/checkpoint/storage.py#L166"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.StorageReader" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.checkpoint.</span></span><span class="sig-name descname"><span class="pre">StorageReader</span></span><a class="reference internal" href="_modules/torch/distributed/checkpoint/storage.html#StorageReader"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/checkpoint/storage.py#L166"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.StorageReader" title="Permalink to this definition">¶</a></dt>
 <dd><p>Interface used by <code class="docutils literal notranslate"><span class="pre">load_state_dict</span></code> to read from storage.</p>
 <p>One StorageReader instance acts as both the coordinator and the follower
 in a distributed checkpoint. As part of initialization, each instance
@@ -1016,7 +1016,7 @@ <h2>Additional resources:<a class="headerlink" href="#additional-resources" titl
 </ol>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.checkpoint.StorageReader.prepare_global_plan">
-<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">prepare_global_plan</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">plans</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/storage.html#StorageReader.prepare_global_plan"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/checkpoint/storage.py#L238"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.StorageReader.prepare_global_plan" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">prepare_global_plan</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">plans</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/storage.html#StorageReader.prepare_global_plan"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/checkpoint/storage.py#L238"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.StorageReader.prepare_global_plan" title="Permalink to this definition">¶</a></dt>
 <dd><p>Perform centralized planning of storage loading.</p>
 <p>This method is only called on the coordinator instance.</p>
 <p>While this method can produce a completely different plan, the preferred
@@ -1036,7 +1036,7 @@ <h2>Additional resources:<a class="headerlink" href="#additional-resources" titl
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.checkpoint.StorageReader.prepare_local_plan">
-<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">prepare_local_plan</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">plan</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/storage.html#StorageReader.prepare_local_plan"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/checkpoint/storage.py#L223"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.StorageReader.prepare_local_plan" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">prepare_local_plan</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">plan</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/storage.html#StorageReader.prepare_local_plan"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/checkpoint/storage.py#L223"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.StorageReader.prepare_local_plan" title="Permalink to this definition">¶</a></dt>
 <dd><p>Perform storage-specific local planning.</p>
 <p>While this method can produce a completely different plan, the recommended
 way is to store storage specific data in LoadPlan::storage_data.</p>
@@ -1055,7 +1055,7 @@ <h2>Additional resources:<a class="headerlink" href="#additional-resources" titl
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.checkpoint.StorageReader.read_data">
-<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">read_data</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">plan</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">planner</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/storage.html#StorageReader.read_data"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/checkpoint/storage.py#L255"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.StorageReader.read_data" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">read_data</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">plan</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">planner</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/storage.html#StorageReader.read_data"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/checkpoint/storage.py#L255"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.StorageReader.read_data" title="Permalink to this definition">¶</a></dt>
 <dd><p>Read all items from <code class="docutils literal notranslate"><span class="pre">plan</span></code> using <code class="docutils literal notranslate"><span class="pre">planner</span></code> to resolve the data.</p>
 <p>A subclass should call <code class="docutils literal notranslate"><span class="pre">LoadPlanner::load_bytes</span></code> to deserialize a BytesIO
 object into the right place.</p>
@@ -1081,7 +1081,7 @@ <h2>Additional resources:<a class="headerlink" href="#additional-resources" titl
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.checkpoint.StorageReader.read_metadata">
-<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">read_metadata</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/storage.html#StorageReader.read_metadata"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/checkpoint/storage.py#L202"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.StorageReader.read_metadata" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">read_metadata</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/storage.html#StorageReader.read_metadata"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/checkpoint/storage.py#L202"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.StorageReader.read_metadata" title="Permalink to this definition">¶</a></dt>
 <dd><p>Read the checkpoint metadata.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Returns</dt>
@@ -1095,7 +1095,7 @@ <h2>Additional resources:<a class="headerlink" href="#additional-resources" titl
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.checkpoint.StorageReader.reset">
-<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">reset</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">checkpoint_id</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/storage.html#StorageReader.reset"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/checkpoint/storage.py#L184"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.StorageReader.reset" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">reset</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">checkpoint_id</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/storage.html#StorageReader.reset"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/checkpoint/storage.py#L184"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.StorageReader.reset" title="Permalink to this definition">¶</a></dt>
 <dd><p>Calls to indicates a brand new checkpoint read is going to happen.
 A checkpoint_id may be present if users set the checkpoint_id for
 this checkpoint read. The meaning of the checkpiont_id is
@@ -1113,7 +1113,7 @@ <h2>Additional resources:<a class="headerlink" href="#additional-resources" titl
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.checkpoint.StorageReader.set_up_storage_reader">
-<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">set_up_storage_reader</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">metadata</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">is_coordinator</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/storage.html#StorageReader.set_up_storage_reader"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/checkpoint/storage.py#L212"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.StorageReader.set_up_storage_reader" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">set_up_storage_reader</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">metadata</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">is_coordinator</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/storage.html#StorageReader.set_up_storage_reader"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/checkpoint/storage.py#L212"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.StorageReader.set_up_storage_reader" title="Permalink to this definition">¶</a></dt>
 <dd><p>Initialize this instance.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -1128,7 +1128,7 @@ <h2>Additional resources:<a class="headerlink" href="#additional-resources" titl
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.checkpoint.StorageReader.validate_checkpoint_id">
-<em class="property"><span class="pre">abstract</span><span class="w"> </span><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">validate_checkpoint_id</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">checkpoint_id</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/storage.html#StorageReader.validate_checkpoint_id"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/checkpoint/storage.py#L277"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.StorageReader.validate_checkpoint_id" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">abstract</span><span class="w"> </span><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">validate_checkpoint_id</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">checkpoint_id</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/storage.html#StorageReader.validate_checkpoint_id"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/checkpoint/storage.py#L277"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.StorageReader.validate_checkpoint_id" title="Permalink to this definition">¶</a></dt>
 <dd><p>Check if the given checkpoint_id is supported by the stroage. This allow
 us to enable automatic storage selection.</p>
 <dl class="field-list simple">
@@ -1142,7 +1142,7 @@ <h2>Additional resources:<a class="headerlink" href="#additional-resources" titl
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributed.checkpoint.StorageWriter">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.checkpoint.</span></span><span class="sig-name descname"><span class="pre">StorageWriter</span></span><a class="reference internal" href="_modules/torch/distributed/checkpoint/storage.html#StorageWriter"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/checkpoint/storage.py#L27"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.StorageWriter" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.checkpoint.</span></span><span class="sig-name descname"><span class="pre">StorageWriter</span></span><a class="reference internal" href="_modules/torch/distributed/checkpoint/storage.html#StorageWriter"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/checkpoint/storage.py#L27"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.StorageWriter" title="Permalink to this definition">¶</a></dt>
 <dd><p>Interface used by <code class="docutils literal notranslate"><span class="pre">save_state_dict</span></code> to write to storage.</p>
 <p>One StorageWriter instance acts as both the coordinator and the follower
 in a distributed checkpoint. As part of initialization, each instance
@@ -1158,7 +1158,7 @@ <h2>Additional resources:<a class="headerlink" href="#additional-resources" titl
 </ol>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.checkpoint.StorageWriter.finish">
-<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">finish</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">metadata</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">results</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/storage.html#StorageWriter.finish"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/checkpoint/storage.py#L129"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.StorageWriter.finish" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">finish</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">metadata</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">results</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/storage.html#StorageWriter.finish"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/checkpoint/storage.py#L129"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.StorageWriter.finish" title="Permalink to this definition">¶</a></dt>
 <dd><p>Write the metadata and marks the current checkpoint as successful.</p>
 <p>The actual format/schema used for serializing <cite>metadata</cite> is an
 implementation detail. The only requirement is that it’s recoverable
@@ -1181,7 +1181,7 @@ <h2>Additional resources:<a class="headerlink" href="#additional-resources" titl
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.checkpoint.StorageWriter.prepare_global_plan">
-<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">prepare_global_plan</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">plans</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/storage.html#StorageWriter.prepare_global_plan"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/checkpoint/storage.py#L88"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.StorageWriter.prepare_global_plan" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">prepare_global_plan</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">plans</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/storage.html#StorageWriter.prepare_global_plan"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/checkpoint/storage.py#L88"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.StorageWriter.prepare_global_plan" title="Permalink to this definition">¶</a></dt>
 <dd><p>Perform centralized planning of storage.</p>
 <p>This method is only called on the coordinator instance.</p>
 <p>While this method can produce a completely different plan, the preferred
@@ -1201,7 +1201,7 @@ <h2>Additional resources:<a class="headerlink" href="#additional-resources" titl
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.checkpoint.StorageWriter.prepare_local_plan">
-<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">prepare_local_plan</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">plan</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/storage.html#StorageWriter.prepare_local_plan"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/checkpoint/storage.py#L73"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.StorageWriter.prepare_local_plan" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">prepare_local_plan</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">plan</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/storage.html#StorageWriter.prepare_local_plan"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/checkpoint/storage.py#L73"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.StorageWriter.prepare_local_plan" title="Permalink to this definition">¶</a></dt>
 <dd><p>Perform storage-specific local planning.</p>
 <p>While this method can produce a completely different plan, the recommended
 way is to store storage specific data in SavePlan::storage_data.</p>
@@ -1220,7 +1220,7 @@ <h2>Additional resources:<a class="headerlink" href="#additional-resources" titl
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.checkpoint.StorageWriter.reset">
-<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">reset</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">checkpoint_id</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/storage.html#StorageWriter.reset"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/checkpoint/storage.py#L45"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.StorageWriter.reset" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">reset</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">checkpoint_id</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/storage.html#StorageWriter.reset"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/checkpoint/storage.py#L45"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.StorageWriter.reset" title="Permalink to this definition">¶</a></dt>
 <dd><p>Calls to indicates a brand new checkpoint write is going to happen.
 A checkpoint_id may be present if users set the checkpoint_id for
 this checkpoint write. The meaning of the checkpiont_id is
@@ -1238,7 +1238,7 @@ <h2>Additional resources:<a class="headerlink" href="#additional-resources" titl
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.checkpoint.StorageWriter.set_up_storage_writer">
-<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">set_up_storage_writer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">is_coordinator</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/storage.html#StorageWriter.set_up_storage_writer"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/checkpoint/storage.py#L63"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.StorageWriter.set_up_storage_writer" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">set_up_storage_writer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">is_coordinator</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/storage.html#StorageWriter.set_up_storage_writer"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/checkpoint/storage.py#L63"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.StorageWriter.set_up_storage_writer" title="Permalink to this definition">¶</a></dt>
 <dd><p>Initialize this instance.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -1250,7 +1250,7 @@ <h2>Additional resources:<a class="headerlink" href="#additional-resources" titl
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.checkpoint.StorageWriter.storage_meta">
-<span class="sig-name descname"><span class="pre">storage_meta</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/storage.html#StorageWriter.storage_meta"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/checkpoint/storage.py#L155"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.StorageWriter.storage_meta" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">storage_meta</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/storage.html#StorageWriter.storage_meta"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/checkpoint/storage.py#L155"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.StorageWriter.storage_meta" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the storage-specific metadata. This is used to store additional information
 in a checkpoint that can be useful for providing request-level observability. StorageMeta
 is passed to the <code class="docutils literal notranslate"><span class="pre">SavePlanner</span></code> during save calls. Returns None by default.</p>
@@ -1264,7 +1264,7 @@ <h2>Additional resources:<a class="headerlink" href="#additional-resources" titl
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.checkpoint.StorageWriter.validate_checkpoint_id">
-<em class="property"><span class="pre">abstract</span><span class="w"> </span><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">validate_checkpoint_id</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">checkpoint_id</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/storage.html#StorageWriter.validate_checkpoint_id"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/checkpoint/storage.py#L146"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.StorageWriter.validate_checkpoint_id" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">abstract</span><span class="w"> </span><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">validate_checkpoint_id</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">checkpoint_id</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/storage.html#StorageWriter.validate_checkpoint_id"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/checkpoint/storage.py#L146"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.StorageWriter.validate_checkpoint_id" title="Permalink to this definition">¶</a></dt>
 <dd><p>Check if the given checkpoint_id is supported by the stroage. This allow
 us to enable automatic storage selection.</p>
 <dl class="field-list simple">
@@ -1276,7 +1276,7 @@ <h2>Additional resources:<a class="headerlink" href="#additional-resources" titl
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.checkpoint.StorageWriter.write_data">
-<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">write_data</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">plan</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">planner</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/storage.html#StorageWriter.write_data"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/checkpoint/storage.py#L105"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.StorageWriter.write_data" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">write_data</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">plan</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">planner</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/storage.html#StorageWriter.write_data"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/checkpoint/storage.py#L105"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.StorageWriter.write_data" title="Permalink to this definition">¶</a></dt>
 <dd><p>Write all items from <code class="docutils literal notranslate"><span class="pre">plan</span></code> using <code class="docutils literal notranslate"><span class="pre">planner</span></code> to resolve the data.</p>
 <p>A subclass should call <code class="docutils literal notranslate"><span class="pre">SavePlanner::resolve_data</span></code> on each item
 from the plan to get access to the underlying object to write.</p>
@@ -1307,7 +1307,7 @@ <h2>Additional resources:<a class="headerlink" href="#additional-resources" titl
 <p>The following types define the planner interface used during checkpoint:</p>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributed.checkpoint.LoadPlanner">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.checkpoint.</span></span><span class="sig-name descname"><span class="pre">LoadPlanner</span></span><a class="reference internal" href="_modules/torch/distributed/checkpoint/planner.html#LoadPlanner"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/checkpoint/planner.py#L271"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.LoadPlanner" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.checkpoint.</span></span><span class="sig-name descname"><span class="pre">LoadPlanner</span></span><a class="reference internal" href="_modules/torch/distributed/checkpoint/planner.html#LoadPlanner"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/checkpoint/planner.py#L271"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.LoadPlanner" title="Permalink to this definition">¶</a></dt>
 <dd><p>Abstract class defining the protocol used by load_state_dict to plan the load process.</p>
 <p>LoadPlanner are stateful objects that can be used to customize the whole load process.</p>
 <p>LoadPlanner acts as an access proxy to the state_dict, so any transformation done to it
@@ -1384,7 +1384,7 @@ <h2>Additional resources:<a class="headerlink" href="#additional-resources" titl
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.checkpoint.LoadPlanner.commit_tensor">
-<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">commit_tensor</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">read_item</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tensor</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/planner.html#LoadPlanner.commit_tensor"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/checkpoint/planner.py#L407"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.LoadPlanner.commit_tensor" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">commit_tensor</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">read_item</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tensor</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/planner.html#LoadPlanner.commit_tensor"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/checkpoint/planner.py#L407"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.LoadPlanner.commit_tensor" title="Permalink to this definition">¶</a></dt>
 <dd><p>Call once the StorageReader finished loading data into <code class="docutils literal notranslate"><span class="pre">tensor</span></code>.</p>
 <p>The provided tensor is the same one returned by the call to <code class="docutils literal notranslate"><span class="pre">resolve_tensor</span></code>.
 This method is only needed if this LoadPlanner needs to post process <code class="docutils literal notranslate"><span class="pre">tensor</span></code> prior to
@@ -1396,7 +1396,7 @@ <h2>Additional resources:<a class="headerlink" href="#additional-resources" titl
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.checkpoint.LoadPlanner.create_global_plan">
-<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">create_global_plan</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">global_plan</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/planner.html#LoadPlanner.create_global_plan"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/checkpoint/planner.py#L366"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.LoadPlanner.create_global_plan" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">create_global_plan</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">global_plan</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/planner.html#LoadPlanner.create_global_plan"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/checkpoint/planner.py#L366"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.LoadPlanner.create_global_plan" title="Permalink to this definition">¶</a></dt>
 <dd><p>Compute the global load plan and return plans for each rank.</p>
 <p>. N.B. This is called on the coordinator rank only</p>
 <dl class="field-list simple">
@@ -1408,7 +1408,7 @@ <h2>Additional resources:<a class="headerlink" href="#additional-resources" titl
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.checkpoint.LoadPlanner.create_local_plan">
-<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">create_local_plan</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/planner.html#LoadPlanner.create_local_plan"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/checkpoint/planner.py#L358"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.LoadPlanner.create_local_plan" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">create_local_plan</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/planner.html#LoadPlanner.create_local_plan"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/checkpoint/planner.py#L358"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.LoadPlanner.create_local_plan" title="Permalink to this definition">¶</a></dt>
 <dd><p>Create a LoadPlan based on state_dict and metadata provided by set_up_planner.</p>
 <p>. N.B. This is called on every rank.</p>
 <dl class="field-list simple">
@@ -1420,7 +1420,7 @@ <h2>Additional resources:<a class="headerlink" href="#additional-resources" titl
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.checkpoint.LoadPlanner.finish_plan">
-<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">finish_plan</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">central_plan</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/planner.html#LoadPlanner.finish_plan"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/checkpoint/planner.py#L374"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.LoadPlanner.finish_plan" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">finish_plan</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">central_plan</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/planner.html#LoadPlanner.finish_plan"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/checkpoint/planner.py#L374"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.LoadPlanner.finish_plan" title="Permalink to this definition">¶</a></dt>
 <dd><p>Accept the plan from coordinator and return final LoadPlan.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -1431,7 +1431,7 @@ <h2>Additional resources:<a class="headerlink" href="#additional-resources" titl
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.checkpoint.LoadPlanner.load_bytes">
-<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">load_bytes</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">read_item</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/planner.html#LoadPlanner.load_bytes"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/checkpoint/planner.py#L378"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.LoadPlanner.load_bytes" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">load_bytes</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">read_item</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/planner.html#LoadPlanner.load_bytes"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/checkpoint/planner.py#L378"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.LoadPlanner.load_bytes" title="Permalink to this definition">¶</a></dt>
 <dd><p>Load the item described by <code class="docutils literal notranslate"><span class="pre">read_item``and</span> <span class="pre">``value</span></code>.</p>
 <p>This method is expected to modify in-place the underlying state_dict.</p>
 <p>The contents of <code class="docutils literal notranslate"><span class="pre">value</span></code> are defined by the SavePlanner used to produce
@@ -1442,7 +1442,7 @@ <h2>Additional resources:<a class="headerlink" href="#additional-resources" titl
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.checkpoint.LoadPlanner.resolve_bytes">
-<span class="sig-name descname"><span class="pre">resolve_bytes</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">read_item</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/planner.html#LoadPlanner.resolve_bytes"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/checkpoint/planner.py#L389"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.LoadPlanner.resolve_bytes" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">resolve_bytes</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">read_item</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/planner.html#LoadPlanner.resolve_bytes"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/checkpoint/planner.py#L389"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.LoadPlanner.resolve_bytes" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the BytesIO to be used by the StorageReader to load <cite>read_item</cite>.</p>
 <p>The BytesIO should alias with one on the underlying state_dict as StorageReader will replace its contents.</p>
 <dl class="field-list simple">
@@ -1454,7 +1454,7 @@ <h2>Additional resources:<a class="headerlink" href="#additional-resources" titl
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.checkpoint.LoadPlanner.resolve_tensor">
-<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">resolve_tensor</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">read_item</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/planner.html#LoadPlanner.resolve_tensor"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/checkpoint/planner.py#L397"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.LoadPlanner.resolve_tensor" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">resolve_tensor</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">read_item</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/planner.html#LoadPlanner.resolve_tensor"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/checkpoint/planner.py#L397"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.LoadPlanner.resolve_tensor" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the tensor described by <code class="docutils literal notranslate"><span class="pre">read_item</span></code> to be used by the StorageReader to load <cite>read_item</cite>.</p>
 <p>The tensor should alias with one on the underlying state_dict as StorageReader will replace its contents.
 If, for any reason, that’s not possible, the planner can use the <code class="docutils literal notranslate"><span class="pre">commit_tensor</span></code> method to copy the data
@@ -1468,7 +1468,7 @@ <h2>Additional resources:<a class="headerlink" href="#additional-resources" titl
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.checkpoint.LoadPlanner.set_up_planner">
-<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">set_up_planner</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state_dict</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">metadata</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">is_coordinator</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/planner.html#LoadPlanner.set_up_planner"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/checkpoint/planner.py#L345"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.LoadPlanner.set_up_planner" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">set_up_planner</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state_dict</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">metadata</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">is_coordinator</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/planner.html#LoadPlanner.set_up_planner"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/checkpoint/planner.py#L345"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.LoadPlanner.set_up_planner" title="Permalink to this definition">¶</a></dt>
 <dd><p>Initialize this instance to load data into <code class="docutils literal notranslate"><span class="pre">state_dict</span></code>.</p>
 <p>. N.B. This is called on every rank.</p>
 <dl class="field-list simple">
@@ -1479,21 +1479,21 @@ <h2>Additional resources:<a class="headerlink" href="#additional-resources" titl
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributed.checkpoint.LoadPlan">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.checkpoint.</span></span><span class="sig-name descname"><span class="pre">LoadPlan</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">items</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.13)"><span class="pre">list</span></a><span class="p"><span class="pre">[</span></span><a class="reference internal" href="#torch.distributed.checkpoint.ReadItem" title="torch.distributed.checkpoint.planner.ReadItem"><span class="pre">torch.distributed.checkpoint.planner.ReadItem</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">storage_data</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Any</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">planner_data</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Any</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/planner.html#LoadPlan"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/checkpoint/planner.py#L102"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.LoadPlan" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.checkpoint.</span></span><span class="sig-name descname"><span class="pre">LoadPlan</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">items</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.13)"><span class="pre">list</span></a><span class="p"><span class="pre">[</span></span><a class="reference internal" href="#torch.distributed.checkpoint.ReadItem" title="torch.distributed.checkpoint.planner.ReadItem"><span class="pre">torch.distributed.checkpoint.planner.ReadItem</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">storage_data</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Any</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">planner_data</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Any</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/planner.html#LoadPlan"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/checkpoint/planner.py#L102"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.LoadPlan" title="Permalink to this definition">¶</a></dt>
 <dd><dl class="field-list simple">
 </dl>
 </dd></dl>
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributed.checkpoint.ReadItem">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.checkpoint.</span></span><span class="sig-name descname"><span class="pre">ReadItem</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">type</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.distributed.checkpoint.planner.LoadItemType</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dest_index</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.distributed.checkpoint.metadata.MetadataIndex</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dest_offsets</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="size.html#torch.Size" title="torch.Size"><span class="pre">torch.Size</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">storage_index</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.distributed.checkpoint.metadata.MetadataIndex</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">storage_offsets</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="size.html#torch.Size" title="torch.Size"><span class="pre">torch.Size</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">lengths</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="size.html#torch.Size" title="torch.Size"><span class="pre">torch.Size</span></a></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/planner.html#ReadItem"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/checkpoint/planner.py#L76"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.ReadItem" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.checkpoint.</span></span><span class="sig-name descname"><span class="pre">ReadItem</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">type</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.distributed.checkpoint.planner.LoadItemType</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dest_index</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.distributed.checkpoint.metadata.MetadataIndex</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dest_offsets</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="size.html#torch.Size" title="torch.Size"><span class="pre">torch.Size</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">storage_index</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.distributed.checkpoint.metadata.MetadataIndex</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">storage_offsets</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="size.html#torch.Size" title="torch.Size"><span class="pre">torch.Size</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">lengths</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="size.html#torch.Size" title="torch.Size"><span class="pre">torch.Size</span></a></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/planner.html#ReadItem"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/checkpoint/planner.py#L76"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.ReadItem" title="Permalink to this definition">¶</a></dt>
 <dd><dl class="field-list simple">
 </dl>
 </dd></dl>
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributed.checkpoint.SavePlanner">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.checkpoint.</span></span><span class="sig-name descname"><span class="pre">SavePlanner</span></span><a class="reference internal" href="_modules/torch/distributed/checkpoint/planner.html#SavePlanner"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/checkpoint/planner.py#L109"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.SavePlanner" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.checkpoint.</span></span><span class="sig-name descname"><span class="pre">SavePlanner</span></span><a class="reference internal" href="_modules/torch/distributed/checkpoint/planner.html#SavePlanner"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/checkpoint/planner.py#L109"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.SavePlanner" title="Permalink to this definition">¶</a></dt>
 <dd><p>Abstract class defining the protocol used by save_state_dict to plan the save process.</p>
 <p>SavePlanners are stateful objects that can be used to customize the whole save process.</p>
 <p>SavePlanner acts as an access proxy to the state_dict, so any transformation done to it
@@ -1592,7 +1592,7 @@ <h2>Additional resources:<a class="headerlink" href="#additional-resources" titl
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.checkpoint.SavePlanner.create_global_plan">
-<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">create_global_plan</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">all_plans</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/planner.html#SavePlanner.create_global_plan"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/checkpoint/planner.py#L232"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.SavePlanner.create_global_plan" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">create_global_plan</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">all_plans</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/planner.html#SavePlanner.create_global_plan"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/checkpoint/planner.py#L232"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.SavePlanner.create_global_plan" title="Permalink to this definition">¶</a></dt>
 <dd><p>Compute the global checkpoint plan and return the local plan of each rank.</p>
 <p>This is called on the coordinator rank only.</p>
 <dl class="field-list simple">
@@ -1604,7 +1604,7 @@ <h2>Additional resources:<a class="headerlink" href="#additional-resources" titl
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.checkpoint.SavePlanner.create_local_plan">
-<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">create_local_plan</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/planner.html#SavePlanner.create_local_plan"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/checkpoint/planner.py#L221"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.SavePlanner.create_local_plan" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">create_local_plan</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/planner.html#SavePlanner.create_local_plan"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/checkpoint/planner.py#L221"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.SavePlanner.create_local_plan" title="Permalink to this definition">¶</a></dt>
 <dd><p>Compute the save plan for the current rank.</p>
 <p>This will be aggregated and passed to create_global_plan.
 Planner specific data can be passed through SavePlan::planner_data.</p>
@@ -1618,7 +1618,7 @@ <h2>Additional resources:<a class="headerlink" href="#additional-resources" titl
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.checkpoint.SavePlanner.finish_plan">
-<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">finish_plan</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">new_plan</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/planner.html#SavePlanner.finish_plan"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/checkpoint/planner.py#L242"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.SavePlanner.finish_plan" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">finish_plan</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">new_plan</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/planner.html#SavePlanner.finish_plan"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/checkpoint/planner.py#L242"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.SavePlanner.finish_plan" title="Permalink to this definition">¶</a></dt>
 <dd><p>Merge the plan created by <cite>create_local_plan</cite> and the result of <cite>create_global_plan</cite>.</p>
 <p>This is called on all ranks.</p>
 <dl class="field-list simple">
@@ -1630,7 +1630,7 @@ <h2>Additional resources:<a class="headerlink" href="#additional-resources" titl
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.checkpoint.SavePlanner.resolve_data">
-<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">resolve_data</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">write_item</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/planner.html#SavePlanner.resolve_data"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/checkpoint/planner.py#L250"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.SavePlanner.resolve_data" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">resolve_data</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">write_item</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/planner.html#SavePlanner.resolve_data"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/checkpoint/planner.py#L250"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.SavePlanner.resolve_data" title="Permalink to this definition">¶</a></dt>
 <dd><p>Transform and prepare <code class="docutils literal notranslate"><span class="pre">write_item</span></code> from <code class="docutils literal notranslate"><span class="pre">state_dict</span></code> for storage, ensuring idempotency and thread-safety.</p>
 <p>Lookup the object associated with <code class="docutils literal notranslate"><span class="pre">write_item</span></code> in <code class="docutils literal notranslate"><span class="pre">state_dict</span></code> and apply any
 transformation (such as serialization) prior to the storage layer consuming it.</p>
@@ -1650,7 +1650,7 @@ <h2>Additional resources:<a class="headerlink" href="#additional-resources" titl
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.checkpoint.SavePlanner.set_up_planner">
-<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">set_up_planner</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state_dict</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">storage_meta</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">is_coordinator</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/planner.html#SavePlanner.set_up_planner"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/checkpoint/planner.py#L206"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.SavePlanner.set_up_planner" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">set_up_planner</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state_dict</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">storage_meta</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">is_coordinator</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/planner.html#SavePlanner.set_up_planner"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/checkpoint/planner.py#L206"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.SavePlanner.set_up_planner" title="Permalink to this definition">¶</a></dt>
 <dd><p>Initialize this planner to save <code class="docutils literal notranslate"><span class="pre">state_dict</span></code>.</p>
 <p>Implementations should save those values as they won’t be provided lated in the save process.</p>
 <p>This is called on all ranks.</p>
@@ -1662,20 +1662,20 @@ <h2>Additional resources:<a class="headerlink" href="#additional-resources" titl
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributed.checkpoint.SavePlan">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.checkpoint.</span></span><span class="sig-name descname"><span class="pre">SavePlan</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">items</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.13)"><span class="pre">list</span></a><span class="p"><span class="pre">[</span></span><a class="reference internal" href="#torch.distributed.checkpoint.planner.WriteItem" title="torch.distributed.checkpoint.planner.WriteItem"><span class="pre">torch.distributed.checkpoint.planner.WriteItem</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">storage_data</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Any</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">planner_data</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Any</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/planner.html#SavePlan"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/checkpoint/planner.py#L95"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.SavePlan" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.checkpoint.</span></span><span class="sig-name descname"><span class="pre">SavePlan</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">items</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.13)"><span class="pre">list</span></a><span class="p"><span class="pre">[</span></span><a class="reference internal" href="#torch.distributed.checkpoint.planner.WriteItem" title="torch.distributed.checkpoint.planner.WriteItem"><span class="pre">torch.distributed.checkpoint.planner.WriteItem</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">storage_data</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Any</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">planner_data</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Any</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/planner.html#SavePlan"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/checkpoint/planner.py#L95"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.SavePlan" title="Permalink to this definition">¶</a></dt>
 <dd><dl class="field-list simple">
 </dl>
 </dd></dl>
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributed.checkpoint.planner.WriteItem">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.checkpoint.planner.</span></span><span class="sig-name descname"><span class="pre">WriteItem</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">index</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">type</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tensor_data</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/planner.html#WriteItem"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/checkpoint/planner.py#L51"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.planner.WriteItem" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.checkpoint.planner.</span></span><span class="sig-name descname"><span class="pre">WriteItem</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">index</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">type</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tensor_data</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/planner.html#WriteItem"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/checkpoint/planner.py#L51"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.planner.WriteItem" title="Permalink to this definition">¶</a></dt>
 <dd><p>Dataclass which holds information about what needs to be written to storage.</p>
 <dl class="field-list simple">
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.checkpoint.planner.WriteItem.tensor_storage_size">
-<span class="sig-name descname"><span class="pre">tensor_storage_size</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/planner.html#WriteItem.tensor_storage_size"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/checkpoint/planner.py#L61"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.planner.WriteItem.tensor_storage_size" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">tensor_storage_size</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/planner.html#WriteItem.tensor_storage_size"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/checkpoint/planner.py#L61"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.planner.WriteItem.tensor_storage_size" title="Permalink to this definition">¶</a></dt>
 <dd><p>Calculates the storage size of the underlying tensor, or None if this is not a tensor write.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Returns</dt>
@@ -1692,7 +1692,7 @@ <h2>Additional resources:<a class="headerlink" href="#additional-resources" titl
 <p>We provide a filesystem based storage layer:</p>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributed.checkpoint.FileSystemReader">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.checkpoint.</span></span><span class="sig-name descname"><span class="pre">FileSystemReader</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">path</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_extension_registry</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/filesystem.html#FileSystemReader"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/checkpoint/filesystem.py#L719"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.FileSystemReader" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.checkpoint.</span></span><span class="sig-name descname"><span class="pre">FileSystemReader</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">path</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_extension_registry</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/filesystem.html#FileSystemReader"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/checkpoint/filesystem.py#L719"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.FileSystemReader" title="Permalink to this definition">¶</a></dt>
 <dd><dl class="field-list simple">
 </dl>
 <dl class="py property">
@@ -1705,7 +1705,7 @@ <h2>Additional resources:<a class="headerlink" href="#additional-resources" titl
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributed.checkpoint.FileSystemWriter">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.checkpoint.</span></span><span class="sig-name descname"><span class="pre">FileSystemWriter</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">path</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">single_file_per_rank</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sync_files</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">thread_count</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">per_thread_copy_ahead</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10000000</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cache_staged_state_dict</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">overwrite</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_extensions</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/filesystem.html#FileSystemWriter"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/checkpoint/filesystem.py#L834"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.FileSystemWriter" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.checkpoint.</span></span><span class="sig-name descname"><span class="pre">FileSystemWriter</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">path</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">single_file_per_rank</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sync_files</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">thread_count</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">per_thread_copy_ahead</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10000000</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cache_staged_state_dict</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">overwrite</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_extensions</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/filesystem.html#FileSystemWriter"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/checkpoint/filesystem.py#L834"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.FileSystemWriter" title="Permalink to this definition">¶</a></dt>
 <dd><p>Basic implementation of StorageWriter using file IO.</p>
 <p>This implementation makes the following assumptions and simplifications:</p>
 <ul class="simple">
@@ -1718,7 +1718,7 @@ <h2>Additional resources:<a class="headerlink" href="#additional-resources" titl
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.checkpoint.FileSystemWriter.stage">
-<span class="sig-name descname"><span class="pre">stage</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state_dict</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/filesystem.html#FileSystemWriter.stage"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/checkpoint/filesystem.py#L891"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.FileSystemWriter.stage" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">stage</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state_dict</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/filesystem.html#FileSystemWriter.stage"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/checkpoint/filesystem.py#L891"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.FileSystemWriter.stage" title="Permalink to this definition">¶</a></dt>
 <dd><p>Override of AsyncStager.stage</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -1733,12 +1733,12 @@ <h2>Additional resources:<a class="headerlink" href="#additional-resources" titl
 can handle all of torch.distributed constructs such as FSDP, DDP, ShardedTensor and DistributedTensor.</p>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributed.checkpoint.DefaultSavePlanner">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.checkpoint.</span></span><span class="sig-name descname"><span class="pre">DefaultSavePlanner</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">flatten_state_dict</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">flatten_sharded_tensors</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dedup_replicated_tensors</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dedup_save_to_lowest_rank</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/default_planner.html#DefaultSavePlanner"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/checkpoint/default_planner.py#L66"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.DefaultSavePlanner" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.checkpoint.</span></span><span class="sig-name descname"><span class="pre">DefaultSavePlanner</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">flatten_state_dict</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">flatten_sharded_tensors</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dedup_replicated_tensors</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dedup_save_to_lowest_rank</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/default_planner.html#DefaultSavePlanner"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/checkpoint/default_planner.py#L66"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.DefaultSavePlanner" title="Permalink to this definition">¶</a></dt>
 <dd><dl class="field-list simple">
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.checkpoint.DefaultSavePlanner.lookup_object">
-<span class="sig-name descname"><span class="pre">lookup_object</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">index</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/default_planner.html#DefaultSavePlanner.lookup_object"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/checkpoint/default_planner.py#L140"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.DefaultSavePlanner.lookup_object" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">lookup_object</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">index</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/default_planner.html#DefaultSavePlanner.lookup_object"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/checkpoint/default_planner.py#L140"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.DefaultSavePlanner.lookup_object" title="Permalink to this definition">¶</a></dt>
 <dd><p>Extension from the planner interface to make it easy to extend the default planner.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -1749,7 +1749,7 @@ <h2>Additional resources:<a class="headerlink" href="#additional-resources" titl
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.checkpoint.DefaultSavePlanner.transform_object">
-<span class="sig-name descname"><span class="pre">transform_object</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">write_item</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">object</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/default_planner.html#DefaultSavePlanner.transform_object"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/checkpoint/default_planner.py#L144"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.DefaultSavePlanner.transform_object" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">transform_object</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">write_item</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">object</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/default_planner.html#DefaultSavePlanner.transform_object"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/checkpoint/default_planner.py#L144"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.DefaultSavePlanner.transform_object" title="Permalink to this definition">¶</a></dt>
 <dd><p>Extension from the planner interface to make it easy to extend the default planner.</p>
 <dl class="field-list simple">
 </dl>
@@ -1759,7 +1759,7 @@ <h2>Additional resources:<a class="headerlink" href="#additional-resources" titl
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributed.checkpoint.DefaultLoadPlanner">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.checkpoint.</span></span><span class="sig-name descname"><span class="pre">DefaultLoadPlanner</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">flatten_state_dict</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">flatten_sharded_tensors</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">allow_partial_load</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/default_planner.html#DefaultLoadPlanner"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/checkpoint/default_planner.py#L153"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.DefaultLoadPlanner" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.checkpoint.</span></span><span class="sig-name descname"><span class="pre">DefaultLoadPlanner</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">flatten_state_dict</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">flatten_sharded_tensors</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">allow_partial_load</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/default_planner.html#DefaultLoadPlanner"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/checkpoint/default_planner.py#L153"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.DefaultLoadPlanner" title="Permalink to this definition">¶</a></dt>
 <dd><p>DefaultLoadPlanner that adds multiple features on top of LoadPlanner.</p>
 <p>In particular it adds the following:</p>
 <p>flatten_state_dict: Handle state_dict with nested dicts
@@ -1769,7 +1769,7 @@ <h2>Additional resources:<a class="headerlink" href="#additional-resources" titl
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.checkpoint.DefaultLoadPlanner.lookup_tensor">
-<span class="sig-name descname"><span class="pre">lookup_tensor</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">index</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/default_planner.html#DefaultLoadPlanner.lookup_tensor"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/checkpoint/default_planner.py#L262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.DefaultLoadPlanner.lookup_tensor" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">lookup_tensor</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">index</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/default_planner.html#DefaultLoadPlanner.lookup_tensor"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/checkpoint/default_planner.py#L262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.DefaultLoadPlanner.lookup_tensor" title="Permalink to this definition">¶</a></dt>
 <dd><p>Extension from the planner interface to make it easy to extend the default planner.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -1780,7 +1780,7 @@ <h2>Additional resources:<a class="headerlink" href="#additional-resources" titl
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.checkpoint.DefaultLoadPlanner.transform_tensor">
-<span class="sig-name descname"><span class="pre">transform_tensor</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">read_item</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tensor</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/default_planner.html#DefaultLoadPlanner.transform_tensor"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/checkpoint/default_planner.py#L266"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.DefaultLoadPlanner.transform_tensor" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">transform_tensor</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">read_item</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tensor</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/default_planner.html#DefaultLoadPlanner.transform_tensor"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/checkpoint/default_planner.py#L266"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.DefaultLoadPlanner.transform_tensor" title="Permalink to this definition">¶</a></dt>
 <dd><p>Extension from the planner interface to make it easy to extend the default planner.</p>
 <dl class="field-list simple">
 </dl>
@@ -1794,7 +1794,7 @@ <h2>Additional resources:<a class="headerlink" href="#additional-resources" titl
 <p>Note that this feature is experimental, and API signatures might change in the future.</p>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.distributed.checkpoint.state_dict.get_state_dict">
-<span class="sig-prename descclassname"><span class="pre">torch.distributed.checkpoint.state_dict.</span></span><span class="sig-name descname"><span class="pre">get_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">optimizers</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">submodules</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">options</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/state_dict.html#get_state_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/checkpoint/state_dict.py#L1049"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.state_dict.get_state_dict" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributed.checkpoint.state_dict.</span></span><span class="sig-name descname"><span class="pre">get_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">optimizers</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">submodules</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">options</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/state_dict.html#get_state_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/checkpoint/state_dict.py#L1049"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.state_dict.get_state_dict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the model state_dict and optimizers state_dict.</p>
 <p><code class="docutils literal notranslate"><span class="pre">get_state_dict</span></code> can process any module that is parallelized by PyTorch
 FSDP/fully_shard, DDP/replicate, tensor_parallel/parallelize_module, and any
@@ -1861,7 +1861,7 @@ <h2>Additional resources:<a class="headerlink" href="#additional-resources" titl
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.distributed.checkpoint.state_dict.get_model_state_dict">
-<span class="sig-prename descclassname"><span class="pre">torch.distributed.checkpoint.state_dict.</span></span><span class="sig-name descname"><span class="pre">get_model_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">submodules</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">options</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/state_dict.html#get_model_state_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/checkpoint/state_dict.py#L967"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.state_dict.get_model_state_dict" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributed.checkpoint.state_dict.</span></span><span class="sig-name descname"><span class="pre">get_model_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">submodules</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">options</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/state_dict.html#get_model_state_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/checkpoint/state_dict.py#L967"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.state_dict.get_model_state_dict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the model state_dict of <code class="docutils literal notranslate"><span class="pre">model</span></code>.</p>
 <p>See <code class="docutils literal notranslate"><span class="pre">get_state_dict</span></code> for the detail usage.</p>
 <dl class="field-list simple">
@@ -1886,7 +1886,7 @@ <h2>Additional resources:<a class="headerlink" href="#additional-resources" titl
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.distributed.checkpoint.state_dict.get_optimizer_state_dict">
-<span class="sig-prename descclassname"><span class="pre">torch.distributed.checkpoint.state_dict.</span></span><span class="sig-name descname"><span class="pre">get_optimizer_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">optimizers</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">submodules</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">options</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/state_dict.html#get_optimizer_state_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/checkpoint/state_dict.py#L1004"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.state_dict.get_optimizer_state_dict" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributed.checkpoint.state_dict.</span></span><span class="sig-name descname"><span class="pre">get_optimizer_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">optimizers</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">submodules</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">options</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/state_dict.html#get_optimizer_state_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/checkpoint/state_dict.py#L1004"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.state_dict.get_optimizer_state_dict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the combined state_dict for optimizers.</p>
 <p>See <code class="docutils literal notranslate"><span class="pre">get_state_dict</span></code> for the detail usage.</p>
 <dl class="field-list simple">
@@ -1912,7 +1912,7 @@ <h2>Additional resources:<a class="headerlink" href="#additional-resources" titl
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.distributed.checkpoint.state_dict.set_state_dict">
-<span class="sig-prename descclassname"><span class="pre">torch.distributed.checkpoint.state_dict.</span></span><span class="sig-name descname"><span class="pre">set_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">optimizers</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">model_state_dict</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">optim_state_dict</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">options</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/state_dict.html#set_state_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/checkpoint/state_dict.py#L1249"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.state_dict.set_state_dict" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributed.checkpoint.state_dict.</span></span><span class="sig-name descname"><span class="pre">set_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">optimizers</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">model_state_dict</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">optim_state_dict</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">options</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/state_dict.html#set_state_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/checkpoint/state_dict.py#L1249"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.state_dict.set_state_dict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Load the model state_dict and optimizers state_dict.</p>
 <p>The counterpart of <code class="docutils literal notranslate"><span class="pre">get_state_dict</span></code> to set the state_dict to the model and
 optimizers.  The given <code class="docutils literal notranslate"><span class="pre">model_state_dict</span></code> and <code class="docutils literal notranslate"><span class="pre">optim_state_dict</span></code> do not
@@ -1953,7 +1953,7 @@ <h2>Additional resources:<a class="headerlink" href="#additional-resources" titl
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.distributed.checkpoint.state_dict.set_model_state_dict">
-<span class="sig-prename descclassname"><span class="pre">torch.distributed.checkpoint.state_dict.</span></span><span class="sig-name descname"><span class="pre">set_model_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">model_state_dict</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">options</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/state_dict.html#set_model_state_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/checkpoint/state_dict.py#L1171"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.state_dict.set_model_state_dict" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributed.checkpoint.state_dict.</span></span><span class="sig-name descname"><span class="pre">set_model_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">model_state_dict</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">options</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/state_dict.html#set_model_state_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/checkpoint/state_dict.py#L1171"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.state_dict.set_model_state_dict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Load the model state_dict.</p>
 <p>The counterpart of <code class="docutils literal notranslate"><span class="pre">get_model_state_dict</span></code> to set the state_dict to the
 model. See <code class="docutils literal notranslate"><span class="pre">set_state_dict</span></code> for the detail usage.</p>
@@ -1986,7 +1986,7 @@ <h2>Additional resources:<a class="headerlink" href="#additional-resources" titl
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.distributed.checkpoint.state_dict.set_optimizer_state_dict">
-<span class="sig-prename descclassname"><span class="pre">torch.distributed.checkpoint.state_dict.</span></span><span class="sig-name descname"><span class="pre">set_optimizer_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">optimizers</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">optim_state_dict</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">options</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/state_dict.html#set_optimizer_state_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/checkpoint/state_dict.py#L1210"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.state_dict.set_optimizer_state_dict" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributed.checkpoint.state_dict.</span></span><span class="sig-name descname"><span class="pre">set_optimizer_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">optimizers</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">optim_state_dict</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">options</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/state_dict.html#set_optimizer_state_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/checkpoint/state_dict.py#L1210"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.state_dict.set_optimizer_state_dict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Load the optimizers state_dict.</p>
 <p>The counterpart of <code class="docutils literal notranslate"><span class="pre">get_optimizer_state_dict</span></code> to set the state_dict to the
 optimizers. See <code class="docutils literal notranslate"><span class="pre">set_state_dict</span></code> for the detail usage.</p>
@@ -2013,7 +2013,7 @@ <h2>Additional resources:<a class="headerlink" href="#additional-resources" titl
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributed.checkpoint.state_dict.StateDictOptions">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.checkpoint.state_dict.</span></span><span class="sig-name descname"><span class="pre">StateDictOptions</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">full_state_dict</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cpu_offload</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ignore_frozen_params</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">keep_submodule_prefixes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">strict</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">broadcast_from_rank0</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">flatten_optimizer_state_dict</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/state_dict.html#StateDictOptions"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/checkpoint/state_dict.py#L92"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.state_dict.StateDictOptions" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.checkpoint.state_dict.</span></span><span class="sig-name descname"><span class="pre">StateDictOptions</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">full_state_dict</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cpu_offload</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ignore_frozen_params</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">keep_submodule_prefixes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">strict</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">broadcast_from_rank0</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">flatten_optimizer_state_dict</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/state_dict.html#StateDictOptions"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/checkpoint/state_dict.py#L92"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.state_dict.StateDictOptions" title="Permalink to this definition">¶</a></dt>
 <dd><p>This dataclass specifies how get_state_dict/set_state_dict will work.</p>
 <ul class="simple">
 <li><p><code class="docutils literal notranslate"><span class="pre">full_state_dict</span></code>: if this is set to True, all the tensors in the
@@ -2053,7 +2053,7 @@ <h2>Additional resources:<a class="headerlink" href="#additional-resources" titl
 <p>For users which are used to using and sharing models in the <cite>torch.save</cite> format, the following methods are provided which provide offline utilities for converting betweeing formats.</p>
 <span class="target" id="module-torch.distributed.checkpoint.format_utils"></span><dl class="py function">
 <dt class="sig sig-object py" id="torch.distributed.checkpoint.format_utils.dcp_to_torch_save">
-<span class="sig-prename descclassname"><span class="pre">torch.distributed.checkpoint.format_utils.</span></span><span class="sig-name descname"><span class="pre">dcp_to_torch_save</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dcp_checkpoint_dir</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">torch_save_path</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/format_utils.html#dcp_to_torch_save"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/checkpoint/format_utils.py#L196"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.format_utils.dcp_to_torch_save" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributed.checkpoint.format_utils.</span></span><span class="sig-name descname"><span class="pre">dcp_to_torch_save</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dcp_checkpoint_dir</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">torch_save_path</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/format_utils.html#dcp_to_torch_save"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/checkpoint/format_utils.py#L196"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.format_utils.dcp_to_torch_save" title="Permalink to this definition">¶</a></dt>
 <dd><p>Given a directory containing a DCP checkpoint, this function will convert it into a
 Torch save file.</p>
 <dl class="field-list simple">
@@ -2072,7 +2072,7 @@ <h2>Additional resources:<a class="headerlink" href="#additional-resources" titl
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.distributed.checkpoint.format_utils.torch_save_to_dcp">
-<span class="sig-prename descclassname"><span class="pre">torch.distributed.checkpoint.format_utils.</span></span><span class="sig-name descname"><span class="pre">torch_save_to_dcp</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">torch_save_path</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dcp_checkpoint_dir</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/format_utils.html#torch_save_to_dcp"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/checkpoint/format_utils.py#L221"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.format_utils.torch_save_to_dcp" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributed.checkpoint.format_utils.</span></span><span class="sig-name descname"><span class="pre">torch_save_to_dcp</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">torch_save_path</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dcp_checkpoint_dir</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/format_utils.html#torch_save_to_dcp"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/checkpoint/format_utils.py#L221"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.format_utils.torch_save_to_dcp" title="Permalink to this definition">¶</a></dt>
 <dd><p>Given the location of a torch save file, converts it into a DCP checkpoint.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -2091,7 +2091,7 @@ <h2>Additional resources:<a class="headerlink" href="#additional-resources" titl
 <p>The following classes can also be utilized for online loading and resharding of models from the torch.save format.</p>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributed.checkpoint.format_utils.BroadcastingTorchSaveReader">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.checkpoint.format_utils.</span></span><span class="sig-name descname"><span class="pre">BroadcastingTorchSaveReader</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">checkpoint_id</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">coordinator_rank</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/format_utils.html#BroadcastingTorchSaveReader"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/checkpoint/format_utils.py#L39"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.format_utils.BroadcastingTorchSaveReader" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.checkpoint.format_utils.</span></span><span class="sig-name descname"><span class="pre">BroadcastingTorchSaveReader</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">checkpoint_id</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">coordinator_rank</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/format_utils.html#BroadcastingTorchSaveReader"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/checkpoint/format_utils.py#L39"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.format_utils.BroadcastingTorchSaveReader" title="Permalink to this definition">¶</a></dt>
 <dd><p>StorageReader for reading a Torch Save file. This reader will read the entire checkpoint
 on the coordinator rank, and then broadcast and shard each tensor to all ranks.</p>
 <p>. N.B. Intended to be used with DynamicMetaLoadPlanner</p>
@@ -2112,7 +2112,7 @@ <h2>Additional resources:<a class="headerlink" href="#additional-resources" titl
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.checkpoint.format_utils.BroadcastingTorchSaveReader.prepare_global_plan">
-<span class="sig-name descname"><span class="pre">prepare_global_plan</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">global_plan</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/format_utils.html#BroadcastingTorchSaveReader.prepare_global_plan"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/checkpoint/format_utils.py#L136"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.format_utils.BroadcastingTorchSaveReader.prepare_global_plan" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">prepare_global_plan</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">global_plan</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/format_utils.html#BroadcastingTorchSaveReader.prepare_global_plan"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/checkpoint/format_utils.py#L136"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.format_utils.BroadcastingTorchSaveReader.prepare_global_plan" title="Permalink to this definition">¶</a></dt>
 <dd><p>Implementation of the StorageReader method</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -2123,7 +2123,7 @@ <h2>Additional resources:<a class="headerlink" href="#additional-resources" titl
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.checkpoint.format_utils.BroadcastingTorchSaveReader.prepare_local_plan">
-<span class="sig-name descname"><span class="pre">prepare_local_plan</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">plan</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/format_utils.html#BroadcastingTorchSaveReader.prepare_local_plan"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/checkpoint/format_utils.py#L132"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.format_utils.BroadcastingTorchSaveReader.prepare_local_plan" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">prepare_local_plan</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">plan</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/format_utils.html#BroadcastingTorchSaveReader.prepare_local_plan"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/checkpoint/format_utils.py#L132"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.format_utils.BroadcastingTorchSaveReader.prepare_local_plan" title="Permalink to this definition">¶</a></dt>
 <dd><p>Implementation of the StorageReader method</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -2134,7 +2134,7 @@ <h2>Additional resources:<a class="headerlink" href="#additional-resources" titl
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.checkpoint.format_utils.BroadcastingTorchSaveReader.read_data">
-<span class="sig-name descname"><span class="pre">read_data</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">plan</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">planner</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/format_utils.html#BroadcastingTorchSaveReader.read_data"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/checkpoint/format_utils.py#L73"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.format_utils.BroadcastingTorchSaveReader.read_data" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">read_data</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">plan</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">planner</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/format_utils.html#BroadcastingTorchSaveReader.read_data"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/checkpoint/format_utils.py#L73"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.format_utils.BroadcastingTorchSaveReader.read_data" title="Permalink to this definition">¶</a></dt>
 <dd><p>Reads torch save data on the coordinator rank, and broadcast afterwards
 this incurrs a communication cost, but avoids having to load
 the entire checkpoint on each rank, hopefully preventing OOM issues</p>
@@ -2147,7 +2147,7 @@ <h2>Additional resources:<a class="headerlink" href="#additional-resources" titl
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.checkpoint.format_utils.BroadcastingTorchSaveReader.read_metadata">
-<span class="sig-name descname"><span class="pre">read_metadata</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/format_utils.html#BroadcastingTorchSaveReader.read_metadata"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/checkpoint/format_utils.py#L67"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.format_utils.BroadcastingTorchSaveReader.read_metadata" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">read_metadata</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/format_utils.html#BroadcastingTorchSaveReader.read_metadata"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/checkpoint/format_utils.py#L67"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.format_utils.BroadcastingTorchSaveReader.read_metadata" title="Permalink to this definition">¶</a></dt>
 <dd><p>Extends the default StorageReader to support building the metadata file</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -2158,7 +2158,7 @@ <h2>Additional resources:<a class="headerlink" href="#additional-resources" titl
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.checkpoint.format_utils.BroadcastingTorchSaveReader.reset">
-<span class="sig-name descname"><span class="pre">reset</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">checkpoint_id</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/format_utils.html#BroadcastingTorchSaveReader.reset"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/checkpoint/format_utils.py#L140"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.format_utils.BroadcastingTorchSaveReader.reset" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">reset</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">checkpoint_id</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/format_utils.html#BroadcastingTorchSaveReader.reset"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/checkpoint/format_utils.py#L140"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.format_utils.BroadcastingTorchSaveReader.reset" title="Permalink to this definition">¶</a></dt>
 <dd><p>Implementation of the StorageReader method</p>
 <dl class="field-list simple">
 </dl>
@@ -2166,7 +2166,7 @@ <h2>Additional resources:<a class="headerlink" href="#additional-resources" titl
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.checkpoint.format_utils.BroadcastingTorchSaveReader.set_up_storage_reader">
-<span class="sig-name descname"><span class="pre">set_up_storage_reader</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">metadata</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">is_coordinator</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/format_utils.html#BroadcastingTorchSaveReader.set_up_storage_reader"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/checkpoint/format_utils.py#L124"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.format_utils.BroadcastingTorchSaveReader.set_up_storage_reader" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">set_up_storage_reader</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">metadata</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">is_coordinator</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/format_utils.html#BroadcastingTorchSaveReader.set_up_storage_reader"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/checkpoint/format_utils.py#L124"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.format_utils.BroadcastingTorchSaveReader.set_up_storage_reader" title="Permalink to this definition">¶</a></dt>
 <dd><p>Implementation of the StorageReader method</p>
 <dl class="field-list simple">
 </dl>
@@ -2174,7 +2174,7 @@ <h2>Additional resources:<a class="headerlink" href="#additional-resources" titl
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.checkpoint.format_utils.BroadcastingTorchSaveReader.validate_checkpoint_id">
-<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">validate_checkpoint_id</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">checkpoint_id</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/format_utils.html#BroadcastingTorchSaveReader.validate_checkpoint_id"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/checkpoint/format_utils.py#L144"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.format_utils.BroadcastingTorchSaveReader.validate_checkpoint_id" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">validate_checkpoint_id</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">checkpoint_id</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/format_utils.html#BroadcastingTorchSaveReader.validate_checkpoint_id"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/checkpoint/format_utils.py#L144"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.format_utils.BroadcastingTorchSaveReader.validate_checkpoint_id" title="Permalink to this definition">¶</a></dt>
 <dd><p>Implementation of the StorageReader method</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -2187,7 +2187,7 @@ <h2>Additional resources:<a class="headerlink" href="#additional-resources" titl
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributed.checkpoint.format_utils.DynamicMetaLoadPlanner">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.checkpoint.format_utils.</span></span><span class="sig-name descname"><span class="pre">DynamicMetaLoadPlanner</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">flatten_state_dict</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">flatten_sharded_tensors</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">allow_partial_load</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/format_utils.html#DynamicMetaLoadPlanner"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/checkpoint/format_utils.py#L150"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.format_utils.DynamicMetaLoadPlanner" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.checkpoint.format_utils.</span></span><span class="sig-name descname"><span class="pre">DynamicMetaLoadPlanner</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">flatten_state_dict</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">flatten_sharded_tensors</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">allow_partial_load</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/format_utils.html#DynamicMetaLoadPlanner"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/checkpoint/format_utils.py#L150"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.format_utils.DynamicMetaLoadPlanner" title="Permalink to this definition">¶</a></dt>
 <dd><p>Extension of DefaultLoadPlanner, which creates a new Metadata object based on the passed in state dict,
 avoiding the need to read metadata from disk. This is useful when reading formats which don’t have a
 metadata file, like Torch Save files.</p>
@@ -2209,7 +2209,7 @@ <h2>Additional resources:<a class="headerlink" href="#additional-resources" titl
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.checkpoint.format_utils.DynamicMetaLoadPlanner.set_up_planner">
-<span class="sig-name descname"><span class="pre">set_up_planner</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state_dict</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">metadata</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">is_coordinator</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/format_utils.html#DynamicMetaLoadPlanner.set_up_planner"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/checkpoint/format_utils.py#L171"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.format_utils.DynamicMetaLoadPlanner.set_up_planner" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">set_up_planner</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state_dict</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">metadata</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">is_coordinator</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/checkpoint/format_utils.html#DynamicMetaLoadPlanner.set_up_planner"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/checkpoint/format_utils.py#L171"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.checkpoint.format_utils.DynamicMetaLoadPlanner.set_up_planner" title="Permalink to this definition">¶</a></dt>
 <dd><p>Setups of the planner, extnding default behavior by creating the Metadata object from the state dict</p>
 <dl class="field-list simple">
 </dl>
diff --git a/main/distributed.elastic.html b/main/distributed.elastic.html
index 9d395d88bfd..e399acc2874 100644
--- a/main/distributed.elastic.html
+++ b/main/distributed.elastic.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/distributed.fsdp.fully_shard.html b/main/distributed.fsdp.fully_shard.html
index 9bf7dda5a52..df2c947c8f7 100644
--- a/main/distributed.fsdp.fully_shard.html
+++ b/main/distributed.fsdp.fully_shard.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -660,7 +660,7 @@ <h2>PyTorch FSDP2 (<code class="docutils literal notranslate"><span class="pre">
 <p>The frontend API is <code class="docutils literal notranslate"><span class="pre">fully_shard</span></code> that can be called on a <code class="docutils literal notranslate"><span class="pre">module</span></code>:</p>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.distributed.fsdp.fully_shard">
-<span class="sig-prename descclassname"><span class="pre">torch.distributed.fsdp.</span></span><span class="sig-name descname"><span class="pre">fully_shard</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mesh</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reshard_after_forward</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shard_placement_fn</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mp_policy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">MixedPrecisionPolicy(param_dtype=None,</span> <span class="pre">reduce_dtype=None,</span> <span class="pre">output_dtype=None,</span> <span class="pre">cast_forward_inputs=True)</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">offload_policy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">OffloadPolicy()</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/fsdp/_fully_shard/_fully_shard.py#L40"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.fsdp.fully_shard" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributed.fsdp.</span></span><span class="sig-name descname"><span class="pre">fully_shard</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mesh</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reshard_after_forward</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shard_placement_fn</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mp_policy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">MixedPrecisionPolicy(param_dtype=None,</span> <span class="pre">reduce_dtype=None,</span> <span class="pre">output_dtype=None,</span> <span class="pre">cast_forward_inputs=True)</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">offload_policy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">OffloadPolicy()</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/fsdp/_fully_shard/_fully_shard.py#L40"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.fsdp.fully_shard" title="Permalink to this definition">¶</a></dt>
 <dd><p>Apply fully sharded data parallelism (FSDP) to <code class="docutils literal notranslate"><span class="pre">module</span></code>, where FSDP
 shards module parameters, gradients, and optimizer states across data
 parallel workers to save memory at the cost of communication.</p>
@@ -760,7 +760,7 @@ <h2>PyTorch FSDP2 (<code class="docutils literal notranslate"><span class="pre">
 <em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.fsdp.</span></span><span class="sig-name descname"><span class="pre">FSDPModule</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#torch.distributed.fsdp.FSDPModule" title="Permalink to this definition">¶</a></dt>
 <dd><dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.fsdp.FSDPModule.reshard">
-<span class="sig-name descname"><span class="pre">reshard</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/fsdp/_fully_shard/_fully_shard.html#FSDPModule.reshard"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/fsdp/_fully_shard/_fully_shard.py#L213"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.fsdp.FSDPModule.reshard" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">reshard</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/fsdp/_fully_shard/_fully_shard.html#FSDPModule.reshard"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/fsdp/_fully_shard/_fully_shard.py#L213"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.fsdp.FSDPModule.reshard" title="Permalink to this definition">¶</a></dt>
 <dd><p>Reshards the module’s parameters, freeing the unsharded parameters if
 they are allocated and registering the sharded parameters to the
 module. This method is <em>not</em> recursive.</p>
@@ -770,7 +770,7 @@ <h2>PyTorch FSDP2 (<code class="docutils literal notranslate"><span class="pre">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.fsdp.FSDPModule.set_is_last_backward">
-<span class="sig-name descname"><span class="pre">set_is_last_backward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">is_last_backward</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/fsdp/_fully_shard/_fully_shard.html#FSDPModule.set_is_last_backward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/fsdp/_fully_shard/_fully_shard.py#L252"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.fsdp.FSDPModule.set_is_last_backward" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">set_is_last_backward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">is_last_backward</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/fsdp/_fully_shard/_fully_shard.html#FSDPModule.set_is_last_backward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/fsdp/_fully_shard/_fully_shard.py#L252"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.fsdp.FSDPModule.set_is_last_backward" title="Permalink to this definition">¶</a></dt>
 <dd><p>Sets whether the next backward is the last one. On the last backward,
 FSDP waits on pending gradient reduction and clears internal data
 data structures for backward prefetching. This can be useful for
@@ -781,7 +781,7 @@ <h2>PyTorch FSDP2 (<code class="docutils literal notranslate"><span class="pre">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.fsdp.FSDPModule.set_modules_to_backward_prefetch">
-<span class="sig-name descname"><span class="pre">set_modules_to_backward_prefetch</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">modules</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/fsdp/_fully_shard/_fully_shard.html#FSDPModule.set_modules_to_backward_prefetch"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/fsdp/_fully_shard/_fully_shard.py#L344"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.fsdp.FSDPModule.set_modules_to_backward_prefetch" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">set_modules_to_backward_prefetch</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">modules</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/fsdp/_fully_shard/_fully_shard.html#FSDPModule.set_modules_to_backward_prefetch"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/fsdp/_fully_shard/_fully_shard.py#L344"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.fsdp.FSDPModule.set_modules_to_backward_prefetch" title="Permalink to this definition">¶</a></dt>
 <dd><p>Sets the FSDP modules for which this FSDP module should explicitly
 prefetch all-gathers in backward. This overrides the default backward
 pretching implementation that prefetches the next FSDP module based on
@@ -799,7 +799,7 @@ <h2>PyTorch FSDP2 (<code class="docutils literal notranslate"><span class="pre">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.fsdp.FSDPModule.set_modules_to_forward_prefetch">
-<span class="sig-name descname"><span class="pre">set_modules_to_forward_prefetch</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">modules</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/fsdp/_fully_shard/_fully_shard.html#FSDPModule.set_modules_to_forward_prefetch"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/fsdp/_fully_shard/_fully_shard.py#L324"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.fsdp.FSDPModule.set_modules_to_forward_prefetch" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">set_modules_to_forward_prefetch</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">modules</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/fsdp/_fully_shard/_fully_shard.html#FSDPModule.set_modules_to_forward_prefetch"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/fsdp/_fully_shard/_fully_shard.py#L324"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.fsdp.FSDPModule.set_modules_to_forward_prefetch" title="Permalink to this definition">¶</a></dt>
 <dd><p>Sets the FSDP modules for which this FSDP module should explicitly
 prefetch all-gathers in forward. The prefetching runs after this
 module’s all-gather copy-out.</p>
@@ -817,7 +817,7 @@ <h2>PyTorch FSDP2 (<code class="docutils literal notranslate"><span class="pre">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.fsdp.FSDPModule.set_post_optim_event">
-<span class="sig-name descname"><span class="pre">set_post_optim_event</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">event</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/fsdp/_fully_shard/_fully_shard.html#FSDPModule.set_post_optim_event"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/fsdp/_fully_shard/_fully_shard.py#L364"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.fsdp.FSDPModule.set_post_optim_event" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">set_post_optim_event</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">event</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/fsdp/_fully_shard/_fully_shard.html#FSDPModule.set_post_optim_event"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/fsdp/_fully_shard/_fully_shard.py#L364"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.fsdp.FSDPModule.set_post_optim_event" title="Permalink to this definition">¶</a></dt>
 <dd><p>Sets a post-optimizer-step event for the root FSDP module to wait the
 all-gather streams on.</p>
 <p>By default, the root FSDP module waits the all-gather streams on the
@@ -837,7 +837,7 @@ <h2>PyTorch FSDP2 (<code class="docutils literal notranslate"><span class="pre">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.fsdp.FSDPModule.set_reduce_scatter_divide_factor">
-<span class="sig-name descname"><span class="pre">set_reduce_scatter_divide_factor</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">factor</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/fsdp/_fully_shard/_fully_shard.html#FSDPModule.set_reduce_scatter_divide_factor"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/fsdp/_fully_shard/_fully_shard.py#L383"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.fsdp.FSDPModule.set_reduce_scatter_divide_factor" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">set_reduce_scatter_divide_factor</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">factor</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/fsdp/_fully_shard/_fully_shard.html#FSDPModule.set_reduce_scatter_divide_factor"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/fsdp/_fully_shard/_fully_shard.py#L383"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.fsdp.FSDPModule.set_reduce_scatter_divide_factor" title="Permalink to this definition">¶</a></dt>
 <dd><p>Sets a custom divide factor for the reduce-scatter. This becomes a
 custom reduce op using NCCL’s PreMulSum, which allows multiplying by
 the factor before reduction.</p>
@@ -850,7 +850,7 @@ <h2>PyTorch FSDP2 (<code class="docutils literal notranslate"><span class="pre">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.fsdp.FSDPModule.set_requires_all_reduce">
-<span class="sig-name descname"><span class="pre">set_requires_all_reduce</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">requires_all_reduce</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">recurse</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/fsdp/_fully_shard/_fully_shard.html#FSDPModule.set_requires_all_reduce"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/fsdp/_fully_shard/_fully_shard.py#L285"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.fsdp.FSDPModule.set_requires_all_reduce" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">set_requires_all_reduce</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">requires_all_reduce</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">recurse</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/fsdp/_fully_shard/_fully_shard.html#FSDPModule.set_requires_all_reduce"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/fsdp/_fully_shard/_fully_shard.py#L285"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.fsdp.FSDPModule.set_requires_all_reduce" title="Permalink to this definition">¶</a></dt>
 <dd><p>Sets if the module should all-reduce gradients. This can be used to
 implement gradient accumulation with only reduce-scatter but not
 all-reduce for HSDP.</p>
@@ -860,7 +860,7 @@ <h2>PyTorch FSDP2 (<code class="docutils literal notranslate"><span class="pre">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.fsdp.FSDPModule.set_requires_gradient_sync">
-<span class="sig-name descname"><span class="pre">set_requires_gradient_sync</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">requires_gradient_sync</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">recurse</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/fsdp/_fully_shard/_fully_shard.html#FSDPModule.set_requires_gradient_sync"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/fsdp/_fully_shard/_fully_shard.py#L262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.fsdp.FSDPModule.set_requires_gradient_sync" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">set_requires_gradient_sync</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">requires_gradient_sync</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">recurse</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/fsdp/_fully_shard/_fully_shard.html#FSDPModule.set_requires_gradient_sync"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/fsdp/_fully_shard/_fully_shard.py#L262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.fsdp.FSDPModule.set_requires_gradient_sync" title="Permalink to this definition">¶</a></dt>
 <dd><p>Sets if the module should sync gradients. This can be used to implement
 gradient accumulation <em>without communication</em>. For HSDP, this controls
 both reduce-scatter and all-reduce together.</p>
@@ -878,7 +878,7 @@ <h2>PyTorch FSDP2 (<code class="docutils literal notranslate"><span class="pre">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.fsdp.FSDPModule.set_reshard_after_backward">
-<span class="sig-name descname"><span class="pre">set_reshard_after_backward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">reshard_after_backward</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">recurse</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/fsdp/_fully_shard/_fully_shard.html#FSDPModule.set_reshard_after_backward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/fsdp/_fully_shard/_fully_shard.py#L301"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.fsdp.FSDPModule.set_reshard_after_backward" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">set_reshard_after_backward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">reshard_after_backward</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">recurse</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/fsdp/_fully_shard/_fully_shard.html#FSDPModule.set_reshard_after_backward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/fsdp/_fully_shard/_fully_shard.py#L301"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.fsdp.FSDPModule.set_reshard_after_backward" title="Permalink to this definition">¶</a></dt>
 <dd><p>Sets if the module should reshard parameters after backward. This can
 be used during gradient accumulation to trade off higher memory for
 reduced communication since the unsharded parameters do not need to be
@@ -897,7 +897,7 @@ <h2>PyTorch FSDP2 (<code class="docutils literal notranslate"><span class="pre">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.fsdp.FSDPModule.set_unshard_in_backward">
-<span class="sig-name descname"><span class="pre">set_unshard_in_backward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">unshard_in_backward</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/fsdp/_fully_shard/_fully_shard.html#FSDPModule.set_unshard_in_backward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/fsdp/_fully_shard/_fully_shard.py#L398"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.fsdp.FSDPModule.set_unshard_in_backward" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">set_unshard_in_backward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">unshard_in_backward</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/fsdp/_fully_shard/_fully_shard.html#FSDPModule.set_unshard_in_backward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/fsdp/_fully_shard/_fully_shard.py#L398"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.fsdp.FSDPModule.set_unshard_in_backward" title="Permalink to this definition">¶</a></dt>
 <dd><p>Sets whether the FSDP module’s parameters need to be unsharded in
 backward. This can be used in expert cases when the user knows that all
 parameters in this FSDP module’s parameter group are not needed for
@@ -908,7 +908,7 @@ <h2>PyTorch FSDP2 (<code class="docutils literal notranslate"><span class="pre">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.fsdp.FSDPModule.unshard">
-<span class="sig-name descname"><span class="pre">unshard</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">async_op</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/fsdp/_fully_shard/_fully_shard.html#FSDPModule.unshard"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/fsdp/_fully_shard/_fully_shard.py#L223"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.fsdp.FSDPModule.unshard" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">unshard</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">async_op</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/fsdp/_fully_shard/_fully_shard.html#FSDPModule.unshard"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/fsdp/_fully_shard/_fully_shard.py#L223"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.fsdp.FSDPModule.unshard" title="Permalink to this definition">¶</a></dt>
 <dd><p>Unshards the module’s parameters by allocating memory and all-gathering
 the parameters. This method is <em>not</em> recursive. The unshard follows the
 <a class="reference internal" href="#torch.distributed.fsdp.MixedPrecisionPolicy" title="torch.distributed.fsdp.MixedPrecisionPolicy"><code class="xref py py-class docutils literal notranslate"><span class="pre">MixedPrecisionPolicy</span></code></a>, so it will all-gather following
@@ -941,7 +941,7 @@ <h2>PyTorch FSDP2 (<code class="docutils literal notranslate"><span class="pre">
 <dd><p>A handle to wait on a <a class="reference internal" href="#torch.distributed.fsdp.FSDPModule.unshard" title="torch.distributed.fsdp.FSDPModule.unshard"><code class="xref py py-meth docutils literal notranslate"><span class="pre">FSDPModule.unshard()</span></code></a> op.</p>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.fsdp.UnshardHandle.wait">
-<span class="sig-name descname"><span class="pre">wait</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/fsdp/_fully_shard/_fully_shard.html#UnshardHandle.wait"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/fsdp/_fully_shard/_fully_shard.py#L453"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.fsdp.UnshardHandle.wait" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">wait</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/fsdp/_fully_shard/_fully_shard.html#UnshardHandle.wait"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/fsdp/_fully_shard/_fully_shard.py#L453"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.fsdp.UnshardHandle.wait" title="Permalink to this definition">¶</a></dt>
 <dd><p>Waits on the unshard op. This ensures that the current stream can use
 the unsharded parameters, which are now registered to the module.</p>
 <dl class="field-list simple">
@@ -952,7 +952,7 @@ <h2>PyTorch FSDP2 (<code class="docutils literal notranslate"><span class="pre">
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.distributed.fsdp.register_fsdp_forward_method">
-<span class="sig-prename descclassname"><span class="pre">torch.distributed.fsdp.</span></span><span class="sig-name descname"><span class="pre">register_fsdp_forward_method</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">method_name</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/fsdp/_fully_shard/_fully_shard.py#L472"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.fsdp.register_fsdp_forward_method" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributed.fsdp.</span></span><span class="sig-name descname"><span class="pre">register_fsdp_forward_method</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">method_name</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/fsdp/_fully_shard/_fully_shard.py#L472"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.fsdp.register_fsdp_forward_method" title="Permalink to this definition">¶</a></dt>
 <dd><p>Registers a method on <code class="docutils literal notranslate"><span class="pre">module</span></code> to be considered a forward method for
 FSDP.</p>
 <p>FSDP all-gathers parameters pre-forward and optionally frees parameters
diff --git a/main/distributed.html b/main/distributed.html
index 26763d41ef9..a852edd94e9 100644
--- a/main/distributed.html
+++ b/main/distributed.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -860,7 +860,7 @@ <h2>Initialization<a class="headerlink" href="#initialization" title="Permalink
 </div>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.distributed.is_available">
-<span class="sig-prename descclassname"><span class="pre">torch.distributed.</span></span><span class="sig-name descname"><span class="pre">is_available</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed.html#is_available"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/__init__.py#L14"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.is_available" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributed.</span></span><span class="sig-name descname"><span class="pre">is_available</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed.html#is_available"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/__init__.py#L14"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.is_available" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return <code class="docutils literal notranslate"><span class="pre">True</span></code> if the distributed package is available.</p>
 <p>Otherwise,
 <code class="docutils literal notranslate"><span class="pre">torch.distributed</span></code> does not expose any other APIs. Currently,
@@ -877,7 +877,7 @@ <h2>Initialization<a class="headerlink" href="#initialization" title="Permalink
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.distributed.init_process_group">
-<span class="sig-prename descclassname"><span class="pre">torch.distributed.</span></span><span class="sig-name descname"><span class="pre">init_process_group</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">backend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">init_method</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">timeout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">world_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">rank</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">store</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group_name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pg_options</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device_id</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/distributed_c10d.html#init_process_group"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/distributed_c10d.py#L1534"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.init_process_group" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributed.</span></span><span class="sig-name descname"><span class="pre">init_process_group</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">backend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">init_method</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">timeout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">world_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">rank</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">store</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group_name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pg_options</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device_id</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/distributed_c10d.html#init_process_group"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/distributed_c10d.py#L1534"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.init_process_group" title="Permalink to this definition">¶</a></dt>
 <dd><p>Initialize the default distributed process group.</p>
 <p>This will also initialize the distributed package.</p>
 <dl class="simple">
@@ -968,7 +968,7 @@ <h2>Initialization<a class="headerlink" href="#initialization" title="Permalink
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.distributed.device_mesh.init_device_mesh">
-<span class="sig-prename descclassname"><span class="pre">torch.distributed.device_mesh.</span></span><span class="sig-name descname"><span class="pre">init_device_mesh</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device_type</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mesh_shape</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mesh_dim_names</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/device_mesh.html#init_device_mesh"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/device_mesh.py#L940"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.device_mesh.init_device_mesh" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributed.device_mesh.</span></span><span class="sig-name descname"><span class="pre">init_device_mesh</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device_type</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mesh_shape</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mesh_dim_names</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/device_mesh.html#init_device_mesh"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/device_mesh.py#L940"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.device_mesh.init_device_mesh" title="Permalink to this definition">¶</a></dt>
 <dd><p>Initializes a <cite>DeviceMesh</cite> based on <cite>device_type</cite>, <cite>mesh_shape</cite>, and <cite>mesh_dim_names</cite> parameters.</p>
 <p>This creates a DeviceMesh with an n-dimensional array layout, where <cite>n</cite> is the length of <cite>mesh_shape</cite>.
 If <cite>mesh_dim_names</cite> is provided, each dimension is labeled as <cite>mesh_dim_names[i]</cite>.</p>
@@ -1015,7 +1015,7 @@ <h2>Initialization<a class="headerlink" href="#initialization" title="Permalink
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.distributed.is_initialized">
-<span class="sig-prename descclassname"><span class="pre">torch.distributed.</span></span><span class="sig-name descname"><span class="pre">is_initialized</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/distributed_c10d.html#is_initialized"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/distributed_c10d.py#L1273"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.is_initialized" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributed.</span></span><span class="sig-name descname"><span class="pre">is_initialized</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/distributed_c10d.html#is_initialized"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/distributed_c10d.py#L1273"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.is_initialized" title="Permalink to this definition">¶</a></dt>
 <dd><p>Check if the default process group has been initialized.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -1026,7 +1026,7 @@ <h2>Initialization<a class="headerlink" href="#initialization" title="Permalink
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.distributed.is_mpi_available">
-<span class="sig-prename descclassname"><span class="pre">torch.distributed.</span></span><span class="sig-name descname"><span class="pre">is_mpi_available</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/distributed_c10d.html#is_mpi_available"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/distributed_c10d.py#L1228"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.is_mpi_available" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributed.</span></span><span class="sig-name descname"><span class="pre">is_mpi_available</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/distributed_c10d.html#is_mpi_available"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/distributed_c10d.py#L1228"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.is_mpi_available" title="Permalink to this definition">¶</a></dt>
 <dd><p>Check if the MPI backend is available.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -1037,7 +1037,7 @@ <h2>Initialization<a class="headerlink" href="#initialization" title="Permalink
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.distributed.is_nccl_available">
-<span class="sig-prename descclassname"><span class="pre">torch.distributed.</span></span><span class="sig-name descname"><span class="pre">is_nccl_available</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/distributed_c10d.html#is_nccl_available"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/distributed_c10d.py#L1233"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.is_nccl_available" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributed.</span></span><span class="sig-name descname"><span class="pre">is_nccl_available</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/distributed_c10d.html#is_nccl_available"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/distributed_c10d.py#L1233"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.is_nccl_available" title="Permalink to this definition">¶</a></dt>
 <dd><p>Check if the NCCL backend is available.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -1048,7 +1048,7 @@ <h2>Initialization<a class="headerlink" href="#initialization" title="Permalink
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.distributed.is_gloo_available">
-<span class="sig-prename descclassname"><span class="pre">torch.distributed.</span></span><span class="sig-name descname"><span class="pre">is_gloo_available</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/distributed_c10d.html#is_gloo_available"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/distributed_c10d.py#L1238"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.is_gloo_available" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributed.</span></span><span class="sig-name descname"><span class="pre">is_gloo_available</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/distributed_c10d.html#is_gloo_available"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/distributed_c10d.py#L1238"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.is_gloo_available" title="Permalink to this definition">¶</a></dt>
 <dd><p>Check if the Gloo backend is available.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -1059,7 +1059,7 @@ <h2>Initialization<a class="headerlink" href="#initialization" title="Permalink
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.distributed.distributed_c10d.is_xccl_available">
-<span class="sig-prename descclassname"><span class="pre">torch.distributed.distributed_c10d.</span></span><span class="sig-name descname"><span class="pre">is_xccl_available</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/distributed_c10d.html#is_xccl_available"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/distributed_c10d.py#L1248"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.distributed_c10d.is_xccl_available" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributed.distributed_c10d.</span></span><span class="sig-name descname"><span class="pre">is_xccl_available</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/distributed_c10d.html#is_xccl_available"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/distributed_c10d.py#L1248"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.distributed_c10d.is_xccl_available" title="Permalink to this definition">¶</a></dt>
 <dd><p>Check if the XCCL backend is available.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -1070,7 +1070,7 @@ <h2>Initialization<a class="headerlink" href="#initialization" title="Permalink
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.distributed.is_torchelastic_launched">
-<span class="sig-prename descclassname"><span class="pre">torch.distributed.</span></span><span class="sig-name descname"><span class="pre">is_torchelastic_launched</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/distributed_c10d.html#is_torchelastic_launched"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/distributed_c10d.py#L1278"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.is_torchelastic_launched" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributed.</span></span><span class="sig-name descname"><span class="pre">is_torchelastic_launched</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/distributed_c10d.html#is_torchelastic_launched"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/distributed_c10d.py#L1278"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.is_torchelastic_launched" title="Permalink to this definition">¶</a></dt>
 <dd><p>Check whether this process was launched with <code class="docutils literal notranslate"><span class="pre">torch.distributed.elastic</span></code> (aka torchelastic).</p>
 <p>The existence of <code class="docutils literal notranslate"><span class="pre">TORCHELASTIC_RUN_ID</span></code> environment
 variable is used as a proxy to determine whether the current process
@@ -1165,7 +1165,7 @@ <h2>Post-Initialization<a class="headerlink" href="#post-initialization" title="
 check whether the process group has already been initialized use <a class="reference internal" href="#torch.distributed.is_initialized" title="torch.distributed.is_initialized"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.distributed.is_initialized()</span></code></a>.</p>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributed.Backend">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.</span></span><span class="sig-name descname"><span class="pre">Backend</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/distributed_c10d.html#Backend"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/distributed_c10d.py#L234"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.Backend" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.</span></span><span class="sig-name descname"><span class="pre">Backend</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/distributed_c10d.html#Backend"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/distributed_c10d.py#L234"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.Backend" title="Permalink to this definition">¶</a></dt>
 <dd><p>An enum-like class for backends.</p>
 <p>Available backends: GLOO, NCCL, UCC, MPI, XCCL, and other registered backends.</p>
 <p>The values of this class are lowercase strings, e.g., <code class="docutils literal notranslate"><span class="pre">&quot;gloo&quot;</span></code>. They can
@@ -1184,7 +1184,7 @@ <h2>Post-Initialization<a class="headerlink" href="#post-initialization" title="
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.Backend.register_backend">
-<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">register_backend</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">func</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">extended_api</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">devices</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/distributed_c10d.html#Backend.register_backend"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/distributed_c10d.py#L300"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.Backend.register_backend" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">register_backend</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">func</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">extended_api</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">devices</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/distributed_c10d.html#Backend.register_backend"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/distributed_c10d.py#L300"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.Backend.register_backend" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register a new backend with the given name and instantiating function.</p>
 <p>This class method is used by 3rd party <code class="docutils literal notranslate"><span class="pre">ProcessGroup</span></code> extension to
 register new backends.</p>
@@ -1217,7 +1217,7 @@ <h2>Post-Initialization<a class="headerlink" href="#post-initialization" title="
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.distributed.get_backend">
-<span class="sig-prename descclassname"><span class="pre">torch.distributed.</span></span><span class="sig-name descname"><span class="pre">get_backend</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">group</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/distributed_c10d.html#get_backend"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/distributed_c10d.py#L1350"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.get_backend" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributed.</span></span><span class="sig-name descname"><span class="pre">get_backend</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">group</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/distributed_c10d.html#get_backend"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/distributed_c10d.py#L1350"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.get_backend" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the backend of the given process group.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -1236,7 +1236,7 @@ <h2>Post-Initialization<a class="headerlink" href="#post-initialization" title="
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.distributed.get_rank">
-<span class="sig-prename descclassname"><span class="pre">torch.distributed.</span></span><span class="sig-name descname"><span class="pre">get_rank</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">group</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/distributed_c10d.html#get_rank"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/distributed_c10d.py#L2286"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.get_rank" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributed.</span></span><span class="sig-name descname"><span class="pre">get_rank</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">group</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/distributed_c10d.html#get_rank"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/distributed_c10d.py#L2286"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.get_rank" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the rank of the current process in the provided <code class="docutils literal notranslate"><span class="pre">group</span></code>, default otherwise.</p>
 <p>Rank is a unique identifier assigned to each process within a distributed
 process group. They are always consecutive integers ranging from 0 to
@@ -1258,7 +1258,7 @@ <h2>Post-Initialization<a class="headerlink" href="#post-initialization" title="
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.distributed.get_world_size">
-<span class="sig-prename descclassname"><span class="pre">torch.distributed.</span></span><span class="sig-name descname"><span class="pre">get_world_size</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">group</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/distributed_c10d.html#get_world_size"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/distributed_c10d.py#L2313"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.get_world_size" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributed.</span></span><span class="sig-name descname"><span class="pre">get_world_size</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">group</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/distributed_c10d.html#get_world_size"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/distributed_c10d.py#L2313"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.get_world_size" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the number of processes in the current process group.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -1314,7 +1314,7 @@ <h2>Groups<a class="headerlink" href="#groups" title="Permalink to this heading"
 (collectives are distributed functions to exchange information in certain well-known programming patterns).</p>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.distributed.new_group">
-<span class="sig-prename descclassname"><span class="pre">torch.distributed.</span></span><span class="sig-name descname"><span class="pre">new_group</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">ranks</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">timeout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">backend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pg_options</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_local_synchronization</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group_desc</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device_id</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/distributed_c10d.html#new_group"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/distributed_c10d.py#L4901"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.new_group" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributed.</span></span><span class="sig-name descname"><span class="pre">new_group</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">ranks</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">timeout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">backend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pg_options</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_local_synchronization</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group_desc</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device_id</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/distributed_c10d.html#new_group"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/distributed_c10d.py#L4901"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.new_group" title="Permalink to this definition">¶</a></dt>
 <dd><p>Create a new distributed group.</p>
 <p>This function requires that all processes in the main group (i.e. all
 processes that are part of the distributed job) enter this function, even
@@ -1382,7 +1382,7 @@ <h2>Groups<a class="headerlink" href="#groups" title="Permalink to this heading"
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.distributed.get_group_rank">
-<span class="sig-prename descclassname"><span class="pre">torch.distributed.</span></span><span class="sig-name descname"><span class="pre">get_group_rank</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">group</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">global_rank</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/distributed_c10d.html#get_group_rank"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/distributed_c10d.py#L1014"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.get_group_rank" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributed.</span></span><span class="sig-name descname"><span class="pre">get_group_rank</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">group</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">global_rank</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/distributed_c10d.html#get_group_rank"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/distributed_c10d.py#L1014"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.get_group_rank" title="Permalink to this definition">¶</a></dt>
 <dd><p>Translate a global rank into a group rank.</p>
 <p><code class="docutils literal notranslate"><span class="pre">global_rank</span></code> must be part of <code class="docutils literal notranslate"><span class="pre">group</span></code> otherwise this raises RuntimeError.</p>
 <dl class="field-list simple">
@@ -1404,7 +1404,7 @@ <h2>Groups<a class="headerlink" href="#groups" title="Permalink to this heading"
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.distributed.get_global_rank">
-<span class="sig-prename descclassname"><span class="pre">torch.distributed.</span></span><span class="sig-name descname"><span class="pre">get_global_rank</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">group</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group_rank</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/distributed_c10d.html#get_global_rank"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/distributed_c10d.py#L1042"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.get_global_rank" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributed.</span></span><span class="sig-name descname"><span class="pre">get_global_rank</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">group</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group_rank</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/distributed_c10d.html#get_global_rank"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/distributed_c10d.py#L1042"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.get_global_rank" title="Permalink to this definition">¶</a></dt>
 <dd><p>Translate a group rank into a global rank.</p>
 <p><code class="docutils literal notranslate"><span class="pre">group_rank</span></code> must be part of <cite>group</cite> otherwise this raises RuntimeError.</p>
 <dl class="field-list simple">
@@ -1426,7 +1426,7 @@ <h2>Groups<a class="headerlink" href="#groups" title="Permalink to this heading"
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.distributed.get_process_group_ranks">
-<span class="sig-prename descclassname"><span class="pre">torch.distributed.</span></span><span class="sig-name descname"><span class="pre">get_process_group_ranks</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">group</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/distributed_c10d.html#get_process_group_ranks"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/distributed_c10d.py#L1080"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.get_process_group_ranks" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributed.</span></span><span class="sig-name descname"><span class="pre">get_process_group_ranks</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">group</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/distributed_c10d.html#get_process_group_ranks"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/distributed_c10d.py#L1080"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.get_process_group_ranks" title="Permalink to this definition">¶</a></dt>
 <dd><p>Get all ranks associated with <code class="docutils literal notranslate"><span class="pre">group</span></code>.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -1451,7 +1451,7 @@ <h2>DeviceMesh<a class="headerlink" href="#devicemesh" title="Permalink to this
 used to create new DeviceMesh, with a mesh shape describing the device topology.</p>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributed.device_mesh.DeviceMesh">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.device_mesh.</span></span><span class="sig-name descname"><span class="pre">DeviceMesh</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device_type</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mesh</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mesh_dim_names</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_init_backend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/device_mesh.html#DeviceMesh"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/device_mesh.py#L379"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.device_mesh.DeviceMesh" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.device_mesh.</span></span><span class="sig-name descname"><span class="pre">DeviceMesh</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device_type</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mesh</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mesh_dim_names</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_init_backend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/device_mesh.html#DeviceMesh"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/device_mesh.py#L379"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.device_mesh.DeviceMesh" title="Permalink to this definition">¶</a></dt>
 <dd><p>DeviceMesh represents a mesh of devices, where layout of devices could be
 represented as a n-d dimension array, and each value of the n-d dimensional
 array is the global id of the default process group ranks.</p>
@@ -1497,7 +1497,7 @@ <h2>DeviceMesh<a class="headerlink" href="#devicemesh" title="Permalink to this
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.device_mesh.DeviceMesh.from_group">
-<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_group</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">group</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device_type</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mesh</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mesh_dim_names</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/device_mesh.html#DeviceMesh.from_group"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/device_mesh.py#L787"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.device_mesh.DeviceMesh.from_group" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_group</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">group</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device_type</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mesh</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mesh_dim_names</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/device_mesh.html#DeviceMesh.from_group"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/device_mesh.py#L787"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.device_mesh.DeviceMesh.from_group" title="Permalink to this definition">¶</a></dt>
 <dd><p>Constructs a <a class="reference internal" href="#torch.distributed.device_mesh.DeviceMesh" title="torch.distributed.device_mesh.DeviceMesh"><code class="xref py py-class docutils literal notranslate"><span class="pre">DeviceMesh</span></code></a> with <code class="docutils literal notranslate"><span class="pre">device_type</span></code> from an
 existing <code class="xref py py-class docutils literal notranslate"><span class="pre">ProcessGroup</span></code>.</p>
 <p>The constructed device mesh has number of dimensions equal to the
@@ -1512,7 +1512,7 @@ <h2>DeviceMesh<a class="headerlink" href="#devicemesh" title="Permalink to this
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.device_mesh.DeviceMesh.get_all_groups">
-<span class="sig-name descname"><span class="pre">get_all_groups</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/device_mesh.html#DeviceMesh.get_all_groups"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/device_mesh.py#L778"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.device_mesh.DeviceMesh.get_all_groups" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">get_all_groups</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/device_mesh.html#DeviceMesh.get_all_groups"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/device_mesh.py#L778"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.device_mesh.DeviceMesh.get_all_groups" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns a list of ProcessGroups for all mesh dimensions.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Returns</dt>
@@ -1526,7 +1526,7 @@ <h2>DeviceMesh<a class="headerlink" href="#devicemesh" title="Permalink to this
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.device_mesh.DeviceMesh.get_coordinate">
-<span class="sig-name descname"><span class="pre">get_coordinate</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/device_mesh.html#DeviceMesh.get_coordinate"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/device_mesh.py#L913"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.device_mesh.DeviceMesh.get_coordinate" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">get_coordinate</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/device_mesh.html#DeviceMesh.get_coordinate"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/device_mesh.py#L913"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.device_mesh.DeviceMesh.get_coordinate" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the relative indices of this rank relative to all
 dimensions of the mesh. If this rank is not part of the mesh, return None.</p>
 <dl class="field-list simple">
@@ -1538,7 +1538,7 @@ <h2>DeviceMesh<a class="headerlink" href="#devicemesh" title="Permalink to this
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.device_mesh.DeviceMesh.get_group">
-<span class="sig-name descname"><span class="pre">get_group</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mesh_dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/device_mesh.html#DeviceMesh.get_group"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/device_mesh.py#L732"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.device_mesh.DeviceMesh.get_group" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">get_group</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mesh_dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/device_mesh.html#DeviceMesh.get_group"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/device_mesh.py#L732"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.device_mesh.DeviceMesh.get_group" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns the single ProcessGroup specified by mesh_dim, or, if mesh_dim is not specified and the
 DeviceMesh is 1-dimensional, returns the only ProcessGroup in the mesh.</p>
 <dl class="field-list simple">
@@ -1559,7 +1559,7 @@ <h2>DeviceMesh<a class="headerlink" href="#devicemesh" title="Permalink to this
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.device_mesh.DeviceMesh.get_local_rank">
-<span class="sig-name descname"><span class="pre">get_local_rank</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mesh_dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/device_mesh.html#DeviceMesh.get_local_rank"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/device_mesh.py#L871"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.device_mesh.DeviceMesh.get_local_rank" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">get_local_rank</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mesh_dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/device_mesh.html#DeviceMesh.get_local_rank"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/device_mesh.py#L871"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.device_mesh.DeviceMesh.get_local_rank" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns the local rank of the given mesh_dim of the DeviceMesh.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -1597,7 +1597,7 @@ <h2>DeviceMesh<a class="headerlink" href="#devicemesh" title="Permalink to this
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.device_mesh.DeviceMesh.get_rank">
-<span class="sig-name descname"><span class="pre">get_rank</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/device_mesh.html#DeviceMesh.get_rank"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/device_mesh.py#L865"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.device_mesh.DeviceMesh.get_rank" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">get_rank</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/device_mesh.html#DeviceMesh.get_rank"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/device_mesh.py#L865"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.device_mesh.DeviceMesh.get_rank" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns the current global rank.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -1613,7 +1613,7 @@ <h2>DeviceMesh<a class="headerlink" href="#devicemesh" title="Permalink to this
 <h2>Point-to-point communication<a class="headerlink" href="#point-to-point-communication" title="Permalink to this heading">¶</a></h2>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.distributed.send">
-<span class="sig-prename descclassname"><span class="pre">torch.distributed.</span></span><span class="sig-name descname"><span class="pre">send</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dst</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tag</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group_dst</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/distributed_c10d.html#send"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/distributed_c10d.py#L2422"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.send" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributed.</span></span><span class="sig-name descname"><span class="pre">send</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dst</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tag</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group_dst</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/distributed_c10d.html#send"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/distributed_c10d.py#L2422"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.send" title="Permalink to this definition">¶</a></dt>
 <dd><p>Send a tensor synchronously.</p>
 <div class="admonition warning">
 <p class="admonition-title">Warning</p>
@@ -1636,7 +1636,7 @@ <h2>Point-to-point communication<a class="headerlink" href="#point-to-point-comm
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.distributed.recv">
-<span class="sig-prename descclassname"><span class="pre">torch.distributed.</span></span><span class="sig-name descname"><span class="pre">recv</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">src</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tag</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group_src</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/distributed_c10d.html#recv"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/distributed_c10d.py#L2454"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.recv" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributed.</span></span><span class="sig-name descname"><span class="pre">recv</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">src</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tag</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group_src</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/distributed_c10d.html#recv"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/distributed_c10d.py#L2454"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.recv" title="Permalink to this definition">¶</a></dt>
 <dd><p>Receives a tensor synchronously.</p>
 <div class="admonition warning">
 <p class="admonition-title">Warning</p>
@@ -1674,7 +1674,7 @@ <h2>Point-to-point communication<a class="headerlink" href="#point-to-point-comm
 </ul>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.distributed.isend">
-<span class="sig-prename descclassname"><span class="pre">torch.distributed.</span></span><span class="sig-name descname"><span class="pre">isend</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dst</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tag</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group_dst</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/distributed_c10d.html#isend"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/distributed_c10d.py#L2332"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.isend" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributed.</span></span><span class="sig-name descname"><span class="pre">isend</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dst</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tag</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group_dst</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/distributed_c10d.html#isend"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/distributed_c10d.py#L2332"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.isend" title="Permalink to this definition">¶</a></dt>
 <dd><p>Send a tensor asynchronously.</p>
 <div class="admonition warning">
 <p class="admonition-title">Warning</p>
@@ -1709,7 +1709,7 @@ <h2>Point-to-point communication<a class="headerlink" href="#point-to-point-comm
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.distributed.irecv">
-<span class="sig-prename descclassname"><span class="pre">torch.distributed.</span></span><span class="sig-name descname"><span class="pre">irecv</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">src</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tag</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group_src</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/distributed_c10d.html#irecv"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/distributed_c10d.py#L2377"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.irecv" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributed.</span></span><span class="sig-name descname"><span class="pre">irecv</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">src</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tag</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group_src</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/distributed_c10d.html#irecv"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/distributed_c10d.py#L2377"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.irecv" title="Permalink to this definition">¶</a></dt>
 <dd><p>Receives a tensor asynchronously.</p>
 <div class="admonition warning">
 <p class="admonition-title">Warning</p>
@@ -1740,7 +1740,7 @@ <h2>Point-to-point communication<a class="headerlink" href="#point-to-point-comm
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.distributed.send_object_list">
-<span class="sig-prename descclassname"><span class="pre">torch.distributed.</span></span><span class="sig-name descname"><span class="pre">send_object_list</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">object_list</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dst</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group_dst</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/distributed_c10d.html#send_object_list"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/distributed_c10d.py#L3179"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.send_object_list" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributed.</span></span><span class="sig-name descname"><span class="pre">send_object_list</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">object_list</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dst</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group_dst</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/distributed_c10d.html#send_object_list"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/distributed_c10d.py#L3179"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.send_object_list" title="Permalink to this definition">¶</a></dt>
 <dd><p>Sends picklable objects in <code class="docutils literal notranslate"><span class="pre">object_list</span></code> synchronously.</p>
 <p>Similar to <a class="reference internal" href="#torch.distributed.send" title="torch.distributed.send"><code class="xref py py-func docutils literal notranslate"><span class="pre">send()</span></code></a>, but Python objects can be passed in.
 Note that all objects in <code class="docutils literal notranslate"><span class="pre">object_list</span></code> must be picklable in order to be
@@ -1809,7 +1809,7 @@ <h2>Point-to-point communication<a class="headerlink" href="#point-to-point-comm
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.distributed.recv_object_list">
-<span class="sig-prename descclassname"><span class="pre">torch.distributed.</span></span><span class="sig-name descname"><span class="pre">recv_object_list</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">object_list</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">src</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group_src</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/distributed_c10d.html#recv_object_list"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/distributed_c10d.py#L3278"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.recv_object_list" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributed.</span></span><span class="sig-name descname"><span class="pre">recv_object_list</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">object_list</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">src</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group_src</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/distributed_c10d.html#recv_object_list"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/distributed_c10d.py#L3278"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.recv_object_list" title="Permalink to this definition">¶</a></dt>
 <dd><p>Receives picklable objects in <code class="docutils literal notranslate"><span class="pre">object_list</span></code> synchronously.</p>
 <p>Similar to <a class="reference internal" href="#torch.distributed.recv" title="torch.distributed.recv"><code class="xref py py-func docutils literal notranslate"><span class="pre">recv()</span></code></a>, but can receive Python objects.</p>
 <dl class="field-list simple">
@@ -1876,7 +1876,7 @@ <h2>Point-to-point communication<a class="headerlink" href="#point-to-point-comm
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.distributed.batch_isend_irecv">
-<span class="sig-prename descclassname"><span class="pre">torch.distributed.</span></span><span class="sig-name descname"><span class="pre">batch_isend_irecv</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">p2p_op_list</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/distributed_c10d.html#batch_isend_irecv"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/distributed_c10d.py#L2610"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.batch_isend_irecv" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributed.</span></span><span class="sig-name descname"><span class="pre">batch_isend_irecv</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">p2p_op_list</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/distributed_c10d.html#batch_isend_irecv"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/distributed_c10d.py#L2610"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.batch_isend_irecv" title="Permalink to this definition">¶</a></dt>
 <dd><p>Send or Receive a batch of tensors asynchronously and return a list of requests.</p>
 <p>Process each of the operations in <code class="docutils literal notranslate"><span class="pre">p2p_op_list</span></code> and return the corresponding
 requests. NCCL, Gloo, and UCC backend are currently supported.</p>
@@ -1923,7 +1923,7 @@ <h2>Point-to-point communication<a class="headerlink" href="#point-to-point-comm
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributed.P2POp">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.</span></span><span class="sig-name descname"><span class="pre">P2POp</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">op</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">peer</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tag</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group_peer</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/distributed_c10d.html#P2POp"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/distributed_c10d.py#L476"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.P2POp" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.</span></span><span class="sig-name descname"><span class="pre">P2POp</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">op</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">peer</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tag</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group_peer</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/distributed_c10d.html#P2POp"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/distributed_c10d.py#L476"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.P2POp" title="Permalink to this definition">¶</a></dt>
 <dd><p>A class to build point-to-point operations for <code class="docutils literal notranslate"><span class="pre">batch_isend_irecv</span></code>.</p>
 <p>This class builds the type of P2P operation, communication buffer, peer rank,
 Process Group, and tag. Instances of this class will be passed to
@@ -1998,7 +1998,7 @@ <h2>Synchronous and asynchronous collective operations<a class="headerlink" href
 <h2>Collective functions<a class="headerlink" href="#collective-functions" title="Permalink to this heading">¶</a></h2>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.distributed.broadcast">
-<span class="sig-prename descclassname"><span class="pre">torch.distributed.</span></span><span class="sig-name descname"><span class="pre">broadcast</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">src</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">async_op</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group_src</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/distributed_c10d.html#broadcast"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/distributed_c10d.py#L2685"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.broadcast" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributed.</span></span><span class="sig-name descname"><span class="pre">broadcast</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">src</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">async_op</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group_src</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/distributed_c10d.html#broadcast"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/distributed_c10d.py#L2685"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.broadcast" title="Permalink to this definition">¶</a></dt>
 <dd><p>Broadcasts the tensor to the whole group.</p>
 <p><code class="docutils literal notranslate"><span class="pre">tensor</span></code> must have the same number of elements in all processes
 participating in the collective.</p>
@@ -2024,7 +2024,7 @@ <h2>Collective functions<a class="headerlink" href="#collective-functions" title
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.distributed.broadcast_object_list">
-<span class="sig-prename descclassname"><span class="pre">torch.distributed.</span></span><span class="sig-name descname"><span class="pre">broadcast_object_list</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">object_list</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">src</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group_src</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/distributed_c10d.html#broadcast_object_list"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/distributed_c10d.py#L3380"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.broadcast_object_list" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributed.</span></span><span class="sig-name descname"><span class="pre">broadcast_object_list</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">object_list</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">src</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group_src</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/distributed_c10d.html#broadcast_object_list"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/distributed_c10d.py#L3380"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.broadcast_object_list" title="Permalink to this definition">¶</a></dt>
 <dd><p>Broadcasts picklable objects in <code class="docutils literal notranslate"><span class="pre">object_list</span></code> to the whole group.</p>
 <p>Similar to <a class="reference internal" href="#torch.distributed.broadcast" title="torch.distributed.broadcast"><code class="xref py py-func docutils literal notranslate"><span class="pre">broadcast()</span></code></a>, but Python objects can be passed in.
 Note that all objects in <code class="docutils literal notranslate"><span class="pre">object_list</span></code> must be picklable in order to be
@@ -2100,7 +2100,7 @@ <h2>Collective functions<a class="headerlink" href="#collective-functions" title
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.distributed.all_reduce">
-<span class="sig-prename descclassname"><span class="pre">torch.distributed.</span></span><span class="sig-name descname"><span class="pre">all_reduce</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">op=&lt;RedOpType.SUM:</span> <span class="pre">0&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">async_op=False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/distributed_c10d.html#all_reduce"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/distributed_c10d.py#L2732"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.all_reduce" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributed.</span></span><span class="sig-name descname"><span class="pre">all_reduce</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">op=&lt;RedOpType.SUM:</span> <span class="pre">0&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">async_op=False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/distributed_c10d.html#all_reduce"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/distributed_c10d.py#L2732"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.all_reduce" title="Permalink to this definition">¶</a></dt>
 <dd><p>Reduces the tensor data across all machines in a way that all get the final result.</p>
 <p>After the call <code class="docutils literal notranslate"><span class="pre">tensor</span></code> is going to be bitwise identical in all processes.</p>
 <p>Complex tensors are supported.</p>
@@ -2152,7 +2152,7 @@ <h2>Collective functions<a class="headerlink" href="#collective-functions" title
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.distributed.reduce">
-<span class="sig-prename descclassname"><span class="pre">torch.distributed.</span></span><span class="sig-name descname"><span class="pre">reduce</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dst=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">op=&lt;RedOpType.SUM:</span> <span class="pre">0&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">async_op=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group_dst=None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/distributed_c10d.html#reduce"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/distributed_c10d.py#L2878"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.reduce" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributed.</span></span><span class="sig-name descname"><span class="pre">reduce</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dst=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">op=&lt;RedOpType.SUM:</span> <span class="pre">0&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">async_op=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group_dst=None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/distributed_c10d.html#reduce"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/distributed_c10d.py#L2878"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.reduce" title="Permalink to this definition">¶</a></dt>
 <dd><p>Reduces the tensor data across all machines.</p>
 <p>Only the process with rank <code class="docutils literal notranslate"><span class="pre">dst</span></code> is going to receive the final result.</p>
 <dl class="field-list simple">
@@ -2180,7 +2180,7 @@ <h2>Collective functions<a class="headerlink" href="#collective-functions" title
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.distributed.all_gather">
-<span class="sig-prename descclassname"><span class="pre">torch.distributed.</span></span><span class="sig-name descname"><span class="pre">all_gather</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tensor_list</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">async_op</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/distributed_c10d.html#all_gather"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/distributed_c10d.py#L3636"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.all_gather" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributed.</span></span><span class="sig-name descname"><span class="pre">all_gather</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tensor_list</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">async_op</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/distributed_c10d.html#all_gather"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/distributed_c10d.py#L3636"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.all_gather" title="Permalink to this definition">¶</a></dt>
 <dd><p>Gathers tensors from the whole group in a list.</p>
 <p>Complex and uneven sized tensors are supported.</p>
 <dl class="field-list simple">
@@ -2238,7 +2238,7 @@ <h2>Collective functions<a class="headerlink" href="#collective-functions" title
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.distributed.all_gather_into_tensor">
-<span class="sig-prename descclassname"><span class="pre">torch.distributed.</span></span><span class="sig-name descname"><span class="pre">all_gather_into_tensor</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">output_tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">async_op</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/distributed_c10d.html#all_gather_into_tensor"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/distributed_c10d.py#L3711"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.all_gather_into_tensor" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributed.</span></span><span class="sig-name descname"><span class="pre">all_gather_into_tensor</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">output_tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">async_op</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/distributed_c10d.html#all_gather_into_tensor"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/distributed_c10d.py#L3711"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.all_gather_into_tensor" title="Permalink to this definition">¶</a></dt>
 <dd><p>Gather tensors from all ranks and put them in a single output tensor.</p>
 <p>This function requires all tensors to be the same size on each process.</p>
 <dl class="field-list simple">
@@ -2297,7 +2297,7 @@ <h2>Collective functions<a class="headerlink" href="#collective-functions" title
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.distributed.all_gather_object">
-<span class="sig-prename descclassname"><span class="pre">torch.distributed.</span></span><span class="sig-name descname"><span class="pre">all_gather_object</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">object_list</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">obj</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/distributed_c10d.html#all_gather_object"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/distributed_c10d.py#L2963"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.all_gather_object" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributed.</span></span><span class="sig-name descname"><span class="pre">all_gather_object</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">object_list</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">obj</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/distributed_c10d.html#all_gather_object"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/distributed_c10d.py#L2963"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.all_gather_object" title="Permalink to this definition">¶</a></dt>
 <dd><p>Gathers picklable objects from the whole group into a list.</p>
 <p>Similar to <a class="reference internal" href="#torch.distributed.all_gather" title="torch.distributed.all_gather"><code class="xref py py-func docutils literal notranslate"><span class="pre">all_gather()</span></code></a>, but Python objects can be passed in.
 Note that the object must be picklable in order to be gathered.</p>
@@ -2363,7 +2363,7 @@ <h2>Collective functions<a class="headerlink" href="#collective-functions" title
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.distributed.gather">
-<span class="sig-prename descclassname"><span class="pre">torch.distributed.</span></span><span class="sig-name descname"><span class="pre">gather</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">gather_list</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dst</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">async_op</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group_dst</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/distributed_c10d.html#gather"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/distributed_c10d.py#L3933"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.gather" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributed.</span></span><span class="sig-name descname"><span class="pre">gather</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">gather_list</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dst</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">async_op</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group_dst</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/distributed_c10d.html#gather"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/distributed_c10d.py#L3933"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.gather" title="Permalink to this definition">¶</a></dt>
 <dd><p>Gathers a list of tensors in a single process.</p>
 <p>This function requires all tensors to be the same size on each process.</p>
 <dl class="field-list simple">
@@ -2412,7 +2412,7 @@ <h2>Collective functions<a class="headerlink" href="#collective-functions" title
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.distributed.gather_object">
-<span class="sig-prename descclassname"><span class="pre">torch.distributed.</span></span><span class="sig-name descname"><span class="pre">gather_object</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">obj</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">object_gather_list</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dst</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group_dst</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/distributed_c10d.html#gather_object"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/distributed_c10d.py#L3054"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.gather_object" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributed.</span></span><span class="sig-name descname"><span class="pre">gather_object</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">obj</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">object_gather_list</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dst</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group_dst</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/distributed_c10d.html#gather_object"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/distributed_c10d.py#L3054"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.gather_object" title="Permalink to this definition">¶</a></dt>
 <dd><p>Gathers picklable objects from the whole group in a single process.</p>
 <p>Similar to <a class="reference internal" href="#torch.distributed.gather" title="torch.distributed.gather"><code class="xref py py-func docutils literal notranslate"><span class="pre">gather()</span></code></a>, but Python objects can be passed in. Note that the
 object must be picklable in order to be gathered.</p>
@@ -2486,7 +2486,7 @@ <h2>Collective functions<a class="headerlink" href="#collective-functions" title
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.distributed.scatter">
-<span class="sig-prename descclassname"><span class="pre">torch.distributed.</span></span><span class="sig-name descname"><span class="pre">scatter</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scatter_list</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">src</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">async_op</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group_src</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/distributed_c10d.html#scatter"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/distributed_c10d.py#L4013"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.scatter" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributed.</span></span><span class="sig-name descname"><span class="pre">scatter</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scatter_list</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">src</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">async_op</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group_src</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/distributed_c10d.html#scatter"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/distributed_c10d.py#L4013"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.scatter" title="Permalink to this definition">¶</a></dt>
 <dd><p>Scatters a list of tensors to all processes in a group.</p>
 <p>Each process will receive exactly one tensor and store its data in the
 <code class="docutils literal notranslate"><span class="pre">tensor</span></code> argument.</p>
@@ -2541,7 +2541,7 @@ <h2>Collective functions<a class="headerlink" href="#collective-functions" title
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.distributed.scatter_object_list">
-<span class="sig-prename descclassname"><span class="pre">torch.distributed.</span></span><span class="sig-name descname"><span class="pre">scatter_object_list</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">scatter_object_output_list</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scatter_object_input_list</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">src</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group_src</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/distributed_c10d.html#scatter_object_list"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/distributed_c10d.py#L3506"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.scatter_object_list" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributed.</span></span><span class="sig-name descname"><span class="pre">scatter_object_list</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">scatter_object_output_list</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scatter_object_input_list</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">src</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group_src</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/distributed_c10d.html#scatter_object_list"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/distributed_c10d.py#L3506"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.scatter_object_list" title="Permalink to this definition">¶</a></dt>
 <dd><p>Scatters picklable objects in <code class="docutils literal notranslate"><span class="pre">scatter_object_input_list</span></code> to the whole group.</p>
 <p>Similar to <a class="reference internal" href="#torch.distributed.scatter" title="torch.distributed.scatter"><code class="xref py py-func docutils literal notranslate"><span class="pre">scatter()</span></code></a>, but Python objects can be passed in. On
 each rank, the scattered object will be stored as the first element of
@@ -2609,7 +2609,7 @@ <h2>Collective functions<a class="headerlink" href="#collective-functions" title
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.distributed.reduce_scatter">
-<span class="sig-prename descclassname"><span class="pre">torch.distributed.</span></span><span class="sig-name descname"><span class="pre">reduce_scatter</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">output</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_list</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">op=&lt;RedOpType.SUM:</span> <span class="pre">0&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">async_op=False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/distributed_c10d.html#reduce_scatter"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/distributed_c10d.py#L4117"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.reduce_scatter" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributed.</span></span><span class="sig-name descname"><span class="pre">reduce_scatter</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">output</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_list</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">op=&lt;RedOpType.SUM:</span> <span class="pre">0&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">async_op=False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/distributed_c10d.html#reduce_scatter"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/distributed_c10d.py#L4117"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.reduce_scatter" title="Permalink to this definition">¶</a></dt>
 <dd><p>Reduces, then scatters a list of tensors to all processes in a group.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -2633,7 +2633,7 @@ <h2>Collective functions<a class="headerlink" href="#collective-functions" title
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.distributed.reduce_scatter_tensor">
-<span class="sig-prename descclassname"><span class="pre">torch.distributed.</span></span><span class="sig-name descname"><span class="pre">reduce_scatter_tensor</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">output</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">op=&lt;RedOpType.SUM:</span> <span class="pre">0&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">async_op=False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/distributed_c10d.html#reduce_scatter_tensor"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/distributed_c10d.py#L4156"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.reduce_scatter_tensor" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributed.</span></span><span class="sig-name descname"><span class="pre">reduce_scatter_tensor</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">output</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">op=&lt;RedOpType.SUM:</span> <span class="pre">0&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">async_op=False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/distributed_c10d.html#reduce_scatter_tensor"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/distributed_c10d.py#L4156"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.reduce_scatter_tensor" title="Permalink to this definition">¶</a></dt>
 <dd><p>Reduces, then scatters a tensor to all ranks in a group.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -2693,7 +2693,7 @@ <h2>Collective functions<a class="headerlink" href="#collective-functions" title
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.distributed.all_to_all_single">
-<span class="sig-prename descclassname"><span class="pre">torch.distributed.</span></span><span class="sig-name descname"><span class="pre">all_to_all_single</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">output</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_split_sizes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_split_sizes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">async_op</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/distributed_c10d.html#all_to_all_single"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/distributed_c10d.py#L4270"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.all_to_all_single" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributed.</span></span><span class="sig-name descname"><span class="pre">all_to_all_single</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">output</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_split_sizes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_split_sizes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">async_op</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/distributed_c10d.html#all_to_all_single"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/distributed_c10d.py#L4270"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.all_to_all_single" title="Permalink to this definition">¶</a></dt>
 <dd><p>Split input tensor and then scatter the split list to all processes in a group.</p>
 <p>Later the received tensors are concatenated from all the processes in the group
 and returned as a single output tensor.</p>
@@ -2791,7 +2791,7 @@ <h2>Collective functions<a class="headerlink" href="#collective-functions" title
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.distributed.all_to_all">
-<span class="sig-prename descclassname"><span class="pre">torch.distributed.</span></span><span class="sig-name descname"><span class="pre">all_to_all</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">output_tensor_list</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_tensor_list</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">async_op</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/distributed_c10d.html#all_to_all"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/distributed_c10d.py#L4397"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.all_to_all" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributed.</span></span><span class="sig-name descname"><span class="pre">all_to_all</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">output_tensor_list</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_tensor_list</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">async_op</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/distributed_c10d.html#all_to_all"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/distributed_c10d.py#L4397"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.all_to_all" title="Permalink to this definition">¶</a></dt>
 <dd><p>Scatters list of input tensors to all processes in a group and return gathered list of tensors in output list.</p>
 <p>Complex tensors are supported.</p>
 <dl class="field-list simple">
@@ -2889,7 +2889,7 @@ <h2>Collective functions<a class="headerlink" href="#collective-functions" title
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.distributed.barrier">
-<span class="sig-prename descclassname"><span class="pre">torch.distributed.</span></span><span class="sig-name descname"><span class="pre">barrier</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">group</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">async_op</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device_ids</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/distributed_c10d.html#barrier"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/distributed_c10d.py#L4513"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.barrier" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributed.</span></span><span class="sig-name descname"><span class="pre">barrier</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">group</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">async_op</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device_ids</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/distributed_c10d.html#barrier"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/distributed_c10d.py#L4513"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.barrier" title="Permalink to this definition">¶</a></dt>
 <dd><p>Synchronize all processes.</p>
 <p>This collective blocks processes until the whole group enters this function,
 if async_op is False, or if async work handle is called on wait().</p>
@@ -2915,7 +2915,7 @@ <h2>Collective functions<a class="headerlink" href="#collective-functions" title
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.distributed.monitored_barrier">
-<span class="sig-prename descclassname"><span class="pre">torch.distributed.</span></span><span class="sig-name descname"><span class="pre">monitored_barrier</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">group</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">timeout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">wait_all_ranks</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/distributed_c10d.html#monitored_barrier"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/distributed_c10d.py#L4558"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.monitored_barrier" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributed.</span></span><span class="sig-name descname"><span class="pre">monitored_barrier</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">group</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">timeout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">wait_all_ranks</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/distributed_c10d.html#monitored_barrier"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/distributed_c10d.py#L4558"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.monitored_barrier" title="Permalink to this definition">¶</a></dt>
 <dd><p>Synchronize processes similar to <code class="docutils literal notranslate"><span class="pre">torch.distributed.barrier</span></code>, but consider a configurable timeout.</p>
 <p>It is able to report ranks that did not pass this barrier within the provided timeout.
 Specifically, for non-zero ranks, will block until a send/recv is processed from rank 0.
@@ -4161,7 +4161,7 @@ <h2>Logging<a class="headerlink" href="#logging" title="Permalink to this headin
 <p>If you are running single node training, it may be convenient to interactively breakpoint your script.  We offer a way to conveniently breakpoint a single rank:</p>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.distributed.breakpoint">
-<span class="sig-prename descclassname"><span class="pre">torch.distributed.</span></span><span class="sig-name descname"><span class="pre">breakpoint</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">rank</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">skip</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed.html#breakpoint"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/__init__.py#L84"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.breakpoint" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributed.</span></span><span class="sig-name descname"><span class="pre">breakpoint</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">rank</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">skip</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed.html#breakpoint"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/__init__.py#L84"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.breakpoint" title="Permalink to this definition">¶</a></dt>
 <dd><p>Set a breakpoint, but only on a single rank.  All other ranks will wait for you to be
 done with the breakpoint before continuing.</p>
 <dl class="field-list simple">
diff --git a/main/distributed.optim.html b/main/distributed.optim.html
index 8e4cddebd4b..753f85b3df1 100644
--- a/main/distributed.optim.html
+++ b/main/distributed.optim.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -619,7 +619,7 @@ <h1>Distributed Optimizers<a class="headerlink" href="#distributed-optimizers" t
 apply the gradients on each worker.</p>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributed.optim.DistributedOptimizer">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.optim.</span></span><span class="sig-name descname"><span class="pre">DistributedOptimizer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">optimizer_class</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">params_rref</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/optim/optimizer.html#DistributedOptimizer"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/optim/optimizer.py#L129"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.optim.DistributedOptimizer" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.optim.</span></span><span class="sig-name descname"><span class="pre">DistributedOptimizer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">optimizer_class</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">params_rref</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/optim/optimizer.html#DistributedOptimizer"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/optim/optimizer.py#L129"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.optim.DistributedOptimizer" title="Permalink to this definition">¶</a></dt>
 <dd><p>DistributedOptimizer takes remote references to parameters scattered
 across workers and applies the given optimizer locally for each parameter.</p>
 <p>This class uses <a class="reference internal" href="rpc.html#torch.distributed.autograd.get_gradients" title="torch.distributed.autograd.get_gradients"><code class="xref py py-meth docutils literal notranslate"><span class="pre">get_gradients()</span></code></a> in order
@@ -679,7 +679,7 @@ <h1>Distributed Optimizers<a class="headerlink" href="#distributed-optimizers" t
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.optim.DistributedOptimizer.step">
-<span class="sig-name descname"><span class="pre">step</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">context_id</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/optim/optimizer.html#DistributedOptimizer.step"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/optim/optimizer.py#L226"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.optim.DistributedOptimizer.step" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">step</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">context_id</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/optim/optimizer.html#DistributedOptimizer.step"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/optim/optimizer.py#L226"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.optim.DistributedOptimizer.step" title="Permalink to this definition">¶</a></dt>
 <dd><p>Performs a single optimization step.</p>
 <p>This will call <a class="reference internal" href="generated/torch.optim.Optimizer.step.html#torch.optim.Optimizer.step" title="torch.optim.Optimizer.step"><code class="xref py py-meth docutils literal notranslate"><span class="pre">torch.optim.Optimizer.step()</span></code></a> on each worker
 containing parameters to be optimized, and will block until all workers
@@ -698,7 +698,7 @@ <h1>Distributed Optimizers<a class="headerlink" href="#distributed-optimizers" t
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributed.optim.PostLocalSGDOptimizer">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.optim.</span></span><span class="sig-name descname"><span class="pre">PostLocalSGDOptimizer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">optim</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">averager</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/optim/post_localSGD_optimizer.html#PostLocalSGDOptimizer"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/optim/post_localSGD_optimizer.py#L8"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.optim.PostLocalSGDOptimizer" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.optim.</span></span><span class="sig-name descname"><span class="pre">PostLocalSGDOptimizer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">optim</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">averager</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/optim/post_localSGD_optimizer.html#PostLocalSGDOptimizer"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/optim/post_localSGD_optimizer.py#L8"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.optim.PostLocalSGDOptimizer" title="Permalink to this definition">¶</a></dt>
 <dd><p>Wraps an arbitrary <a class="reference internal" href="optim.html#torch.optim.Optimizer" title="torch.optim.Optimizer"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.optim.Optimizer</span></code></a> and runs <a class="reference external" href="https://arxiv.org/abs/1808.07217">post-local SGD</a>,
 This optimizer runs local optimizer at every step.
 After the warm-up stage, it averages parameters periodically afer the local optimizer is applied.</p>
@@ -750,7 +750,7 @@ <h1>Distributed Optimizers<a class="headerlink" href="#distributed-optimizers" t
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.optim.PostLocalSGDOptimizer.load_state_dict">
-<span class="sig-name descname"><span class="pre">load_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state_dict</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/optim/post_localSGD_optimizer.html#PostLocalSGDOptimizer.load_state_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/optim/post_localSGD_optimizer.py#L80"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.optim.PostLocalSGDOptimizer.load_state_dict" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">load_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state_dict</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/optim/post_localSGD_optimizer.html#PostLocalSGDOptimizer.load_state_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/optim/post_localSGD_optimizer.py#L80"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.optim.PostLocalSGDOptimizer.load_state_dict" title="Permalink to this definition">¶</a></dt>
 <dd><p>This is the same as <a class="reference internal" href="optim.html#torch.optim.Optimizer" title="torch.optim.Optimizer"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.optim.Optimizer</span></code></a> <a class="reference internal" href="#torch.distributed.optim.PostLocalSGDOptimizer.load_state_dict" title="torch.distributed.optim.PostLocalSGDOptimizer.load_state_dict"><code class="xref py py-meth docutils literal notranslate"><span class="pre">load_state_dict()</span></code></a>,
 but also restores model averager’s step value to the one
 saved in the provided <code class="docutils literal notranslate"><span class="pre">state_dict</span></code>.</p>
@@ -760,7 +760,7 @@ <h1>Distributed Optimizers<a class="headerlink" href="#distributed-optimizers" t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.optim.PostLocalSGDOptimizer.state_dict">
-<span class="sig-name descname"><span class="pre">state_dict</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/optim/post_localSGD_optimizer.html#PostLocalSGDOptimizer.state_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/optim/post_localSGD_optimizer.py#L70"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.optim.PostLocalSGDOptimizer.state_dict" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">state_dict</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/optim/post_localSGD_optimizer.html#PostLocalSGDOptimizer.state_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/optim/post_localSGD_optimizer.py#L70"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.optim.PostLocalSGDOptimizer.state_dict" title="Permalink to this definition">¶</a></dt>
 <dd><p>This is the same as <a class="reference internal" href="optim.html#torch.optim.Optimizer" title="torch.optim.Optimizer"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.optim.Optimizer</span></code></a> <a class="reference internal" href="#torch.distributed.optim.PostLocalSGDOptimizer.state_dict" title="torch.distributed.optim.PostLocalSGDOptimizer.state_dict"><code class="xref py py-meth docutils literal notranslate"><span class="pre">state_dict()</span></code></a>,
 but adds an extra entry to record model averager’s step to the checkpoint
 to ensure reload does not cause unnecessary warm up again.</p>
@@ -768,7 +768,7 @@ <h1>Distributed Optimizers<a class="headerlink" href="#distributed-optimizers" t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.optim.PostLocalSGDOptimizer.step">
-<span class="sig-name descname"><span class="pre">step</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/optim/post_localSGD_optimizer.html#PostLocalSGDOptimizer.step"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/optim/post_localSGD_optimizer.py#L99"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.optim.PostLocalSGDOptimizer.step" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">step</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/optim/post_localSGD_optimizer.html#PostLocalSGDOptimizer.step"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/optim/post_localSGD_optimizer.py#L99"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.optim.PostLocalSGDOptimizer.step" title="Permalink to this definition">¶</a></dt>
 <dd><p>Performs a single optimization step (parameter update).</p>
 </dd></dl>
 
@@ -776,7 +776,7 @@ <h1>Distributed Optimizers<a class="headerlink" href="#distributed-optimizers" t
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributed.optim.ZeroRedundancyOptimizer">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.optim.</span></span><span class="sig-name descname"><span class="pre">ZeroRedundancyOptimizer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">params</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">optimizer_class</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">process_group</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">parameters_as_bucket_view</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">overlap_with_ddp</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">defaults</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/optim/zero_redundancy_optimizer.html#ZeroRedundancyOptimizer"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/optim/zero_redundancy_optimizer.py#L282"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.optim.ZeroRedundancyOptimizer" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.optim.</span></span><span class="sig-name descname"><span class="pre">ZeroRedundancyOptimizer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">params</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">optimizer_class</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">process_group</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">parameters_as_bucket_view</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">overlap_with_ddp</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">defaults</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/optim/zero_redundancy_optimizer.html#ZeroRedundancyOptimizer"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/optim/zero_redundancy_optimizer.py#L282"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.optim.ZeroRedundancyOptimizer" title="Permalink to this definition">¶</a></dt>
 <dd><p>Wrap an arbitrary <a class="reference internal" href="optim.html#torch.optim.Optimizer" title="torch.optim.Optimizer"><code class="xref py py-class docutils literal notranslate"><span class="pre">optim.Optimizer</span></code></a> and shards its states across ranks in the group.</p>
 <p>The sharing is done as described by <a class="reference external" href="https://arxiv.org/abs/1910.02054">ZeRO</a>.</p>
 <p>The local optimizer instance in each rank is only
@@ -866,7 +866,7 @@ <h1>Distributed Optimizers<a class="headerlink" href="#distributed-optimizers" t
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.optim.ZeroRedundancyOptimizer.add_param_group">
-<span class="sig-name descname"><span class="pre">add_param_group</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">param_group</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/optim/zero_redundancy_optimizer.html#ZeroRedundancyOptimizer.add_param_group"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/optim/zero_redundancy_optimizer.py#L460"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.optim.ZeroRedundancyOptimizer.add_param_group" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">add_param_group</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">param_group</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/optim/zero_redundancy_optimizer.html#ZeroRedundancyOptimizer.add_param_group"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/optim/zero_redundancy_optimizer.py#L460"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.optim.ZeroRedundancyOptimizer.add_param_group" title="Permalink to this definition">¶</a></dt>
 <dd><p>Add a parameter group to the <code class="xref py py-class docutils literal notranslate"><span class="pre">Optimizer</span></code> ‘s <code class="docutils literal notranslate"><span class="pre">param_groups</span></code>.</p>
 <p>This can be useful when fine tuning a pre-trained network, as frozen
 layers can be made trainable and added to the <code class="xref py py-class docutils literal notranslate"><span class="pre">Optimizer</span></code> as
@@ -889,7 +889,7 @@ <h1>Distributed Optimizers<a class="headerlink" href="#distributed-optimizers" t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.optim.ZeroRedundancyOptimizer.consolidate_state_dict">
-<span class="sig-name descname"><span class="pre">consolidate_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">to</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/optim/zero_redundancy_optimizer.html#ZeroRedundancyOptimizer.consolidate_state_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/optim/zero_redundancy_optimizer.py#L506"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.optim.ZeroRedundancyOptimizer.consolidate_state_dict" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">consolidate_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">to</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/optim/zero_redundancy_optimizer.html#ZeroRedundancyOptimizer.consolidate_state_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/optim/zero_redundancy_optimizer.py#L506"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.optim.ZeroRedundancyOptimizer.consolidate_state_dict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Consolidate a list of <code class="docutils literal notranslate"><span class="pre">state_dict</span></code> s (one per rank) on the target rank.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -917,7 +917,7 @@ <h1>Distributed Optimizers<a class="headerlink" href="#distributed-optimizers" t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.optim.ZeroRedundancyOptimizer.join_hook">
-<span class="sig-name descname"><span class="pre">join_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/optim/zero_redundancy_optimizer.html#ZeroRedundancyOptimizer.join_hook"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/optim/zero_redundancy_optimizer.py#L1121"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.optim.ZeroRedundancyOptimizer.join_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">join_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/optim/zero_redundancy_optimizer.html#ZeroRedundancyOptimizer.join_hook"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/optim/zero_redundancy_optimizer.py#L1121"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.optim.ZeroRedundancyOptimizer.join_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the ZeRO join hook.</p>
 <p>It enables training on uneven inputs by
 shadowing the collective communications in the optimizer step.</p>
@@ -942,7 +942,7 @@ <h1>Distributed Optimizers<a class="headerlink" href="#distributed-optimizers" t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.optim.ZeroRedundancyOptimizer.load_state_dict">
-<span class="sig-name descname"><span class="pre">load_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state_dict</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/optim/zero_redundancy_optimizer.html#ZeroRedundancyOptimizer.load_state_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/optim/zero_redundancy_optimizer.py#L1151"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.optim.ZeroRedundancyOptimizer.load_state_dict" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">load_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state_dict</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/optim/zero_redundancy_optimizer.html#ZeroRedundancyOptimizer.load_state_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/optim/zero_redundancy_optimizer.py#L1151"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.optim.ZeroRedundancyOptimizer.load_state_dict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Load the state pertaining to the given rank from the input <code class="docutils literal notranslate"><span class="pre">state_dict</span></code>, updating the local optimizer as needed.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -961,7 +961,7 @@ <h1>Distributed Optimizers<a class="headerlink" href="#distributed-optimizers" t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.optim.ZeroRedundancyOptimizer.state_dict">
-<span class="sig-name descname"><span class="pre">state_dict</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/optim/zero_redundancy_optimizer.html#ZeroRedundancyOptimizer.state_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/optim/zero_redundancy_optimizer.py#L1189"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.optim.ZeroRedundancyOptimizer.state_dict" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">state_dict</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/optim/zero_redundancy_optimizer.html#ZeroRedundancyOptimizer.state_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/optim/zero_redundancy_optimizer.py#L1189"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.optim.ZeroRedundancyOptimizer.state_dict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the last global optimizer state known to this rank.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Raises</dt>
@@ -980,7 +980,7 @@ <h1>Distributed Optimizers<a class="headerlink" href="#distributed-optimizers" t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.optim.ZeroRedundancyOptimizer.step">
-<span class="sig-name descname"><span class="pre">step</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">closure</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/optim/zero_redundancy_optimizer.html#ZeroRedundancyOptimizer.step"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/optim/zero_redundancy_optimizer.py#L1090"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.optim.ZeroRedundancyOptimizer.step" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">step</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">closure</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/optim/zero_redundancy_optimizer.html#ZeroRedundancyOptimizer.step"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/optim/zero_redundancy_optimizer.py#L1090"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.optim.ZeroRedundancyOptimizer.step" title="Permalink to this definition">¶</a></dt>
 <dd><p>Perform a single optimizer step and syncs parameters across all ranks.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/distributed.pipelining.html b/main/distributed.pipelining.html
index 08b2bf5fcce..5529f673fcf 100644
--- a/main/distributed.pipelining.html
+++ b/main/distributed.pipelining.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -976,7 +976,7 @@ <h3>Model Split APIs<a class="headerlink" href="#model-split-apis" title="Permal
 <p>The following set of APIs transform your model into a pipeline representation.</p>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributed.pipelining.SplitPoint">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.pipelining.</span></span><span class="sig-name descname"><span class="pre">SplitPoint</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/pipelining/_IR.html#SplitPoint"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/pipelining/_IR.py#L1145"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.pipelining.SplitPoint" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.pipelining.</span></span><span class="sig-name descname"><span class="pre">SplitPoint</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/pipelining/_IR.html#SplitPoint"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/pipelining/_IR.py#L1145"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.pipelining.SplitPoint" title="Permalink to this definition">¶</a></dt>
 <dd><p>Enum representing the points at which a split can occur in the execution of a submodule.
 :ivar BEGINNING: Represents adding a split point <em>before</em> the execution of a certain submodule in the <cite>forward</cite> function.
 :ivar END: Represents adding a split point <em>after</em> the execution of a certain submodule in the <cite>forward</cite> function.</p>
@@ -984,7 +984,7 @@ <h3>Model Split APIs<a class="headerlink" href="#model-split-apis" title="Permal
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.distributed.pipelining.pipeline">
-<span class="sig-prename descclassname"><span class="pre">torch.distributed.pipelining.</span></span><span class="sig-name descname"><span class="pre">pipeline</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mb_args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mb_kwargs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">split_spec</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">split_policy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/pipelining/_IR.html#pipeline"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/pipelining/_IR.py#L1200"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.pipelining.pipeline" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributed.pipelining.</span></span><span class="sig-name descname"><span class="pre">pipeline</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mb_args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mb_kwargs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">split_spec</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">split_policy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/pipelining/_IR.html#pipeline"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/pipelining/_IR.py#L1200"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.pipelining.pipeline" title="Permalink to this definition">¶</a></dt>
 <dd><p>Split a module based on a specification.</p>
 <p>See <cite>Pipe</cite> for more details.</p>
 <dl class="field-list simple">
@@ -1005,14 +1005,14 @@ <h3>Model Split APIs<a class="headerlink" href="#model-split-apis" title="Permal
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributed.pipelining.Pipe">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.pipelining.</span></span><span class="sig-name descname"><span class="pre">Pipe</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">split_gm</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_stages</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">has_loss_and_backward</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">loss_spec</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/pipelining/_IR.html#Pipe"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/pipelining/_IR.py#L525"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.pipelining.Pipe" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.pipelining.</span></span><span class="sig-name descname"><span class="pre">Pipe</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">split_gm</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_stages</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">has_loss_and_backward</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">loss_spec</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/pipelining/_IR.html#Pipe"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/pipelining/_IR.py#L525"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.pipelining.Pipe" title="Permalink to this definition">¶</a></dt>
 <dd><dl class="field-list simple">
 </dl>
 </dd></dl>
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.distributed.pipelining.pipe_split">
-<span class="sig-prename descclassname"><span class="pre">torch.distributed.pipelining.</span></span><span class="sig-name descname"><span class="pre">pipe_split</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/pipelining/_IR.html#pipe_split"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/pipelining/_IR.py#L327"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.pipelining.pipe_split" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributed.pipelining.</span></span><span class="sig-name descname"><span class="pre">pipe_split</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/pipelining/_IR.html#pipe_split"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/pipelining/_IR.py#L327"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.pipelining.pipe_split" title="Permalink to this definition">¶</a></dt>
 <dd><p>pipe_split is a special operator that is used to mark the boundary between
 stages in a module. It is used to split the module into stages. It is a
 no-op if your annotated module is run eagerly.</p>
@@ -1033,7 +1033,7 @@ <h3>Model Split APIs<a class="headerlink" href="#model-split-apis" title="Permal
 <span id="microbatch-utilities"></span><h3>Microbatch Utilities<a class="headerlink" href="#module-torch.distributed.pipelining.microbatch" title="Permalink to this heading">¶</a></h3>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributed.pipelining.microbatch.TensorChunkSpec">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.pipelining.microbatch.</span></span><span class="sig-name descname"><span class="pre">TensorChunkSpec</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">split_dim</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/pipelining/microbatch.html#TensorChunkSpec"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/pipelining/microbatch.py#L56"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.pipelining.microbatch.TensorChunkSpec" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.pipelining.microbatch.</span></span><span class="sig-name descname"><span class="pre">TensorChunkSpec</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">split_dim</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/pipelining/microbatch.html#TensorChunkSpec"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/pipelining/microbatch.py#L56"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.pipelining.microbatch.TensorChunkSpec" title="Permalink to this definition">¶</a></dt>
 <dd><p>Class used to specify chunking of inputs</p>
 <dl class="field-list simple">
 </dl>
@@ -1041,7 +1041,7 @@ <h3>Model Split APIs<a class="headerlink" href="#model-split-apis" title="Permal
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.distributed.pipelining.microbatch.split_args_kwargs_into_chunks">
-<span class="sig-prename descclassname"><span class="pre">torch.distributed.pipelining.microbatch.</span></span><span class="sig-name descname"><span class="pre">split_args_kwargs_into_chunks</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kwargs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">chunks</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args_chunk_spec</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kwargs_chunk_spec</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/pipelining/microbatch.html#split_args_kwargs_into_chunks"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/pipelining/microbatch.py#L244"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.pipelining.microbatch.split_args_kwargs_into_chunks" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributed.pipelining.microbatch.</span></span><span class="sig-name descname"><span class="pre">split_args_kwargs_into_chunks</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kwargs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">chunks</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args_chunk_spec</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kwargs_chunk_spec</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/pipelining/microbatch.html#split_args_kwargs_into_chunks"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/pipelining/microbatch.py#L244"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.pipelining.microbatch.split_args_kwargs_into_chunks" title="Permalink to this definition">¶</a></dt>
 <dd><p>Given a sequence of args and kwargs, split them into a number of chunks
 according to  their respective chunking specs.</p>
 <dl class="field-list simple">
@@ -1066,7 +1066,7 @@ <h3>Model Split APIs<a class="headerlink" href="#model-split-apis" title="Permal
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.distributed.pipelining.microbatch.merge_chunks">
-<span class="sig-prename descclassname"><span class="pre">torch.distributed.pipelining.microbatch.</span></span><span class="sig-name descname"><span class="pre">merge_chunks</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">chunks</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">chunk_spec</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/pipelining/microbatch.html#merge_chunks"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/pipelining/microbatch.py#L349"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.pipelining.microbatch.merge_chunks" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributed.pipelining.microbatch.</span></span><span class="sig-name descname"><span class="pre">merge_chunks</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">chunks</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">chunk_spec</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/pipelining/microbatch.html#merge_chunks"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/pipelining/microbatch.py#L349"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.pipelining.microbatch.merge_chunks" title="Permalink to this definition">¶</a></dt>
 <dd><p>Given a list of chunks, merge them into a single value according to
 the chunk spec.</p>
 <dl class="field-list simple">
@@ -1090,7 +1090,7 @@ <h3>Model Split APIs<a class="headerlink" href="#model-split-apis" title="Permal
 <span id="pipeline-stages"></span><h3>Pipeline Stages<a class="headerlink" href="#module-torch.distributed.pipelining.stage" title="Permalink to this heading">¶</a></h3>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributed.pipelining.stage.PipelineStage">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.pipelining.stage.</span></span><span class="sig-name descname"><span class="pre">PipelineStage</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">submodule</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stage_index</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_stages</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dw_builder</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/pipelining/stage.html#PipelineStage"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/pipelining/stage.py#L1244"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.pipelining.stage.PipelineStage" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.pipelining.stage.</span></span><span class="sig-name descname"><span class="pre">PipelineStage</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">submodule</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stage_index</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_stages</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dw_builder</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/pipelining/stage.html#PipelineStage"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/pipelining/stage.py#L1244"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.pipelining.stage.PipelineStage" title="Permalink to this definition">¶</a></dt>
 <dd><p>A class representing a pipeline stage in a pipeline parallelism setup.</p>
 <p>PipelineStage assumes sequential partitioning of the model, i.e. the model is split into chunks where outputs from
 one chunk feed into inputs of the next chunk, with no skip connections.</p>
@@ -1115,7 +1115,7 @@ <h3>Model Split APIs<a class="headerlink" href="#model-split-apis" title="Permal
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.distributed.pipelining.stage.build_stage">
-<span class="sig-prename descclassname"><span class="pre">torch.distributed.pipelining.stage.</span></span><span class="sig-name descname"><span class="pre">build_stage</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">stage_module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stage_index</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pipe_info</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/pipelining/stage.html#build_stage"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/pipelining/stage.py#L1214"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.pipelining.stage.build_stage" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributed.pipelining.stage.</span></span><span class="sig-name descname"><span class="pre">build_stage</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">stage_module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stage_index</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pipe_info</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/pipelining/stage.html#build_stage"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/pipelining/stage.py#L1214"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.pipelining.stage.build_stage" title="Permalink to this definition">¶</a></dt>
 <dd><p>Create a pipeline stage given a stage_module to be wrapped by this stage
 and pipeline information.</p>
 <dl class="field-list simple">
@@ -1142,7 +1142,7 @@ <h3>Model Split APIs<a class="headerlink" href="#model-split-apis" title="Permal
 <span id="pipeline-schedules"></span><h3>Pipeline Schedules<a class="headerlink" href="#module-torch.distributed.pipelining.schedules" title="Permalink to this heading">¶</a></h3>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributed.pipelining.schedules.ScheduleGPipe">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.pipelining.schedules.</span></span><span class="sig-name descname"><span class="pre">ScheduleGPipe</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">stage</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_microbatches</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">loss_fn</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args_chunk_spec</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kwargs_chunk_spec</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_merge_spec</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale_grads</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/pipelining/schedules.html#ScheduleGPipe"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/pipelining/schedules.py#L571"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.pipelining.schedules.ScheduleGPipe" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.pipelining.schedules.</span></span><span class="sig-name descname"><span class="pre">ScheduleGPipe</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">stage</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_microbatches</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">loss_fn</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args_chunk_spec</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kwargs_chunk_spec</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_merge_spec</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale_grads</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/pipelining/schedules.html#ScheduleGPipe"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/pipelining/schedules.py#L571"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.pipelining.schedules.ScheduleGPipe" title="Permalink to this definition">¶</a></dt>
 <dd><p>The GPipe schedule.
 Will go through all the microbatches in a fill-drain manner.</p>
 <dl class="field-list simple">
@@ -1151,7 +1151,7 @@ <h3>Model Split APIs<a class="headerlink" href="#model-split-apis" title="Permal
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributed.pipelining.schedules.Schedule1F1B">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.pipelining.schedules.</span></span><span class="sig-name descname"><span class="pre">Schedule1F1B</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">stage</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_microbatches</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">loss_fn</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args_chunk_spec</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kwargs_chunk_spec</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_merge_spec</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale_grads</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/pipelining/schedules.html#Schedule1F1B"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/pipelining/schedules.py#L662"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.pipelining.schedules.Schedule1F1B" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.pipelining.schedules.</span></span><span class="sig-name descname"><span class="pre">Schedule1F1B</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">stage</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_microbatches</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">loss_fn</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args_chunk_spec</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kwargs_chunk_spec</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_merge_spec</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale_grads</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/pipelining/schedules.html#Schedule1F1B"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/pipelining/schedules.py#L662"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.pipelining.schedules.Schedule1F1B" title="Permalink to this definition">¶</a></dt>
 <dd><p>The 1F1B schedule.
 Will perform one forward and one backward on the microbatches in steady state.</p>
 <dl class="field-list simple">
@@ -1160,7 +1160,7 @@ <h3>Model Split APIs<a class="headerlink" href="#model-split-apis" title="Permal
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributed.pipelining.schedules.ScheduleInterleaved1F1B">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.pipelining.schedules.</span></span><span class="sig-name descname"><span class="pre">ScheduleInterleaved1F1B</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">stages</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_microbatches</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">loss_fn</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args_chunk_spec</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kwargs_chunk_spec</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_merge_spec</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale_grads</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/pipelining/schedules.html#ScheduleInterleaved1F1B"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/pipelining/schedules.py#L1933"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.pipelining.schedules.ScheduleInterleaved1F1B" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.pipelining.schedules.</span></span><span class="sig-name descname"><span class="pre">ScheduleInterleaved1F1B</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">stages</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_microbatches</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">loss_fn</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args_chunk_spec</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kwargs_chunk_spec</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_merge_spec</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale_grads</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/pipelining/schedules.html#ScheduleInterleaved1F1B"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/pipelining/schedules.py#L1933"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.pipelining.schedules.ScheduleInterleaved1F1B" title="Permalink to this definition">¶</a></dt>
 <dd><p>The Interleaved 1F1B schedule.
 See <a class="reference external" href="https://arxiv.org/pdf/2104.04473">https://arxiv.org/pdf/2104.04473</a> for details.
 Will perform one forward and one backward on the microbatches in steady
@@ -1181,7 +1181,7 @@ <h3>Model Split APIs<a class="headerlink" href="#model-split-apis" title="Permal
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributed.pipelining.schedules.ScheduleLoopedBFS">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.pipelining.schedules.</span></span><span class="sig-name descname"><span class="pre">ScheduleLoopedBFS</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">stages</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_microbatches</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">loss_fn</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_merge_spec</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale_grads</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/pipelining/schedules.html#ScheduleLoopedBFS"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/pipelining/schedules.py#L1732"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.pipelining.schedules.ScheduleLoopedBFS" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.pipelining.schedules.</span></span><span class="sig-name descname"><span class="pre">ScheduleLoopedBFS</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">stages</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_microbatches</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">loss_fn</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_merge_spec</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale_grads</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/pipelining/schedules.html#ScheduleLoopedBFS"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/pipelining/schedules.py#L1732"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.pipelining.schedules.ScheduleLoopedBFS" title="Permalink to this definition">¶</a></dt>
 <dd><p>Breadth-First Pipeline Parallelism.
 See <a class="reference external" href="https://arxiv.org/abs/2211.05953">https://arxiv.org/abs/2211.05953</a> for details.
 Simliar to Interleaved 1F1B, Looped BFS supports multiple stages per rank.
@@ -1194,7 +1194,7 @@ <h3>Model Split APIs<a class="headerlink" href="#model-split-apis" title="Permal
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributed.pipelining.schedules.ScheduleInterleavedZeroBubble">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.pipelining.schedules.</span></span><span class="sig-name descname"><span class="pre">ScheduleInterleavedZeroBubble</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">stages</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_microbatches</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">loss_fn</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args_chunk_spec</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kwargs_chunk_spec</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_merge_spec</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale_grads</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/pipelining/schedules.html#ScheduleInterleavedZeroBubble"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/pipelining/schedules.py#L2049"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.pipelining.schedules.ScheduleInterleavedZeroBubble" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.pipelining.schedules.</span></span><span class="sig-name descname"><span class="pre">ScheduleInterleavedZeroBubble</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">stages</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_microbatches</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">loss_fn</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args_chunk_spec</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kwargs_chunk_spec</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_merge_spec</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale_grads</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/pipelining/schedules.html#ScheduleInterleavedZeroBubble"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/pipelining/schedules.py#L2049"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.pipelining.schedules.ScheduleInterleavedZeroBubble" title="Permalink to this definition">¶</a></dt>
 <dd><p>The Interleaved Zero Bubble schedule.
 See <a class="reference external" href="https://arxiv.org/pdf/2401.10241">https://arxiv.org/pdf/2401.10241</a> for details.
 Will perform one forward and one backward on inputs for the microbatches in steady
@@ -1207,7 +1207,7 @@ <h3>Model Split APIs<a class="headerlink" href="#model-split-apis" title="Permal
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributed.pipelining.schedules.ScheduleZBVZeroBubble">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.pipelining.schedules.</span></span><span class="sig-name descname"><span class="pre">ScheduleZBVZeroBubble</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">stages</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_microbatches</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">loss_fn</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args_chunk_spec</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kwargs_chunk_spec</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_merge_spec</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stage_index_to_group_rank</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale_grads</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/pipelining/schedules.html#ScheduleZBVZeroBubble"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/pipelining/schedules.py#L2245"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.pipelining.schedules.ScheduleZBVZeroBubble" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.pipelining.schedules.</span></span><span class="sig-name descname"><span class="pre">ScheduleZBVZeroBubble</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">stages</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_microbatches</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">loss_fn</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args_chunk_spec</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kwargs_chunk_spec</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_merge_spec</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stage_index_to_group_rank</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale_grads</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/pipelining/schedules.html#ScheduleZBVZeroBubble"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/pipelining/schedules.py#L2245"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.pipelining.schedules.ScheduleZBVZeroBubble" title="Permalink to this definition">¶</a></dt>
 <dd><p>The Zero Bubble schedule (ZBV variant).
 See <a class="reference external" href="https://arxiv.org/pdf/2401.10241">https://arxiv.org/pdf/2401.10241</a> Section 6 for details.</p>
 <p>This schedules requires exactly two stages per rank.</p>
@@ -1223,7 +1223,7 @@ <h3>Model Split APIs<a class="headerlink" href="#model-split-apis" title="Permal
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributed.pipelining.schedules.PipelineScheduleSingle">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.pipelining.schedules.</span></span><span class="sig-name descname"><span class="pre">PipelineScheduleSingle</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">stage</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_microbatches</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">loss_fn</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args_chunk_spec</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kwargs_chunk_spec</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_merge_spec</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale_grads</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/pipelining/schedules.html#PipelineScheduleSingle"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/pipelining/schedules.py#L437"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.pipelining.schedules.PipelineScheduleSingle" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.pipelining.schedules.</span></span><span class="sig-name descname"><span class="pre">PipelineScheduleSingle</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">stage</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_microbatches</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">loss_fn</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args_chunk_spec</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kwargs_chunk_spec</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_merge_spec</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale_grads</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/pipelining/schedules.html#PipelineScheduleSingle"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/pipelining/schedules.py#L437"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.pipelining.schedules.PipelineScheduleSingle" title="Permalink to this definition">¶</a></dt>
 <dd><p>Base class for single-stage schedules.
 Implements the <cite>step</cite> method.
 Derived classes should implement <cite>_step_microbatches</cite>.</p>
@@ -1234,7 +1234,7 @@ <h3>Model Split APIs<a class="headerlink" href="#model-split-apis" title="Permal
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.pipelining.schedules.PipelineScheduleSingle.step">
-<span class="sig-name descname"><span class="pre">step</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">losses</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/pipelining/schedules.html#PipelineScheduleSingle.step"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/pipelining/schedules.py#L486"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.pipelining.schedules.PipelineScheduleSingle.step" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">step</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">losses</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/pipelining/schedules.html#PipelineScheduleSingle.step"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/pipelining/schedules.py#L486"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.pipelining.schedules.PipelineScheduleSingle.step" title="Permalink to this definition">¶</a></dt>
 <dd><p>Run one iteration of the pipeline schedule with <em>whole-batch</em> input.
 Will chunk the input into microbatches automatically, and go through the
 microbatches according to the schedule implementation.</p>
@@ -1250,7 +1250,7 @@ <h3>Model Split APIs<a class="headerlink" href="#model-split-apis" title="Permal
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributed.pipelining.schedules.PipelineScheduleMulti">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.pipelining.schedules.</span></span><span class="sig-name descname"><span class="pre">PipelineScheduleMulti</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">stages</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_microbatches</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">loss_fn</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args_chunk_spec</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kwargs_chunk_spec</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_merge_spec</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stage_index_to_group_rank</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_full_backward</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale_grads</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/pipelining/schedules.html#PipelineScheduleMulti"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/pipelining/schedules.py#L1089"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.pipelining.schedules.PipelineScheduleMulti" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.pipelining.schedules.</span></span><span class="sig-name descname"><span class="pre">PipelineScheduleMulti</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">stages</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_microbatches</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">loss_fn</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args_chunk_spec</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kwargs_chunk_spec</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_merge_spec</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stage_index_to_group_rank</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_full_backward</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale_grads</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/pipelining/schedules.html#PipelineScheduleMulti"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/pipelining/schedules.py#L1089"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.pipelining.schedules.PipelineScheduleMulti" title="Permalink to this definition">¶</a></dt>
 <dd><p>Base class for multi-stage schedules.
 Implements the <cite>step</cite> method.</p>
 <p>Gradients are scaled by num_microbatches depending on the <cite>scale_grads</cite> argument, defaulting to True.  This setting
@@ -1260,7 +1260,7 @@ <h3>Model Split APIs<a class="headerlink" href="#model-split-apis" title="Permal
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.pipelining.schedules.PipelineScheduleMulti.step">
-<span class="sig-name descname"><span class="pre">step</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">losses</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/pipelining/schedules.html#PipelineScheduleMulti.step"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/pipelining/schedules.py#L1192"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.pipelining.schedules.PipelineScheduleMulti.step" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">step</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">losses</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/pipelining/schedules.html#PipelineScheduleMulti.step"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/pipelining/schedules.py#L1192"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.pipelining.schedules.PipelineScheduleMulti.step" title="Permalink to this definition">¶</a></dt>
 <dd><p>Run one iteration of the pipeline schedule with <em>whole-batch</em> input.
 Will chunk the input into microbatches automatically, and go through the
 microbatches according to the schedule implementation.</p>
diff --git a/main/distributed.tensor.html b/main/distributed.tensor.html
index ffecb963977..54154f93079 100644
--- a/main/distributed.tensor.html
+++ b/main/distributed.tensor.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -672,7 +672,7 @@ <h3>DTensor Class APIs<a class="headerlink" href="#dtensor-class-apis" title="Pe
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.tensor.DTensor.__create_chunk_list__">
-<span class="sig-name descname"><span class="pre">__create_chunk_list__</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/tensor/_api.html#DTensor.__create_chunk_list__"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/tensor/_api.py#L610"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.tensor.DTensor.__create_chunk_list__" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">__create_chunk_list__</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/tensor/_api.html#DTensor.__create_chunk_list__"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/tensor/_api.py#L610"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.tensor.DTensor.__create_chunk_list__" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return a list of ChunkStorageMetadata, which is a dataclass that describes the size/offset of the local shard/replica
 on current rank. For DTensor, each rank will have a single local shard/replica, so the returned list usually only
 has one element.</p>
@@ -686,7 +686,7 @@ <h3>DTensor Class APIs<a class="headerlink" href="#dtensor-class-apis" title="Pe
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.tensor.DTensor.from_local">
-<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_local</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">local_tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device_mesh</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">placements</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">run_check</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shape</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/tensor/_api.html#DTensor.from_local"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/tensor/_api.py#L354"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.tensor.DTensor.from_local" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_local</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">local_tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device_mesh</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">placements</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">run_check</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shape</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/tensor/_api.html#DTensor.from_local"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/tensor/_api.py#L354"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.tensor.DTensor.from_local" title="Permalink to this definition">¶</a></dt>
 <dd><p>Create a <a class="reference internal" href="#torch.distributed.tensor.DTensor" title="torch.distributed.tensor.DTensor"><code class="xref py py-class docutils literal notranslate"><span class="pre">DTensor</span></code></a> from a local torch.Tensor on each rank
 according to the <code class="docutils literal notranslate"><span class="pre">device_mesh</span></code> and <code class="docutils literal notranslate"><span class="pre">placements</span></code> specified.</p>
 <dl class="field-list simple">
@@ -741,7 +741,7 @@ <h3>DTensor Class APIs<a class="headerlink" href="#dtensor-class-apis" title="Pe
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.tensor.DTensor.full_tensor">
-<span class="sig-name descname"><span class="pre">full_tensor</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">grad_placements</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/tensor/_api.html#DTensor.full_tensor"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/tensor/_api.py#L548"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.tensor.DTensor.full_tensor" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">full_tensor</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">grad_placements</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/tensor/_api.html#DTensor.full_tensor"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/tensor/_api.py#L548"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.tensor.DTensor.full_tensor" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the full tensor of this DTensor. It will perform necessary collectives
 to gather the local tensors from other ranks in its DeviceMesh and concatenate
 them together. It’s a syntatic sugar of the following code:</p>
@@ -772,7 +772,7 @@ <h3>DTensor Class APIs<a class="headerlink" href="#dtensor-class-apis" title="Pe
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.tensor.DTensor.redistribute">
-<span class="sig-name descname"><span class="pre">redistribute</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device_mesh</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">placements</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">async_op</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/tensor/_api.html#DTensor.redistribute"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/tensor/_api.py#L476"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.tensor.DTensor.redistribute" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">redistribute</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device_mesh</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">placements</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">async_op</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/tensor/_api.html#DTensor.redistribute"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/tensor/_api.py#L476"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.tensor.DTensor.redistribute" title="Permalink to this definition">¶</a></dt>
 <dd><p><code class="docutils literal notranslate"><span class="pre">redistribute</span></code> performs necessary collective operations that redistribute the current
 DTensor from its current placements to a new placements, or from is current DeviceMesh
 to a new DeviceMesh. i.e. we can turn a Sharded DTensor to a Replicated DTensor by
@@ -825,7 +825,7 @@ <h3>DTensor Class APIs<a class="headerlink" href="#dtensor-class-apis" title="Pe
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.tensor.DTensor.to_local">
-<span class="sig-name descname"><span class="pre">to_local</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">grad_placements</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/tensor/_api.html#DTensor.to_local"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/tensor/_api.py#L439"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.tensor.DTensor.to_local" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">to_local</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">grad_placements</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/tensor/_api.html#DTensor.to_local"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/tensor/_api.py#L439"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.tensor.DTensor.to_local" title="Permalink to this definition">¶</a></dt>
 <dd><p>Get the local tensor of this DTensor on its current rank. For sharding it returns
 a local shard of the logical tensor view, for replication it returns the replica on
 its current rank.</p>
@@ -893,7 +893,7 @@ <h3>DeviceMesh as the distributed communicator<a class="headerlink" href="#devic
 <p>DTensor supports the following types of <a class="reference internal" href="#torch.distributed.tensor.placement_types.Placement" title="torch.distributed.tensor.placement_types.Placement"><code class="xref py py-class docutils literal notranslate"><span class="pre">Placement</span></code></a> on each <code class="xref py py-class docutils literal notranslate"><span class="pre">DeviceMesh</span></code> dimension:</p>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributed.tensor.placement_types.Shard">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.tensor.placement_types.</span></span><span class="sig-name descname"><span class="pre">Shard</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dim</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/tensor/placement_types.html#Shard"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/tensor/placement_types.py#L48"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.tensor.placement_types.Shard" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.tensor.placement_types.</span></span><span class="sig-name descname"><span class="pre">Shard</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dim</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/tensor/placement_types.html#Shard"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/tensor/placement_types.py#L48"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.tensor.placement_types.Shard" title="Permalink to this definition">¶</a></dt>
 <dd><p>The <code class="docutils literal notranslate"><span class="pre">Shard(dim)</span></code> placement describes the DTensor sharding on tensor dimension
 <code class="docutils literal notranslate"><span class="pre">dim</span></code> over a corresponding <code class="docutils literal notranslate"><span class="pre">DeviceMesh</span></code> dimension, where each rank on the
 DeviceMesh dimension only holds a shard/piece of the global Tensor. The
@@ -921,7 +921,7 @@ <h3>DeviceMesh as the distributed communicator<a class="headerlink" href="#devic
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributed.tensor.placement_types.Replicate">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.tensor.placement_types.</span></span><span class="sig-name descname"><span class="pre">Replicate</span></span><a class="reference internal" href="_modules/torch/distributed/tensor/placement_types.html#Replicate"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/tensor/placement_types.py#L543"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.tensor.placement_types.Replicate" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.tensor.placement_types.</span></span><span class="sig-name descname"><span class="pre">Replicate</span></span><a class="reference internal" href="_modules/torch/distributed/tensor/placement_types.html#Replicate"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/tensor/placement_types.py#L543"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.tensor.placement_types.Replicate" title="Permalink to this definition">¶</a></dt>
 <dd><p>The <code class="docutils literal notranslate"><span class="pre">Replicate()</span></code> placement describes the DTensor replicating on a corresponding
 <code class="docutils literal notranslate"><span class="pre">DeviceMesh</span></code> dimension, where each rank on the DeviceMesh dimension holds a
 replica of the global Tensor. The <code class="docutils literal notranslate"><span class="pre">Replicate</span></code> placement can be used by all
@@ -932,7 +932,7 @@ <h3>DeviceMesh as the distributed communicator<a class="headerlink" href="#devic
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributed.tensor.placement_types.Partial">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.tensor.placement_types.</span></span><span class="sig-name descname"><span class="pre">Partial</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">reduce_op</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'sum'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/tensor/placement_types.html#Partial"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/tensor/placement_types.py#L595"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.tensor.placement_types.Partial" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.tensor.placement_types.</span></span><span class="sig-name descname"><span class="pre">Partial</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">reduce_op</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'sum'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/tensor/placement_types.html#Partial"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/tensor/placement_types.py#L595"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.tensor.placement_types.Partial" title="Permalink to this definition">¶</a></dt>
 <dd><p>The <code class="docutils literal notranslate"><span class="pre">Partial(reduce_op)</span></code> placement describes the DTensor that is pending
 reduction on a specified <code class="docutils literal notranslate"><span class="pre">DeviceMesh</span></code> dimension, where each rank on the
 DeviceMesh dimension holds the partial value of the global Tensor. User can
@@ -961,7 +961,7 @@ <h3>DeviceMesh as the distributed communicator<a class="headerlink" href="#devic
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributed.tensor.placement_types.Placement">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.tensor.placement_types.</span></span><span class="sig-name descname"><span class="pre">Placement</span></span><a class="reference internal" href="_modules/torch/distributed/tensor/placement_types.html#Placement"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/tensor/placement_types.py#L23"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.tensor.placement_types.Placement" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.tensor.placement_types.</span></span><span class="sig-name descname"><span class="pre">Placement</span></span><a class="reference internal" href="_modules/torch/distributed/tensor/placement_types.html#Placement"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/tensor/placement_types.py#L23"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.tensor.placement_types.Placement" title="Permalink to this definition">¶</a></dt>
 <dd><p>The base class for the Placement type, where it describes how a DTensor is placed onto the
 <code class="docutils literal notranslate"><span class="pre">DeviceMesh</span></code>. <code class="docutils literal notranslate"><span class="pre">Placement</span></code> and <code class="docutils literal notranslate"><span class="pre">DeviceMesh</span></code> together could describe the DTensor Layout.
 It is the base class of the three main DTensor Placement types: <code class="docutils literal notranslate"><span class="pre">Shard</span></code>, <code class="docutils literal notranslate"><span class="pre">Replicate</span></code>,
@@ -969,7 +969,7 @@ <h3>DeviceMesh as the distributed communicator<a class="headerlink" href="#devic
 <p>This class is not meant to be used directly, mainly served as a typing stub.</p>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.tensor.placement_types.Placement.is_partial">
-<span class="sig-name descname"><span class="pre">is_partial</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/tensor/placement_types.html#Placement.is_partial"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/tensor/placement_types.py#L44"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.tensor.placement_types.Placement.is_partial" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">is_partial</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/tensor/placement_types.html#Placement.is_partial"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/tensor/placement_types.py#L44"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.tensor.placement_types.Placement.is_partial" title="Permalink to this definition">¶</a></dt>
 <dd><dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
 <dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)">bool</a></p>
@@ -979,7 +979,7 @@ <h3>DeviceMesh as the distributed communicator<a class="headerlink" href="#devic
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.tensor.placement_types.Placement.is_replicate">
-<span class="sig-name descname"><span class="pre">is_replicate</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/tensor/placement_types.html#Placement.is_replicate"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/tensor/placement_types.py#L41"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.tensor.placement_types.Placement.is_replicate" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">is_replicate</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/tensor/placement_types.html#Placement.is_replicate"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/tensor/placement_types.py#L41"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.tensor.placement_types.Placement.is_replicate" title="Permalink to this definition">¶</a></dt>
 <dd><dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
 <dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)">bool</a></p>
@@ -989,7 +989,7 @@ <h3>DeviceMesh as the distributed communicator<a class="headerlink" href="#devic
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.tensor.placement_types.Placement.is_shard">
-<span class="sig-name descname"><span class="pre">is_shard</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/tensor/placement_types.html#Placement.is_shard"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/tensor/placement_types.py#L34"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.tensor.placement_types.Placement.is_shard" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">is_shard</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/tensor/placement_types.html#Placement.is_shard"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/tensor/placement_types.py#L34"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.tensor.placement_types.Placement.is_shard" title="Permalink to this definition">¶</a></dt>
 <dd><dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
 <dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)">bool</a></p>
@@ -1029,7 +1029,7 @@ <h3>Create DTensor from a logical torch.Tensor<a class="headerlink" href="#creat
 <code class="docutils literal notranslate"><span class="pre">DTensor</span></code> s to comply with the single device semantic, which is critical for <strong>numerical correctness</strong>.</p>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.distributed.tensor.distribute_tensor">
-<span class="sig-prename descclassname"><span class="pre">torch.distributed.tensor.</span></span><span class="sig-name descname"><span class="pre">distribute_tensor</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device_mesh</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">placements</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">src_data_rank</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/tensor/_api.py#L641"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.tensor.distribute_tensor" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributed.tensor.</span></span><span class="sig-name descname"><span class="pre">distribute_tensor</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device_mesh</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">placements</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">src_data_rank</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/tensor/_api.py#L641"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.tensor.distribute_tensor" title="Permalink to this definition">¶</a></dt>
 <dd><p>Distribute a leaf <code class="docutils literal notranslate"><span class="pre">torch.Tensor</span></code> (i.e. nn.Parameter/buffers) to the <code class="docutils literal notranslate"><span class="pre">device_mesh</span></code> according
 to the <code class="docutils literal notranslate"><span class="pre">placements</span></code> specified. The rank of <code class="docutils literal notranslate"><span class="pre">device_mesh</span></code> and <code class="docutils literal notranslate"><span class="pre">placements</span></code> must be the
 same. The <code class="docutils literal notranslate"><span class="pre">tensor</span></code> to distribute is the logical or “global” tensor, and the API would use
@@ -1081,7 +1081,7 @@ <h3>Create DTensor from a logical torch.Tensor<a class="headerlink" href="#creat
 sharding on the <code class="xref py py-class docutils literal notranslate"><span class="pre">nn.Module</span></code> level</p>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.distributed.tensor.distribute_module">
-<span class="sig-prename descclassname"><span class="pre">torch.distributed.tensor.</span></span><span class="sig-name descname"><span class="pre">distribute_module</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device_mesh</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">partition_fn</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_fn</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_fn</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/tensor/_api.py#L834"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.tensor.distribute_module" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributed.tensor.</span></span><span class="sig-name descname"><span class="pre">distribute_module</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device_mesh</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">partition_fn</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_fn</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_fn</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/tensor/_api.py#L834"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.tensor.distribute_module" title="Permalink to this definition">¶</a></dt>
 <dd><p>This function expose three functions to control the parameters/inputs/outputs of the module:</p>
 <p>1. To perform sharding on the module before runtime execution by specifying the
 <code class="docutils literal notranslate"><span class="pre">partition_fn</span></code> (i.e. allow user to convert Module parameters to <a class="reference internal" href="#torch.distributed.tensor.DTensor" title="torch.distributed.tensor.DTensor"><code class="xref py py-class docutils literal notranslate"><span class="pre">DTensor</span></code></a>
@@ -1129,7 +1129,7 @@ <h3>DTensor Factory Functions<a class="headerlink" href="#dtensor-factory-functi
 specifying the <code class="xref py py-class docutils literal notranslate"><span class="pre">DeviceMesh</span></code> and <code class="xref py py-class docutils literal notranslate"><span class="pre">Placement</span></code> for the <a class="reference internal" href="#torch.distributed.tensor.DTensor" title="torch.distributed.tensor.DTensor"><code class="xref py py-class docutils literal notranslate"><span class="pre">DTensor</span></code></a> created:</p>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.distributed.tensor.zeros">
-<span class="sig-prename descclassname"><span class="pre">torch.distributed.tensor.</span></span><span class="sig-name descname"><span class="pre">zeros</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">requires_grad</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">layout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.strided</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device_mesh</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">placements</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/tensor/_api.py#L1266"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.tensor.zeros" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributed.tensor.</span></span><span class="sig-name descname"><span class="pre">zeros</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">requires_grad</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">layout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.strided</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device_mesh</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">placements</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/tensor/_api.py#L1266"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.tensor.zeros" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns a <a class="reference internal" href="#torch.distributed.tensor.DTensor" title="torch.distributed.tensor.DTensor"><code class="xref py py-class docutils literal notranslate"><span class="pre">DTensor</span></code></a> filled with the scalar value 0.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -1160,7 +1160,7 @@ <h3>DTensor Factory Functions<a class="headerlink" href="#dtensor-factory-functi
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.distributed.tensor.ones">
-<span class="sig-prename descclassname"><span class="pre">torch.distributed.tensor.</span></span><span class="sig-name descname"><span class="pre">ones</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">layout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.strided</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">requires_grad</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device_mesh</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">placements</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/tensor/_api.py#L1045"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.tensor.ones" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributed.tensor.</span></span><span class="sig-name descname"><span class="pre">ones</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">layout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.strided</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">requires_grad</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device_mesh</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">placements</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/tensor/_api.py#L1045"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.tensor.ones" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns a <a class="reference internal" href="#torch.distributed.tensor.DTensor" title="torch.distributed.tensor.DTensor"><code class="xref py py-class docutils literal notranslate"><span class="pre">DTensor</span></code></a> filled with the scalar value 1, with the shape defined
 by the variable argument <code class="docutils literal notranslate"><span class="pre">size</span></code>.</p>
 <dl class="field-list simple">
@@ -1192,7 +1192,7 @@ <h3>DTensor Factory Functions<a class="headerlink" href="#dtensor-factory-functi
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.distributed.tensor.empty">
-<span class="sig-prename descclassname"><span class="pre">torch.distributed.tensor.</span></span><span class="sig-name descname"><span class="pre">empty</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">layout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.strided</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">requires_grad</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device_mesh</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">placements</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/tensor/_api.py#L1088"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.tensor.empty" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributed.tensor.</span></span><span class="sig-name descname"><span class="pre">empty</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">layout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.strided</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">requires_grad</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device_mesh</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">placements</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/tensor/_api.py#L1088"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.tensor.empty" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns a <a class="reference internal" href="#torch.distributed.tensor.DTensor" title="torch.distributed.tensor.DTensor"><code class="xref py py-class docutils literal notranslate"><span class="pre">DTensor</span></code></a> filled with uninitialized data. The shape of the <a class="reference internal" href="#torch.distributed.tensor.DTensor" title="torch.distributed.tensor.DTensor"><code class="xref py py-class docutils literal notranslate"><span class="pre">DTensor</span></code></a>
 is defined by the variable argument <code class="docutils literal notranslate"><span class="pre">size</span></code>.</p>
 <dl class="field-list simple">
@@ -1223,7 +1223,7 @@ <h3>DTensor Factory Functions<a class="headerlink" href="#dtensor-factory-functi
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.distributed.tensor.full">
-<span class="sig-prename descclassname"><span class="pre">torch.distributed.tensor.</span></span><span class="sig-name descname"><span class="pre">full</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fill_value</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">layout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.strided</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">requires_grad</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device_mesh</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">placements</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/tensor/_api.py#L1131"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.tensor.full" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributed.tensor.</span></span><span class="sig-name descname"><span class="pre">full</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fill_value</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">layout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.strided</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">requires_grad</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device_mesh</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">placements</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/tensor/_api.py#L1131"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.tensor.full" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns a <a class="reference internal" href="#torch.distributed.tensor.DTensor" title="torch.distributed.tensor.DTensor"><code class="xref py py-class docutils literal notranslate"><span class="pre">DTensor</span></code></a> filled with <code class="docutils literal notranslate"><span class="pre">fill_value</span></code> according to <code class="docutils literal notranslate"><span class="pre">device_mesh</span></code> and
 <code class="docutils literal notranslate"><span class="pre">placements</span></code>, with the shape defined by the argument <code class="docutils literal notranslate"><span class="pre">size</span></code>.</p>
 <dl class="field-list simple">
@@ -1258,7 +1258,7 @@ <h3>DTensor Factory Functions<a class="headerlink" href="#dtensor-factory-functi
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.distributed.tensor.rand">
-<span class="sig-prename descclassname"><span class="pre">torch.distributed.tensor.</span></span><span class="sig-name descname"><span class="pre">rand</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">requires_grad</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">layout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.strided</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device_mesh</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">placements</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/tensor/_api.py#L1178"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.tensor.rand" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributed.tensor.</span></span><span class="sig-name descname"><span class="pre">rand</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">requires_grad</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">layout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.strided</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device_mesh</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">placements</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/tensor/_api.py#L1178"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.tensor.rand" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns a <a class="reference internal" href="#torch.distributed.tensor.DTensor" title="torch.distributed.tensor.DTensor"><code class="xref py py-class docutils literal notranslate"><span class="pre">DTensor</span></code></a> filled with random numbers from a uniform distribution
 on the interval <code class="docutils literal notranslate"><span class="pre">[0,</span> <span class="pre">1)</span></code>. The shape of the tensor is defined by the variable
 argument <code class="docutils literal notranslate"><span class="pre">size</span></code>.</p>
@@ -1291,7 +1291,7 @@ <h3>DTensor Factory Functions<a class="headerlink" href="#dtensor-factory-functi
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.distributed.tensor.randn">
-<span class="sig-prename descclassname"><span class="pre">torch.distributed.tensor.</span></span><span class="sig-name descname"><span class="pre">randn</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">requires_grad</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">layout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.strided</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device_mesh</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">placements</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/tensor/_api.py#L1222"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.tensor.randn" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributed.tensor.</span></span><span class="sig-name descname"><span class="pre">randn</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">requires_grad</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">layout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.strided</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device_mesh</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">placements</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/tensor/_api.py#L1222"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.tensor.randn" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns a <a class="reference internal" href="#torch.distributed.tensor.DTensor" title="torch.distributed.tensor.DTensor"><code class="xref py py-class docutils literal notranslate"><span class="pre">DTensor</span></code></a> filled with random numbers from a normal distribution
 with mean 0 and variance 1. The shape of the tensor is defined by the variable
 argument <code class="docutils literal notranslate"><span class="pre">size</span></code>.</p>
@@ -1360,7 +1360,7 @@ <h3>Debugging Tools<a class="headerlink" href="#debugging-tools" title="Permalin
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.tensor.debug.CommDebugMode.generate_comm_debug_tracing_table">
-<span class="sig-name descname"><span class="pre">generate_comm_debug_tracing_table</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">noise_level</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">3</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/tensor/debug/_comm_mode.html#CommDebugMode.generate_comm_debug_tracing_table"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/tensor/debug/_comm_mode.py#L402"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.tensor.debug.CommDebugMode.generate_comm_debug_tracing_table" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">generate_comm_debug_tracing_table</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">noise_level</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">3</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/tensor/debug/_comm_mode.html#CommDebugMode.generate_comm_debug_tracing_table"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/tensor/debug/_comm_mode.py#L402"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.tensor.debug.CommDebugMode.generate_comm_debug_tracing_table" title="Permalink to this definition">¶</a></dt>
 <dd><p>Generates detailed table displaying operations and collective tracing information
 on a module level. Amount of information is dependent on noise_level</p>
 <ol class="arabic simple" start="0">
@@ -1373,7 +1373,7 @@ <h3>Debugging Tools<a class="headerlink" href="#debugging-tools" title="Permalin
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.tensor.debug.CommDebugMode.generate_json_dump">
-<span class="sig-name descname"><span class="pre">generate_json_dump</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">file_name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'comm_mode_log.json'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">noise_level</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">3</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/tensor/debug/_comm_mode.html#CommDebugMode.generate_json_dump"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/tensor/debug/_comm_mode.py#L254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.tensor.debug.CommDebugMode.generate_json_dump" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">generate_json_dump</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">file_name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'comm_mode_log.json'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">noise_level</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">3</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/tensor/debug/_comm_mode.html#CommDebugMode.generate_json_dump"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/tensor/debug/_comm_mode.py#L254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.tensor.debug.CommDebugMode.generate_json_dump" title="Permalink to this definition">¶</a></dt>
 <dd><p>Creates json file used to build browser visual
 0. prints module-level collective counts
 1. prints dTensor operations not included in trivial operations
@@ -1383,7 +1383,7 @@ <h3>Debugging Tools<a class="headerlink" href="#debugging-tools" title="Permalin
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.tensor.debug.CommDebugMode.get_comm_counts">
-<span class="sig-name descname"><span class="pre">get_comm_counts</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/tensor/debug/_comm_mode.html#CommDebugMode.get_comm_counts"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/tensor/debug/_comm_mode.py#L570"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.tensor.debug.CommDebugMode.get_comm_counts" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">get_comm_counts</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/tensor/debug/_comm_mode.html#CommDebugMode.get_comm_counts"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/tensor/debug/_comm_mode.py#L570"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.tensor.debug.CommDebugMode.get_comm_counts" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns the communication counts as a dictionary.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Returns</dt>
@@ -1397,7 +1397,7 @@ <h3>Debugging Tools<a class="headerlink" href="#debugging-tools" title="Permalin
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.tensor.debug.CommDebugMode.get_parameter_info">
-<span class="sig-name descname"><span class="pre">get_parameter_info</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/tensor/debug/_comm_mode.html#CommDebugMode.get_parameter_info"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/tensor/debug/_comm_mode.py#L578"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.tensor.debug.CommDebugMode.get_parameter_info" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">get_parameter_info</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/tensor/debug/_comm_mode.html#CommDebugMode.get_parameter_info"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/tensor/debug/_comm_mode.py#L578"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.tensor.debug.CommDebugMode.get_parameter_info" title="Permalink to this definition">¶</a></dt>
 <dd><dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
 <dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.13)">dict</a>[<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)">str</a>, <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.13)">dict</a>[<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)">str</a>, <a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.13)"><em>Any</em></a>]]</p>
@@ -1407,7 +1407,7 @@ <h3>Debugging Tools<a class="headerlink" href="#debugging-tools" title="Permalin
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.tensor.debug.CommDebugMode.get_sharding_info">
-<span class="sig-name descname"><span class="pre">get_sharding_info</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/tensor/debug/_comm_mode.html#CommDebugMode.get_sharding_info"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/tensor/debug/_comm_mode.py#L581"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.tensor.debug.CommDebugMode.get_sharding_info" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">get_sharding_info</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/tensor/debug/_comm_mode.html#CommDebugMode.get_sharding_info"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/tensor/debug/_comm_mode.py#L581"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.tensor.debug.CommDebugMode.get_sharding_info" title="Permalink to this definition">¶</a></dt>
 <dd><dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
 <dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.13)">dict</a>[<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)">str</a>, <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.13)">dict</a>[<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)">str</a>, <a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.13)"><em>Any</em></a>]]</p>
@@ -1417,7 +1417,7 @@ <h3>Debugging Tools<a class="headerlink" href="#debugging-tools" title="Permalin
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.tensor.debug.CommDebugMode.get_total_counts">
-<span class="sig-name descname"><span class="pre">get_total_counts</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/tensor/debug/_comm_mode.html#CommDebugMode.get_total_counts"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/tensor/debug/_comm_mode.py#L567"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.tensor.debug.CommDebugMode.get_total_counts" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">get_total_counts</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/tensor/debug/_comm_mode.html#CommDebugMode.get_total_counts"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/tensor/debug/_comm_mode.py#L567"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.tensor.debug.CommDebugMode.get_total_counts" title="Permalink to this definition">¶</a></dt>
 <dd><dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
 <dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)">int</a></p>
@@ -1427,7 +1427,7 @@ <h3>Debugging Tools<a class="headerlink" href="#debugging-tools" title="Permalin
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.tensor.debug.CommDebugMode.log_comm_debug_tracing_table_to_file">
-<span class="sig-name descname"><span class="pre">log_comm_debug_tracing_table_to_file</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">file_name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'comm_mode_log.txt'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">noise_level</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">3</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/tensor/debug/_comm_mode.html#CommDebugMode.log_comm_debug_tracing_table_to_file"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/tensor/debug/_comm_mode.py#L601"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.tensor.debug.CommDebugMode.log_comm_debug_tracing_table_to_file" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">log_comm_debug_tracing_table_to_file</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">file_name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'comm_mode_log.txt'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">noise_level</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">3</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/tensor/debug/_comm_mode.html#CommDebugMode.log_comm_debug_tracing_table_to_file"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/tensor/debug/_comm_mode.py#L601"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.tensor.debug.CommDebugMode.log_comm_debug_tracing_table_to_file" title="Permalink to this definition">¶</a></dt>
 <dd><p>Alternative to console CommDebugMode output, writes to file specified by the user</p>
 </dd></dl>
 
@@ -1436,7 +1436,7 @@ <h3>Debugging Tools<a class="headerlink" href="#debugging-tools" title="Permalin
 <p>To visualize the sharding of a DTensor that have less than 3 dimensions, DTensor provides <a class="reference internal" href="#torch.distributed.tensor.debug.visualize_sharding" title="torch.distributed.tensor.debug.visualize_sharding"><code class="xref py py-meth docutils literal notranslate"><span class="pre">visualize_sharding()</span></code></a>:</p>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.distributed.tensor.debug.visualize_sharding">
-<span class="sig-prename descclassname"><span class="pre">torch.distributed.tensor.debug.</span></span><span class="sig-name descname"><span class="pre">visualize_sharding</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dtensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">header</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/tensor/debug/_visualize_sharding.py#L136"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.tensor.debug.visualize_sharding" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributed.tensor.debug.</span></span><span class="sig-name descname"><span class="pre">visualize_sharding</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dtensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">header</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/tensor/debug/_visualize_sharding.py#L136"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.tensor.debug.visualize_sharding" title="Permalink to this definition">¶</a></dt>
 <dd><p>Visualizes sharding in the terminal for <code class="xref py py-class docutils literal notranslate"><span class="pre">DTensor</span></code> that are 1D or 2D.</p>
 <div class="admonition note">
 <p class="admonition-title">Note</p>
@@ -1453,7 +1453,7 @@ <h2>Experimental Features<a class="headerlink" href="#experimental-features" tit
 these features.</p>
 <span class="target" id="module-torch.distributed.tensor.experimental"></span><dl class="py function">
 <dt class="sig sig-object py" id="torch.distributed.tensor.experimental.context_parallel">
-<span class="sig-prename descclassname"><span class="pre">torch.distributed.tensor.experimental.</span></span><span class="sig-name descname"><span class="pre">context_parallel</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mesh</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">buffers</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">buffer_seq_dims</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">no_restore_buffers</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/tensor/experimental/_attention.py#L1202"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.tensor.experimental.context_parallel" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributed.tensor.experimental.</span></span><span class="sig-name descname"><span class="pre">context_parallel</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mesh</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">buffers</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">buffer_seq_dims</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">no_restore_buffers</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/tensor/experimental/_attention.py#L1202"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.tensor.experimental.context_parallel" title="Permalink to this definition">¶</a></dt>
 <dd><p><code class="docutils literal notranslate"><span class="pre">context_parallel</span></code> is an experimental API to enable context
 parallelism (CP). This API performs two actions: 1) patch the SDPA
 (<code class="docutils literal notranslate"><span class="pre">torch.nn.functional.scaled_dot_product_attention</span></code>) with the CP-enabled
@@ -1492,7 +1492,7 @@ <h2>Experimental Features<a class="headerlink" href="#experimental-features" tit
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.distributed.tensor.experimental.local_map">
-<span class="sig-prename descclassname"><span class="pre">torch.distributed.tensor.experimental.</span></span><span class="sig-name descname"><span class="pre">local_map</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">func</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out_placements</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">in_placements</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device_mesh</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">redistribute_inputs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/tensor/experimental/_func_map.py#L25"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.tensor.experimental.local_map" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributed.tensor.experimental.</span></span><span class="sig-name descname"><span class="pre">local_map</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">func</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out_placements</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">in_placements</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device_mesh</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">redistribute_inputs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/tensor/experimental/_func_map.py#L25"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.tensor.experimental.local_map" title="Permalink to this definition">¶</a></dt>
 <dd><p><a class="reference internal" href="#torch.distributed.tensor.experimental.local_map" title="torch.distributed.tensor.experimental.local_map"><code class="xref py py-meth docutils literal notranslate"><span class="pre">local_map()</span></code></a> is an experimental API that allows users to pass <code class="xref py py-class docutils literal notranslate"><span class="pre">DTensor</span></code> s
 to a function that is written to be applied on <code class="docutils literal notranslate"><span class="pre">torch.Tensor</span></code> s. It is done by extracting
 the local components of <code class="xref py py-class docutils literal notranslate"><span class="pre">DTensor</span></code>, call the function, and wrap the outputs to
@@ -1586,7 +1586,7 @@ <h2>Experimental Features<a class="headerlink" href="#experimental-features" tit
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.distributed.tensor.experimental.register_sharding">
-<span class="sig-prename descclassname"><span class="pre">torch.distributed.tensor.experimental.</span></span><span class="sig-name descname"><span class="pre">register_sharding</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">op</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/tensor/experimental/_register_sharding.py#L25"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.tensor.experimental.register_sharding" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributed.tensor.experimental.</span></span><span class="sig-name descname"><span class="pre">register_sharding</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">op</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/tensor/experimental/_register_sharding.py#L25"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.tensor.experimental.register_sharding" title="Permalink to this definition">¶</a></dt>
 <dd><p><a class="reference internal" href="#torch.distributed.tensor.experimental.register_sharding" title="torch.distributed.tensor.experimental.register_sharding"><code class="xref py py-meth docutils literal notranslate"><span class="pre">register_sharding()</span></code></a> is an experimental API that allows users to register sharding
 strategies for an operator when the tensor inputs and outputs are DTensor.
 It can be useful when: (1) there doesn’t exist a default sharding strategy for <code class="docutils literal notranslate"><span class="pre">op</span></code>,
diff --git a/main/distributed.tensor.parallel.html b/main/distributed.tensor.parallel.html
index c26bb65eb5d..ec89ccce8cf 100644
--- a/main/distributed.tensor.parallel.html
+++ b/main/distributed.tensor.parallel.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -618,7 +618,7 @@ <h1>Tensor Parallelism - torch.distributed.tensor.parallel<a class="headerlink"
 <p>The entrypoint to parallelize your <code class="docutils literal notranslate"><span class="pre">nn.Module</span></code> using Tensor Parallelism is:</p>
 <span class="target" id="module-torch.distributed.tensor.parallel"></span><dl class="py function">
 <dt class="sig sig-object py" id="torch.distributed.tensor.parallel.parallelize_module">
-<span class="sig-prename descclassname"><span class="pre">torch.distributed.tensor.parallel.</span></span><span class="sig-name descname"><span class="pre">parallelize_module</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device_mesh</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">parallelize_plan</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">src_data_rank</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/tensor/parallel/api.html#parallelize_module"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/tensor/parallel/api.py#L16"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.tensor.parallel.parallelize_module" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributed.tensor.parallel.</span></span><span class="sig-name descname"><span class="pre">parallelize_module</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device_mesh</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">parallelize_plan</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">src_data_rank</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/tensor/parallel/api.html#parallelize_module"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/tensor/parallel/api.py#L16"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.tensor.parallel.parallelize_module" title="Permalink to this definition">¶</a></dt>
 <dd><p>Apply Tensor Parallelism in PyTorch by parallelizing modules or sub-modules based on a user-specified plan.</p>
 <p>We parallelize module or sub_modules based on a parallelize_plan. The parallelize_plan contains
 <code class="xref py py-class docutils literal notranslate"><span class="pre">ParallelStyle</span></code>, which indicates how user wants the module or sub_module
@@ -677,7 +677,7 @@ <h1>Tensor Parallelism - torch.distributed.tensor.parallel<a class="headerlink"
 <p>Tensor Parallelism supports the following parallel styles:</p>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributed.tensor.parallel.ColwiseParallel">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.tensor.parallel.</span></span><span class="sig-name descname"><span class="pre">ColwiseParallel</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_layouts</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_layouts</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_local_output</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/tensor/parallel/style.html#ColwiseParallel"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/tensor/parallel/style.py#L45"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.tensor.parallel.ColwiseParallel" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.tensor.parallel.</span></span><span class="sig-name descname"><span class="pre">ColwiseParallel</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_layouts</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_layouts</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_local_output</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/tensor/parallel/style.html#ColwiseParallel"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/tensor/parallel/style.py#L45"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.tensor.parallel.ColwiseParallel" title="Permalink to this definition">¶</a></dt>
 <dd><p>Partition a compatible nn.Module in a column-wise fashion. Currently supports nn.Linear and nn.Embedding.
 Users can compose it together with RowwiseParallel to achieve the sharding of more complicated modules.
 (i.e. MLP, Attention)</p>
@@ -721,7 +721,7 @@ <h1>Tensor Parallelism - torch.distributed.tensor.parallel<a class="headerlink"
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributed.tensor.parallel.RowwiseParallel">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.tensor.parallel.</span></span><span class="sig-name descname"><span class="pre">RowwiseParallel</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_layouts</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_layouts</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_local_output</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/tensor/parallel/style.html#RowwiseParallel"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/tensor/parallel/style.py#L171"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.tensor.parallel.RowwiseParallel" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.tensor.parallel.</span></span><span class="sig-name descname"><span class="pre">RowwiseParallel</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_layouts</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_layouts</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_local_output</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/tensor/parallel/style.html#RowwiseParallel"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/tensor/parallel/style.py#L171"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.tensor.parallel.RowwiseParallel" title="Permalink to this definition">¶</a></dt>
 <dd><p>Partition a compatible nn.Module in a row-wise fashion. Currently supports nn.Linear and nn.Embedding.
 Users can compose it with ColwiseParallel to achieve the sharding of more complicated modules.
 (i.e. MLP, Attention)</p>
@@ -759,7 +759,7 @@ <h1>Tensor Parallelism - torch.distributed.tensor.parallel<a class="headerlink"
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributed.tensor.parallel.SequenceParallel">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.tensor.parallel.</span></span><span class="sig-name descname"><span class="pre">SequenceParallel</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sequence_dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_local_output</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/tensor/parallel/style.html#SequenceParallel"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/tensor/parallel/style.py#L308"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.tensor.parallel.SequenceParallel" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.tensor.parallel.</span></span><span class="sig-name descname"><span class="pre">SequenceParallel</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sequence_dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_local_output</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/tensor/parallel/style.html#SequenceParallel"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/tensor/parallel/style.py#L308"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.tensor.parallel.SequenceParallel" title="Permalink to this definition">¶</a></dt>
 <dd><p>SequenceParallel replicates a compatible <code class="docutils literal notranslate"><span class="pre">nn.Module</span></code> parameters and runs the sharded computation with
 input sharded on the sequence dimension. This currently supports <code class="docutils literal notranslate"><span class="pre">nn.LayerNorm</span></code>, <code class="docutils literal notranslate"><span class="pre">nn.Dropout</span></code>, and the
 <a class="reference external" href="https://github.com/facebookresearch/llama/blob/main/llama/model.py#L34">RMSNorm python implementation</a></p>
@@ -813,7 +813,7 @@ <h1>Tensor Parallelism - torch.distributed.tensor.parallel<a class="headerlink"
 the <code class="docutils literal notranslate"><span class="pre">parallelize_plan</span></code> when calling <code class="docutils literal notranslate"><span class="pre">parallelize_module</span></code>:</p>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributed.tensor.parallel.PrepareModuleInput">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.tensor.parallel.</span></span><span class="sig-name descname"><span class="pre">PrepareModuleInput</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_layouts</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">desired_input_layouts</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_kwarg_layouts</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">desired_input_kwarg_layouts</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_local_output</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/tensor/parallel/style.html#PrepareModuleInput"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/tensor/parallel/style.py#L403"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.tensor.parallel.PrepareModuleInput" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.tensor.parallel.</span></span><span class="sig-name descname"><span class="pre">PrepareModuleInput</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_layouts</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">desired_input_layouts</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_kwarg_layouts</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">desired_input_kwarg_layouts</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_local_output</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/tensor/parallel/style.html#PrepareModuleInput"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/tensor/parallel/style.py#L403"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.tensor.parallel.PrepareModuleInput" title="Permalink to this definition">¶</a></dt>
 <dd><p>Configure the nn.Module’s inputs to convert the input tensors of the nn.Module to DTensors at runtime according to
 <code class="docutils literal notranslate"><span class="pre">input_layouts</span></code>, and perform layout redistribution according to the <code class="docutils literal notranslate"><span class="pre">desired_input_layouts</span></code>.</p>
 <dl class="field-list simple">
@@ -863,7 +863,7 @@ <h1>Tensor Parallelism - torch.distributed.tensor.parallel<a class="headerlink"
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributed.tensor.parallel.PrepareModuleOutput">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.tensor.parallel.</span></span><span class="sig-name descname"><span class="pre">PrepareModuleOutput</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_layouts</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">desired_output_layouts</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_local_output</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/tensor/parallel/style.html#PrepareModuleOutput"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/tensor/parallel/style.py#L558"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.tensor.parallel.PrepareModuleOutput" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.tensor.parallel.</span></span><span class="sig-name descname"><span class="pre">PrepareModuleOutput</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_layouts</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">desired_output_layouts</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_local_output</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/tensor/parallel/style.html#PrepareModuleOutput"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/tensor/parallel/style.py#L558"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.tensor.parallel.PrepareModuleOutput" title="Permalink to this definition">¶</a></dt>
 <dd><p>Configure the nn.Module’s outputs to convert the output tensors of the nn.Module to DTensors at runtime according to
 <code class="docutils literal notranslate"><span class="pre">output_layouts</span></code>, and perform layout redistribution according to the <code class="docutils literal notranslate"><span class="pre">desired_output_layouts</span></code>.</p>
 <dl class="field-list simple">
@@ -921,7 +921,7 @@ <h1>Tensor Parallelism - torch.distributed.tensor.parallel<a class="headerlink"
 <p>Parallelized cross-entropy loss computation (loss parallelism), is supported via the following context manager:</p>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.distributed.tensor.parallel.loss_parallel">
-<span class="sig-prename descclassname"><span class="pre">torch.distributed.tensor.parallel.</span></span><span class="sig-name descname"><span class="pre">loss_parallel</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/tensor/parallel/loss.html#loss_parallel"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/tensor/parallel/loss.py#L29"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.tensor.parallel.loss_parallel" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributed.tensor.parallel.</span></span><span class="sig-name descname"><span class="pre">loss_parallel</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/tensor/parallel/loss.html#loss_parallel"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/tensor/parallel/loss.py#L29"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.tensor.parallel.loss_parallel" title="Permalink to this definition">¶</a></dt>
 <dd><p>A context manager that enables loss parallelism, where efficient parallelized loss computation
 can be performed when the input is sharded on the class dimension. Currently only the cross-entropy
 loss is supported.</p>
diff --git a/main/distributions.html b/main/distributions.html
index 3088d84b4b0..09d2639f90d 100644
--- a/main/distributions.html
+++ b/main/distributions.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -670,7 +670,7 @@ <h2>Pathwise derivative<a class="headerlink" href="#pathwise-derivative" title="
 <h2><span class="hidden-section">Distribution</span><a class="headerlink" href="#distribution" title="Permalink to this heading">¶</a></h2>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributions.distribution.Distribution">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.distribution.</span></span><span class="sig-name descname"><span class="pre">Distribution</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">batch_shape</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.Size([])</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">event_shape</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.Size([])</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">validate_args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/distribution.html#Distribution"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/distribution.py#L16"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.distribution.Distribution" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.distribution.</span></span><span class="sig-name descname"><span class="pre">Distribution</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">batch_shape</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.Size([])</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">event_shape</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.Size([])</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">validate_args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/distribution.html#Distribution"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/distribution.py#L16"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.distribution.Distribution" title="Permalink to this definition">¶</a></dt>
 <dd><p>Bases: <a class="reference external" href="https://docs.python.org/3/library/functions.html#object" title="(in Python v3.13)"><code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></a></p>
 <p>Distribution is the abstract base class for probability distributions.</p>
 <dl class="field-list simple">
@@ -692,7 +692,7 @@ <h2><span class="hidden-section">Distribution</span><a class="headerlink" href="
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.distribution.Distribution.cdf">
-<span class="sig-name descname"><span class="pre">cdf</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/distribution.html#Distribution.cdf"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/distribution.py#L199"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.distribution.Distribution.cdf" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">cdf</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/distribution.html#Distribution.cdf"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/distribution.py#L199"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.distribution.Distribution.cdf" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns the cumulative density/mass function evaluated at
 <cite>value</cite>.</p>
 <dl class="field-list simple">
@@ -707,7 +707,7 @@ <h2><span class="hidden-section">Distribution</span><a class="headerlink" href="
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.distribution.Distribution.entropy">
-<span class="sig-name descname"><span class="pre">entropy</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/distribution.html#Distribution.entropy"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/distribution.py#L243"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.distribution.Distribution.entropy" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">entropy</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/distribution.html#Distribution.entropy"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/distribution.py#L243"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.distribution.Distribution.entropy" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns entropy of distribution, batched over batch_shape.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Returns</dt>
@@ -721,7 +721,7 @@ <h2><span class="hidden-section">Distribution</span><a class="headerlink" href="
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.distribution.Distribution.enumerate_support">
-<span class="sig-name descname"><span class="pre">enumerate_support</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">expand</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/distribution.html#Distribution.enumerate_support"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/distribution.py#L219"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.distribution.Distribution.enumerate_support" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">enumerate_support</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">expand</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/distribution.html#Distribution.enumerate_support"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/distribution.py#L219"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.distribution.Distribution.enumerate_support" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns tensor containing all values supported by a discrete
 distribution. The result will enumerate over dimension 0, so the shape
 of the result will be <cite>(cardinality,) + batch_shape + event_shape</cite>
@@ -754,7 +754,7 @@ <h2><span class="hidden-section">Distribution</span><a class="headerlink" href="
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.distribution.Distribution.expand">
-<span class="sig-name descname"><span class="pre">expand</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">batch_shape</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_instance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/distribution.html#Distribution.expand"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/distribution.py#L81"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.distribution.Distribution.expand" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">expand</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">batch_shape</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_instance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/distribution.html#Distribution.expand"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/distribution.py#L81"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.distribution.Distribution.expand" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns a new distribution instance (or populates an existing instance
 provided by a derived class) with batch dimensions expanded to
 <cite>batch_shape</cite>. This method calls <a class="reference internal" href="generated/torch.Tensor.expand.html#torch.Tensor.expand" title="torch.Tensor.expand"><code class="xref py py-class docutils literal notranslate"><span class="pre">expand</span></code></a> on
@@ -779,7 +779,7 @@ <h2><span class="hidden-section">Distribution</span><a class="headerlink" href="
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.distribution.Distribution.icdf">
-<span class="sig-name descname"><span class="pre">icdf</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/distribution.html#Distribution.icdf"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/distribution.py#L209"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.distribution.Distribution.icdf" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">icdf</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/distribution.html#Distribution.icdf"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/distribution.py#L209"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.distribution.Distribution.icdf" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns the inverse cumulative density/mass function evaluated at
 <cite>value</cite>.</p>
 <dl class="field-list simple">
@@ -794,7 +794,7 @@ <h2><span class="hidden-section">Distribution</span><a class="headerlink" href="
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.distribution.Distribution.log_prob">
-<span class="sig-name descname"><span class="pre">log_prob</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/distribution.html#Distribution.log_prob"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/distribution.py#L189"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.distribution.Distribution.log_prob" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">log_prob</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/distribution.html#Distribution.log_prob"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/distribution.py#L189"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.distribution.Distribution.log_prob" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns the log of the probability density/mass function evaluated at
 <cite>value</cite>.</p>
 <dl class="field-list simple">
@@ -821,7 +821,7 @@ <h2><span class="hidden-section">Distribution</span><a class="headerlink" href="
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.distribution.Distribution.perplexity">
-<span class="sig-name descname"><span class="pre">perplexity</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/distribution.html#Distribution.perplexity"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/distribution.py#L252"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.distribution.Distribution.perplexity" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">perplexity</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/distribution.html#Distribution.perplexity"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/distribution.py#L252"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.distribution.Distribution.perplexity" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns perplexity of distribution, batched over batch_shape.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Returns</dt>
@@ -835,7 +835,7 @@ <h2><span class="hidden-section">Distribution</span><a class="headerlink" href="
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.distribution.Distribution.rsample">
-<span class="sig-name descname"><span class="pre">rsample</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sample_shape</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.Size([])</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/distribution.html#Distribution.rsample"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/distribution.py#L170"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.distribution.Distribution.rsample" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">rsample</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sample_shape</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.Size([])</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/distribution.html#Distribution.rsample"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/distribution.py#L170"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.distribution.Distribution.rsample" title="Permalink to this definition">¶</a></dt>
 <dd><p>Generates a sample_shape shaped reparameterized sample or sample_shape
 shaped batch of reparameterized samples if the distribution parameters
 are batched.</p>
@@ -848,7 +848,7 @@ <h2><span class="hidden-section">Distribution</span><a class="headerlink" href="
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.distribution.Distribution.sample">
-<span class="sig-name descname"><span class="pre">sample</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sample_shape</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.Size([])</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/distribution.html#Distribution.sample"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/distribution.py#L162"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.distribution.Distribution.sample" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">sample</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sample_shape</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.Size([])</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/distribution.html#Distribution.sample"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/distribution.py#L162"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.distribution.Distribution.sample" title="Permalink to this definition">¶</a></dt>
 <dd><p>Generates a sample_shape shaped sample or sample_shape shaped batch of
 samples if the distribution parameters are batched.</p>
 <dl class="field-list simple">
@@ -860,7 +860,7 @@ <h2><span class="hidden-section">Distribution</span><a class="headerlink" href="
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.distribution.Distribution.sample_n">
-<span class="sig-name descname"><span class="pre">sample_n</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/distribution.html#Distribution.sample_n"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/distribution.py#L178"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.distribution.Distribution.sample_n" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">sample_n</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/distribution.html#Distribution.sample_n"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/distribution.py#L178"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.distribution.Distribution.sample_n" title="Permalink to this definition">¶</a></dt>
 <dd><p>Generates n samples or n batches of samples if the distribution
 parameters are batched.</p>
 <dl class="field-list simple">
@@ -872,7 +872,7 @@ <h2><span class="hidden-section">Distribution</span><a class="headerlink" href="
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.distribution.Distribution.set_default_validate_args">
-<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">set_default_validate_args</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/distribution.html#Distribution.set_default_validate_args"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/distribution.py#L25"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.distribution.Distribution.set_default_validate_args" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">set_default_validate_args</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/distribution.html#Distribution.set_default_validate_args"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/distribution.py#L25"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.distribution.Distribution.set_default_validate_args" title="Permalink to this definition">¶</a></dt>
 <dd><p>Sets whether validation is enabled or disabled.</p>
 <p>The default behavior mimics Python’s <code class="docutils literal notranslate"><span class="pre">assert</span></code> statement: validation
 is on by default, but is disabled if Python is run in optimized mode
@@ -911,7 +911,7 @@ <h2><span class="hidden-section">Distribution</span><a class="headerlink" href="
 <h2><span class="hidden-section">ExponentialFamily</span><a class="headerlink" href="#exponentialfamily" title="Permalink to this heading">¶</a></h2>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributions.exp_family.ExponentialFamily">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.exp_family.</span></span><span class="sig-name descname"><span class="pre">ExponentialFamily</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">batch_shape</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.Size([])</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">event_shape</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.Size([])</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">validate_args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/exp_family.html#ExponentialFamily"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/exp_family.py#L10"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.exp_family.ExponentialFamily" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.exp_family.</span></span><span class="sig-name descname"><span class="pre">ExponentialFamily</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">batch_shape</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.Size([])</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">event_shape</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.Size([])</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">validate_args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/exp_family.html#ExponentialFamily"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/exp_family.py#L10"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.exp_family.ExponentialFamily" title="Permalink to this definition">¶</a></dt>
 <dd><p>Bases: <a class="reference internal" href="#torch.distributions.distribution.Distribution" title="torch.distributions.distribution.Distribution"><code class="xref py py-class docutils literal notranslate"><span class="pre">Distribution</span></code></a></p>
 <p>ExponentialFamily is the abstract base class for probability distributions belonging to an
 exponential family, whose probability mass/density function has the form is defined below</p>
@@ -931,7 +931,7 @@ <h2><span class="hidden-section">ExponentialFamily</span><a class="headerlink" h
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.exp_family.ExponentialFamily.entropy">
-<span class="sig-name descname"><span class="pre">entropy</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/exp_family.html#ExponentialFamily.entropy"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/exp_family.py#L54"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.exp_family.ExponentialFamily.entropy" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">entropy</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/exp_family.html#ExponentialFamily.entropy"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/exp_family.py#L54"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.exp_family.ExponentialFamily.entropy" title="Permalink to this definition">¶</a></dt>
 <dd><p>Method to compute the entropy using Bregman divergence of the log normalizer.</p>
 </dd></dl>
 
@@ -942,7 +942,7 @@ <h2><span class="hidden-section">ExponentialFamily</span><a class="headerlink" h
 <h2><span class="hidden-section">Bernoulli</span><a class="headerlink" href="#bernoulli" title="Permalink to this heading">¶</a></h2>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributions.bernoulli.Bernoulli">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.bernoulli.</span></span><span class="sig-name descname"><span class="pre">Bernoulli</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">probs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">logits</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">validate_args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/bernoulli.html#Bernoulli"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/bernoulli.py#L19"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.bernoulli.Bernoulli" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.bernoulli.</span></span><span class="sig-name descname"><span class="pre">Bernoulli</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">probs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">logits</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">validate_args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/bernoulli.html#Bernoulli"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/bernoulli.py#L19"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.bernoulli.Bernoulli" title="Permalink to this definition">¶</a></dt>
 <dd><p>Bases: <a class="reference internal" href="#torch.distributions.exp_family.ExponentialFamily" title="torch.distributions.exp_family.ExponentialFamily"><code class="xref py py-class docutils literal notranslate"><span class="pre">ExponentialFamily</span></code></a></p>
 <p>Creates a Bernoulli distribution parameterized by <a class="reference internal" href="#torch.distributions.bernoulli.Bernoulli.probs" title="torch.distributions.bernoulli.Bernoulli.probs"><code class="xref py py-attr docutils literal notranslate"><span class="pre">probs</span></code></a>
 or <a class="reference internal" href="#torch.distributions.bernoulli.Bernoulli.logits" title="torch.distributions.bernoulli.Bernoulli.logits"><code class="xref py py-attr docutils literal notranslate"><span class="pre">logits</span></code></a> (but not both).</p>
@@ -969,17 +969,17 @@ <h2><span class="hidden-section">Bernoulli</span><a class="headerlink" href="#be
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.bernoulli.Bernoulli.entropy">
-<span class="sig-name descname"><span class="pre">entropy</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/bernoulli.html#Bernoulli.entropy"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/bernoulli.py#L114"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.bernoulli.Bernoulli.entropy" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">entropy</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/bernoulli.html#Bernoulli.entropy"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/bernoulli.py#L114"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.bernoulli.Bernoulli.entropy" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.bernoulli.Bernoulli.enumerate_support">
-<span class="sig-name descname"><span class="pre">enumerate_support</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">expand</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/bernoulli.html#Bernoulli.enumerate_support"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/bernoulli.py#L119"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.bernoulli.Bernoulli.enumerate_support" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">enumerate_support</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">expand</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/bernoulli.html#Bernoulli.enumerate_support"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/bernoulli.py#L119"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.bernoulli.Bernoulli.enumerate_support" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.bernoulli.Bernoulli.expand">
-<span class="sig-name descname"><span class="pre">expand</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">batch_shape</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_instance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/bernoulli.html#Bernoulli.expand"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/bernoulli.py#L61"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.bernoulli.Bernoulli.expand" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">expand</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">batch_shape</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_instance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/bernoulli.html#Bernoulli.expand"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/bernoulli.py#L61"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.bernoulli.Bernoulli.expand" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py attribute">
@@ -989,7 +989,7 @@ <h2><span class="hidden-section">Bernoulli</span><a class="headerlink" href="#be
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.bernoulli.Bernoulli.log_prob">
-<span class="sig-name descname"><span class="pre">log_prob</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/bernoulli.html#Bernoulli.log_prob"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/bernoulli.py#L108"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.bernoulli.Bernoulli.log_prob" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">log_prob</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/bernoulli.html#Bernoulli.log_prob"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/bernoulli.py#L108"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.bernoulli.Bernoulli.log_prob" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py property">
@@ -1019,7 +1019,7 @@ <h2><span class="hidden-section">Bernoulli</span><a class="headerlink" href="#be
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.bernoulli.Bernoulli.sample">
-<span class="sig-name descname"><span class="pre">sample</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sample_shape</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.Size([])</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/bernoulli.html#Bernoulli.sample"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/bernoulli.py#L103"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.bernoulli.Bernoulli.sample" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">sample</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sample_shape</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.Size([])</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/bernoulli.html#Bernoulli.sample"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/bernoulli.py#L103"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.bernoulli.Bernoulli.sample" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py attribute">
@@ -1039,7 +1039,7 @@ <h2><span class="hidden-section">Bernoulli</span><a class="headerlink" href="#be
 <h2><span class="hidden-section">Beta</span><a class="headerlink" href="#beta" title="Permalink to this heading">¶</a></h2>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributions.beta.Beta">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.beta.</span></span><span class="sig-name descname"><span class="pre">Beta</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">concentration1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">concentration0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">validate_args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/beta.html#Beta"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/beta.py#L14"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.beta.Beta" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.beta.</span></span><span class="sig-name descname"><span class="pre">Beta</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">concentration1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">concentration0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">validate_args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/beta.html#Beta"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/beta.py#L14"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.beta.Beta" title="Permalink to this definition">¶</a></dt>
 <dd><p>Bases: <a class="reference internal" href="#torch.distributions.exp_family.ExponentialFamily" title="torch.distributions.exp_family.ExponentialFamily"><code class="xref py py-class docutils literal notranslate"><span class="pre">ExponentialFamily</span></code></a></p>
 <p>Beta distribution parameterized by <a class="reference internal" href="#torch.distributions.beta.Beta.concentration1" title="torch.distributions.beta.Beta.concentration1"><code class="xref py py-attr docutils literal notranslate"><span class="pre">concentration1</span></code></a> and <a class="reference internal" href="#torch.distributions.beta.Beta.concentration0" title="torch.distributions.beta.Beta.concentration0"><code class="xref py py-attr docutils literal notranslate"><span class="pre">concentration0</span></code></a>.</p>
 <p>Example:</p>
@@ -1075,12 +1075,12 @@ <h2><span class="hidden-section">Beta</span><a class="headerlink" href="#beta" t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.beta.Beta.entropy">
-<span class="sig-name descname"><span class="pre">entropy</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/beta.html#Beta.entropy"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/beta.py#L85"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.beta.Beta.entropy" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">entropy</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/beta.html#Beta.entropy"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/beta.py#L85"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.beta.Beta.entropy" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.beta.Beta.expand">
-<span class="sig-name descname"><span class="pre">expand</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">batch_shape</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_instance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/beta.html#Beta.expand"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/beta.py#L55"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.beta.Beta.expand" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">expand</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">batch_shape</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_instance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/beta.html#Beta.expand"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/beta.py#L55"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.beta.Beta.expand" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py attribute">
@@ -1090,7 +1090,7 @@ <h2><span class="hidden-section">Beta</span><a class="headerlink" href="#beta" t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.beta.Beta.log_prob">
-<span class="sig-name descname"><span class="pre">log_prob</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/beta.html#Beta.log_prob"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/beta.py#L79"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.beta.Beta.log_prob" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">log_prob</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/beta.html#Beta.log_prob"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/beta.py#L79"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.beta.Beta.log_prob" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py property">
@@ -1105,7 +1105,7 @@ <h2><span class="hidden-section">Beta</span><a class="headerlink" href="#beta" t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.beta.Beta.rsample">
-<span class="sig-name descname"><span class="pre">rsample</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sample_shape</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">()</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/beta.html#Beta.rsample"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/beta.py#L76"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.beta.Beta.rsample" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">rsample</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sample_shape</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">()</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/beta.html#Beta.rsample"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/beta.py#L76"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.beta.Beta.rsample" title="Permalink to this definition">¶</a></dt>
 <dd><dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
 <dd class="field-odd"><p><a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a></p>
@@ -1130,7 +1130,7 @@ <h2><span class="hidden-section">Beta</span><a class="headerlink" href="#beta" t
 <h2><span class="hidden-section">Binomial</span><a class="headerlink" href="#binomial" title="Permalink to this heading">¶</a></h2>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributions.binomial.Binomial">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.binomial.</span></span><span class="sig-name descname"><span class="pre">Binomial</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">total_count</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">probs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">logits</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">validate_args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/binomial.html#Binomial"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/binomial.py#L22"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.binomial.Binomial" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.binomial.</span></span><span class="sig-name descname"><span class="pre">Binomial</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">total_count</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">probs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">logits</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">validate_args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/binomial.html#Binomial"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/binomial.py#L22"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.binomial.Binomial" title="Permalink to this definition">¶</a></dt>
 <dd><p>Bases: <a class="reference internal" href="#torch.distributions.distribution.Distribution" title="torch.distributions.distribution.Distribution"><code class="xref py py-class docutils literal notranslate"><span class="pre">Distribution</span></code></a></p>
 <p>Creates a Binomial distribution parameterized by <code class="xref py py-attr docutils literal notranslate"><span class="pre">total_count</span></code> and
 either <a class="reference internal" href="#torch.distributions.binomial.Binomial.probs" title="torch.distributions.binomial.Binomial.probs"><code class="xref py py-attr docutils literal notranslate"><span class="pre">probs</span></code></a> or <a class="reference internal" href="#torch.distributions.binomial.Binomial.logits" title="torch.distributions.binomial.Binomial.logits"><code class="xref py py-attr docutils literal notranslate"><span class="pre">logits</span></code></a> (but not both). <code class="xref py py-attr docutils literal notranslate"><span class="pre">total_count</span></code> must be
@@ -1162,17 +1162,17 @@ <h2><span class="hidden-section">Binomial</span><a class="headerlink" href="#bin
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.binomial.Binomial.entropy">
-<span class="sig-name descname"><span class="pre">entropy</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/binomial.html#Binomial.entropy"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/binomial.py#L146"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.binomial.Binomial.entropy" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">entropy</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/binomial.html#Binomial.entropy"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/binomial.py#L146"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.binomial.Binomial.entropy" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.binomial.Binomial.enumerate_support">
-<span class="sig-name descname"><span class="pre">enumerate_support</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">expand</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/binomial.html#Binomial.enumerate_support"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/binomial.py#L156"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.binomial.Binomial.enumerate_support" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">enumerate_support</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">expand</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/binomial.html#Binomial.enumerate_support"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/binomial.py#L156"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.binomial.Binomial.enumerate_support" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.binomial.Binomial.expand">
-<span class="sig-name descname"><span class="pre">expand</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">batch_shape</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_instance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/binomial.html#Binomial.expand"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/binomial.py#L74"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.binomial.Binomial.expand" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">expand</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">batch_shape</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_instance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/binomial.html#Binomial.expand"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/binomial.py#L74"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.binomial.Binomial.expand" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py attribute">
@@ -1182,7 +1182,7 @@ <h2><span class="hidden-section">Binomial</span><a class="headerlink" href="#bin
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.binomial.Binomial.log_prob">
-<span class="sig-name descname"><span class="pre">log_prob</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/binomial.html#Binomial.log_prob"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/binomial.py#L126"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.binomial.Binomial.log_prob" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">log_prob</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/binomial.html#Binomial.log_prob"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/binomial.py#L126"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.binomial.Binomial.log_prob" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py property">
@@ -1212,7 +1212,7 @@ <h2><span class="hidden-section">Binomial</span><a class="headerlink" href="#bin
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.binomial.Binomial.sample">
-<span class="sig-name descname"><span class="pre">sample</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sample_shape</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.Size([])</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/binomial.html#Binomial.sample"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/binomial.py#L119"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.binomial.Binomial.sample" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">sample</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sample_shape</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.Size([])</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/binomial.html#Binomial.sample"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/binomial.py#L119"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.binomial.Binomial.sample" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py property">
@@ -1237,7 +1237,7 @@ <h2><span class="hidden-section">Binomial</span><a class="headerlink" href="#bin
 <h2><span class="hidden-section">Categorical</span><a class="headerlink" href="#categorical" title="Permalink to this heading">¶</a></h2>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributions.categorical.Categorical">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.categorical.</span></span><span class="sig-name descname"><span class="pre">Categorical</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">probs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">logits</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">validate_args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/categorical.html#Categorical"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/categorical.py#L12"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.categorical.Categorical" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.categorical.</span></span><span class="sig-name descname"><span class="pre">Categorical</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">probs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">logits</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">validate_args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/categorical.html#Categorical"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/categorical.py#L12"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.categorical.Categorical" title="Permalink to this definition">¶</a></dt>
 <dd><p>Bases: <a class="reference internal" href="#torch.distributions.distribution.Distribution" title="torch.distributions.distribution.Distribution"><code class="xref py py-class docutils literal notranslate"><span class="pre">Distribution</span></code></a></p>
 <p>Creates a categorical distribution parameterized by either <a class="reference internal" href="#torch.distributions.categorical.Categorical.probs" title="torch.distributions.categorical.Categorical.probs"><code class="xref py py-attr docutils literal notranslate"><span class="pre">probs</span></code></a> or
 <a class="reference internal" href="#torch.distributions.categorical.Categorical.logits" title="torch.distributions.categorical.Categorical.logits"><code class="xref py py-attr docutils literal notranslate"><span class="pre">logits</span></code></a> (but not both).</p>
@@ -1283,17 +1283,17 @@ <h2><span class="hidden-section">Categorical</span><a class="headerlink" href="#
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.categorical.Categorical.entropy">
-<span class="sig-name descname"><span class="pre">entropy</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/categorical.html#Categorical.entropy"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/categorical.py#L145"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.categorical.Categorical.entropy" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">entropy</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/categorical.html#Categorical.entropy"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/categorical.py#L145"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.categorical.Categorical.entropy" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.categorical.Categorical.enumerate_support">
-<span class="sig-name descname"><span class="pre">enumerate_support</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">expand</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/categorical.html#Categorical.enumerate_support"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/categorical.py#L151"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.categorical.Categorical.enumerate_support" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">enumerate_support</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">expand</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/categorical.html#Categorical.enumerate_support"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/categorical.py#L151"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.categorical.Categorical.enumerate_support" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.categorical.Categorical.expand">
-<span class="sig-name descname"><span class="pre">expand</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">batch_shape</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_instance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/categorical.html#Categorical.expand"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/categorical.py#L74"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.categorical.Categorical.expand" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">expand</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">batch_shape</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_instance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/categorical.html#Categorical.expand"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/categorical.py#L74"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.categorical.Categorical.expand" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py attribute">
@@ -1303,7 +1303,7 @@ <h2><span class="hidden-section">Categorical</span><a class="headerlink" href="#
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.categorical.Categorical.log_prob">
-<span class="sig-name descname"><span class="pre">log_prob</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/categorical.html#Categorical.log_prob"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/categorical.py#L137"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.categorical.Categorical.log_prob" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">log_prob</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/categorical.html#Categorical.log_prob"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/categorical.py#L137"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.categorical.Categorical.log_prob" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py property">
@@ -1333,7 +1333,7 @@ <h2><span class="hidden-section">Categorical</span><a class="headerlink" href="#
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.categorical.Categorical.sample">
-<span class="sig-name descname"><span class="pre">sample</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sample_shape</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.Size([])</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/categorical.html#Categorical.sample"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/categorical.py#L130"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.categorical.Categorical.sample" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">sample</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sample_shape</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.Size([])</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/categorical.html#Categorical.sample"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/categorical.py#L130"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.categorical.Categorical.sample" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py property">
@@ -1358,7 +1358,7 @@ <h2><span class="hidden-section">Categorical</span><a class="headerlink" href="#
 <h2><span class="hidden-section">Cauchy</span><a class="headerlink" href="#cauchy" title="Permalink to this heading">¶</a></h2>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributions.cauchy.Cauchy">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.cauchy.</span></span><span class="sig-name descname"><span class="pre">Cauchy</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">loc</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">validate_args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/cauchy.html#Cauchy"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/cauchy.py#L15"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.cauchy.Cauchy" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.cauchy.</span></span><span class="sig-name descname"><span class="pre">Cauchy</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">loc</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">validate_args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/cauchy.html#Cauchy"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/cauchy.py#L15"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.cauchy.Cauchy" title="Permalink to this definition">¶</a></dt>
 <dd><p>Bases: <a class="reference internal" href="#torch.distributions.distribution.Distribution" title="torch.distributions.distribution.Distribution"><code class="xref py py-class docutils literal notranslate"><span class="pre">Distribution</span></code></a></p>
 <p>Samples from a Cauchy (Lorentz) distribution. The distribution of the ratio of
 independent normally distributed random variables with means <cite>0</cite> follows a
@@ -1384,17 +1384,17 @@ <h2><span class="hidden-section">Cauchy</span><a class="headerlink" href="#cauch
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.cauchy.Cauchy.cdf">
-<span class="sig-name descname"><span class="pre">cdf</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/cauchy.html#Cauchy.cdf"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/cauchy.py#L83"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.cauchy.Cauchy.cdf" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">cdf</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/cauchy.html#Cauchy.cdf"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/cauchy.py#L83"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.cauchy.Cauchy.cdf" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.cauchy.Cauchy.entropy">
-<span class="sig-name descname"><span class="pre">entropy</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/cauchy.html#Cauchy.entropy"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/cauchy.py#L91"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.cauchy.Cauchy.entropy" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">entropy</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/cauchy.html#Cauchy.entropy"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/cauchy.py#L91"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.cauchy.Cauchy.entropy" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.cauchy.Cauchy.expand">
-<span class="sig-name descname"><span class="pre">expand</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">batch_shape</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_instance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/cauchy.html#Cauchy.expand"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/cauchy.py#L44"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.cauchy.Cauchy.expand" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">expand</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">batch_shape</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_instance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/cauchy.html#Cauchy.expand"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/cauchy.py#L44"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.cauchy.Cauchy.expand" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py attribute">
@@ -1404,12 +1404,12 @@ <h2><span class="hidden-section">Cauchy</span><a class="headerlink" href="#cauch
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.cauchy.Cauchy.icdf">
-<span class="sig-name descname"><span class="pre">icdf</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/cauchy.html#Cauchy.icdf"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/cauchy.py#L88"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.cauchy.Cauchy.icdf" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">icdf</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/cauchy.html#Cauchy.icdf"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/cauchy.py#L88"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.cauchy.Cauchy.icdf" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.cauchy.Cauchy.log_prob">
-<span class="sig-name descname"><span class="pre">log_prob</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/cauchy.html#Cauchy.log_prob"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/cauchy.py#L74"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.cauchy.Cauchy.log_prob" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">log_prob</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/cauchy.html#Cauchy.log_prob"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/cauchy.py#L74"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.cauchy.Cauchy.log_prob" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py property">
@@ -1424,7 +1424,7 @@ <h2><span class="hidden-section">Cauchy</span><a class="headerlink" href="#cauch
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.cauchy.Cauchy.rsample">
-<span class="sig-name descname"><span class="pre">rsample</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sample_shape</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.Size([])</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/cauchy.html#Cauchy.rsample"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/cauchy.py#L69"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.cauchy.Cauchy.rsample" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">rsample</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sample_shape</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.Size([])</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/cauchy.html#Cauchy.rsample"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/cauchy.py#L69"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.cauchy.Cauchy.rsample" title="Permalink to this definition">¶</a></dt>
 <dd><dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
 <dd class="field-odd"><p><a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a></p>
@@ -1449,7 +1449,7 @@ <h2><span class="hidden-section">Cauchy</span><a class="headerlink" href="#cauch
 <h2><span class="hidden-section">Chi2</span><a class="headerlink" href="#chi2" title="Permalink to this heading">¶</a></h2>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributions.chi2.Chi2">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.chi2.</span></span><span class="sig-name descname"><span class="pre">Chi2</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">df</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">validate_args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/chi2.html#Chi2"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/chi2.py#L10"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.chi2.Chi2" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.chi2.</span></span><span class="sig-name descname"><span class="pre">Chi2</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">df</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">validate_args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/chi2.html#Chi2"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/chi2.py#L10"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.chi2.Chi2" title="Permalink to this definition">¶</a></dt>
 <dd><p>Bases: <a class="reference internal" href="#torch.distributions.gamma.Gamma" title="torch.distributions.gamma.Gamma"><code class="xref py py-class docutils literal notranslate"><span class="pre">Gamma</span></code></a></p>
 <p>Creates a Chi-squared distribution parameterized by shape parameter <a class="reference internal" href="#torch.distributions.chi2.Chi2.df" title="torch.distributions.chi2.Chi2.df"><code class="xref py py-attr docutils literal notranslate"><span class="pre">df</span></code></a>.
 This is exactly equivalent to <code class="docutils literal notranslate"><span class="pre">Gamma(alpha=0.5*df,</span> <span class="pre">beta=0.5)</span></code></p>
@@ -1476,7 +1476,7 @@ <h2><span class="hidden-section">Chi2</span><a class="headerlink" href="#chi2" t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.chi2.Chi2.expand">
-<span class="sig-name descname"><span class="pre">expand</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">batch_shape</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_instance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/chi2.html#Chi2.expand"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/chi2.py#L30"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.chi2.Chi2.expand" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">expand</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">batch_shape</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_instance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/chi2.html#Chi2.expand"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/chi2.py#L30"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.chi2.Chi2.expand" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 </dd></dl>
@@ -1486,7 +1486,7 @@ <h2><span class="hidden-section">Chi2</span><a class="headerlink" href="#chi2" t
 <h2><span class="hidden-section">ContinuousBernoulli</span><a class="headerlink" href="#continuousbernoulli" title="Permalink to this heading">¶</a></h2>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributions.continuous_bernoulli.ContinuousBernoulli">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.continuous_bernoulli.</span></span><span class="sig-name descname"><span class="pre">ContinuousBernoulli</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">probs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">logits</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lims</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">(0.499,</span> <span class="pre">0.501)</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">validate_args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/continuous_bernoulli.html#ContinuousBernoulli"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/continuous_bernoulli.py#L22"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.continuous_bernoulli.ContinuousBernoulli" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.continuous_bernoulli.</span></span><span class="sig-name descname"><span class="pre">ContinuousBernoulli</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">probs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">logits</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lims</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">(0.499,</span> <span class="pre">0.501)</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">validate_args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/continuous_bernoulli.html#ContinuousBernoulli"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/continuous_bernoulli.py#L22"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.continuous_bernoulli.ContinuousBernoulli" title="Permalink to this definition">¶</a></dt>
 <dd><p>Bases: <a class="reference internal" href="#torch.distributions.exp_family.ExponentialFamily" title="torch.distributions.exp_family.ExponentialFamily"><code class="xref py py-class docutils literal notranslate"><span class="pre">ExponentialFamily</span></code></a></p>
 <p>Creates a continuous Bernoulli distribution parameterized by <a class="reference internal" href="#torch.distributions.continuous_bernoulli.ContinuousBernoulli.probs" title="torch.distributions.continuous_bernoulli.ContinuousBernoulli.probs"><code class="xref py py-attr docutils literal notranslate"><span class="pre">probs</span></code></a>
 or <a class="reference internal" href="#torch.distributions.continuous_bernoulli.ContinuousBernoulli.logits" title="torch.distributions.continuous_bernoulli.ContinuousBernoulli.logits"><code class="xref py py-attr docutils literal notranslate"><span class="pre">logits</span></code></a> (but not both).</p>
@@ -1519,17 +1519,17 @@ <h2><span class="hidden-section">ContinuousBernoulli</span><a class="headerlink"
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.continuous_bernoulli.ContinuousBernoulli.cdf">
-<span class="sig-name descname"><span class="pre">cdf</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/continuous_bernoulli.html#ContinuousBernoulli.cdf"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/continuous_bernoulli.py#L185"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.continuous_bernoulli.ContinuousBernoulli.cdf" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">cdf</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/continuous_bernoulli.html#ContinuousBernoulli.cdf"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/continuous_bernoulli.py#L185"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.continuous_bernoulli.ContinuousBernoulli.cdf" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.continuous_bernoulli.ContinuousBernoulli.entropy">
-<span class="sig-name descname"><span class="pre">entropy</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/continuous_bernoulli.html#ContinuousBernoulli.entropy"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/continuous_bernoulli.py#L213"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.continuous_bernoulli.ContinuousBernoulli.entropy" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">entropy</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/continuous_bernoulli.html#ContinuousBernoulli.entropy"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/continuous_bernoulli.py#L213"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.continuous_bernoulli.ContinuousBernoulli.entropy" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.continuous_bernoulli.ContinuousBernoulli.expand">
-<span class="sig-name descname"><span class="pre">expand</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">batch_shape</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_instance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/continuous_bernoulli.html#ContinuousBernoulli.expand"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/continuous_bernoulli.py#L80"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.continuous_bernoulli.ContinuousBernoulli.expand" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">expand</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">batch_shape</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_instance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/continuous_bernoulli.html#ContinuousBernoulli.expand"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/continuous_bernoulli.py#L80"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.continuous_bernoulli.ContinuousBernoulli.expand" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py attribute">
@@ -1539,12 +1539,12 @@ <h2><span class="hidden-section">ContinuousBernoulli</span><a class="headerlink"
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.continuous_bernoulli.ContinuousBernoulli.icdf">
-<span class="sig-name descname"><span class="pre">icdf</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/continuous_bernoulli.html#ContinuousBernoulli.icdf"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/continuous_bernoulli.py#L201"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.continuous_bernoulli.ContinuousBernoulli.icdf" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">icdf</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/continuous_bernoulli.html#ContinuousBernoulli.icdf"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/continuous_bernoulli.py#L201"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.continuous_bernoulli.ContinuousBernoulli.icdf" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.continuous_bernoulli.ContinuousBernoulli.log_prob">
-<span class="sig-name descname"><span class="pre">log_prob</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/continuous_bernoulli.html#ContinuousBernoulli.log_prob"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/continuous_bernoulli.py#L176"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.continuous_bernoulli.ContinuousBernoulli.log_prob" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">log_prob</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/continuous_bernoulli.html#ContinuousBernoulli.log_prob"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/continuous_bernoulli.py#L176"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.continuous_bernoulli.ContinuousBernoulli.log_prob" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py property">
@@ -1569,7 +1569,7 @@ <h2><span class="hidden-section">ContinuousBernoulli</span><a class="headerlink"
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.continuous_bernoulli.ContinuousBernoulli.rsample">
-<span class="sig-name descname"><span class="pre">rsample</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sample_shape</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.Size([])</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/continuous_bernoulli.html#ContinuousBernoulli.rsample"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/continuous_bernoulli.py#L171"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.continuous_bernoulli.ContinuousBernoulli.rsample" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">rsample</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sample_shape</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.Size([])</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/continuous_bernoulli.html#ContinuousBernoulli.rsample"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/continuous_bernoulli.py#L171"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.continuous_bernoulli.ContinuousBernoulli.rsample" title="Permalink to this definition">¶</a></dt>
 <dd><dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
 <dd class="field-odd"><p><a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a></p>
@@ -1579,7 +1579,7 @@ <h2><span class="hidden-section">ContinuousBernoulli</span><a class="headerlink"
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.continuous_bernoulli.ContinuousBernoulli.sample">
-<span class="sig-name descname"><span class="pre">sample</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sample_shape</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.Size([])</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/continuous_bernoulli.html#ContinuousBernoulli.sample"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/continuous_bernoulli.py#L165"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.continuous_bernoulli.ContinuousBernoulli.sample" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">sample</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sample_shape</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.Size([])</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/continuous_bernoulli.html#ContinuousBernoulli.sample"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/continuous_bernoulli.py#L165"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.continuous_bernoulli.ContinuousBernoulli.sample" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py property">
@@ -1604,7 +1604,7 @@ <h2><span class="hidden-section">ContinuousBernoulli</span><a class="headerlink"
 <h2><span class="hidden-section">Dirichlet</span><a class="headerlink" href="#dirichlet" title="Permalink to this heading">¶</a></h2>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributions.dirichlet.Dirichlet">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.dirichlet.</span></span><span class="sig-name descname"><span class="pre">Dirichlet</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">concentration</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">validate_args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/dirichlet.html#Dirichlet"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/dirichlet.py#L35"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.dirichlet.Dirichlet" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.dirichlet.</span></span><span class="sig-name descname"><span class="pre">Dirichlet</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">concentration</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">validate_args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/dirichlet.html#Dirichlet"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/dirichlet.py#L35"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.dirichlet.Dirichlet" title="Permalink to this definition">¶</a></dt>
 <dd><p>Bases: <a class="reference internal" href="#torch.distributions.exp_family.ExponentialFamily" title="torch.distributions.exp_family.ExponentialFamily"><code class="xref py py-class docutils literal notranslate"><span class="pre">ExponentialFamily</span></code></a></p>
 <p>Creates a Dirichlet distribution parameterized by concentration <code class="xref py py-attr docutils literal notranslate"><span class="pre">concentration</span></code>.</p>
 <p>Example:</p>
@@ -1626,12 +1626,12 @@ <h2><span class="hidden-section">Dirichlet</span><a class="headerlink" href="#di
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.dirichlet.Dirichlet.entropy">
-<span class="sig-name descname"><span class="pre">entropy</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/dirichlet.html#Dirichlet.entropy"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/dirichlet.py#L112"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.dirichlet.Dirichlet.entropy" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">entropy</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/dirichlet.html#Dirichlet.entropy"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/dirichlet.py#L112"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.dirichlet.Dirichlet.entropy" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.dirichlet.Dirichlet.expand">
-<span class="sig-name descname"><span class="pre">expand</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">batch_shape</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_instance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/dirichlet.html#Dirichlet.expand"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/dirichlet.py#L65"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.dirichlet.Dirichlet.expand" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">expand</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">batch_shape</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_instance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/dirichlet.html#Dirichlet.expand"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/dirichlet.py#L65"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.dirichlet.Dirichlet.expand" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py attribute">
@@ -1641,7 +1641,7 @@ <h2><span class="hidden-section">Dirichlet</span><a class="headerlink" href="#di
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.dirichlet.Dirichlet.log_prob">
-<span class="sig-name descname"><span class="pre">log_prob</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/dirichlet.html#Dirichlet.log_prob"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/dirichlet.py#L80"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.dirichlet.Dirichlet.log_prob" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">log_prob</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/dirichlet.html#Dirichlet.log_prob"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/dirichlet.py#L80"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.dirichlet.Dirichlet.log_prob" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py property">
@@ -1656,7 +1656,7 @@ <h2><span class="hidden-section">Dirichlet</span><a class="headerlink" href="#di
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.dirichlet.Dirichlet.rsample">
-<span class="sig-name descname"><span class="pre">rsample</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sample_shape</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">()</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/dirichlet.html#Dirichlet.rsample"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/dirichlet.py#L75"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.dirichlet.Dirichlet.rsample" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">rsample</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sample_shape</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">()</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/dirichlet.html#Dirichlet.rsample"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/dirichlet.py#L75"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.dirichlet.Dirichlet.rsample" title="Permalink to this definition">¶</a></dt>
 <dd><dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
 <dd class="field-odd"><p><a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a></p>
@@ -1681,7 +1681,7 @@ <h2><span class="hidden-section">Dirichlet</span><a class="headerlink" href="#di
 <h2><span class="hidden-section">Exponential</span><a class="headerlink" href="#exponential" title="Permalink to this heading">¶</a></h2>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributions.exponential.Exponential">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.exponential.</span></span><span class="sig-name descname"><span class="pre">Exponential</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">rate</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">validate_args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/exponential.html#Exponential"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/exponential.py#L13"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.exponential.Exponential" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.exponential.</span></span><span class="sig-name descname"><span class="pre">Exponential</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">rate</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">validate_args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/exponential.html#Exponential"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/exponential.py#L13"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.exponential.Exponential" title="Permalink to this definition">¶</a></dt>
 <dd><p>Bases: <a class="reference internal" href="#torch.distributions.exp_family.ExponentialFamily" title="torch.distributions.exp_family.ExponentialFamily"><code class="xref py py-class docutils literal notranslate"><span class="pre">ExponentialFamily</span></code></a></p>
 <p>Creates a Exponential distribution parameterized by <code class="xref py py-attr docutils literal notranslate"><span class="pre">rate</span></code>.</p>
 <p>Example:</p>
@@ -1702,17 +1702,17 @@ <h2><span class="hidden-section">Exponential</span><a class="headerlink" href="#
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.exponential.Exponential.cdf">
-<span class="sig-name descname"><span class="pre">cdf</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/exponential.html#Exponential.cdf"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/exponential.py#L70"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.exponential.Exponential.cdf" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">cdf</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/exponential.html#Exponential.cdf"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/exponential.py#L70"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.exponential.Exponential.cdf" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.exponential.Exponential.entropy">
-<span class="sig-name descname"><span class="pre">entropy</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/exponential.html#Exponential.entropy"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/exponential.py#L78"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.exponential.Exponential.entropy" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">entropy</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/exponential.html#Exponential.entropy"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/exponential.py#L78"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.exponential.Exponential.entropy" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.exponential.Exponential.expand">
-<span class="sig-name descname"><span class="pre">expand</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">batch_shape</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_instance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/exponential.html#Exponential.expand"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/exponential.py#L53"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.exponential.Exponential.expand" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">expand</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">batch_shape</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_instance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/exponential.html#Exponential.expand"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/exponential.py#L53"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.exponential.Exponential.expand" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py attribute">
@@ -1722,12 +1722,12 @@ <h2><span class="hidden-section">Exponential</span><a class="headerlink" href="#
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.exponential.Exponential.icdf">
-<span class="sig-name descname"><span class="pre">icdf</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/exponential.html#Exponential.icdf"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/exponential.py#L75"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.exponential.Exponential.icdf" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">icdf</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/exponential.html#Exponential.icdf"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/exponential.py#L75"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.exponential.Exponential.icdf" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.exponential.Exponential.log_prob">
-<span class="sig-name descname"><span class="pre">log_prob</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/exponential.html#Exponential.log_prob"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/exponential.py#L65"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.exponential.Exponential.log_prob" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">log_prob</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/exponential.html#Exponential.log_prob"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/exponential.py#L65"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.exponential.Exponential.log_prob" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py property">
@@ -1742,7 +1742,7 @@ <h2><span class="hidden-section">Exponential</span><a class="headerlink" href="#
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.exponential.Exponential.rsample">
-<span class="sig-name descname"><span class="pre">rsample</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sample_shape</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.Size([])</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/exponential.html#Exponential.rsample"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/exponential.py#L61"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.exponential.Exponential.rsample" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">rsample</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sample_shape</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.Size([])</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/exponential.html#Exponential.rsample"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/exponential.py#L61"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.exponential.Exponential.rsample" title="Permalink to this definition">¶</a></dt>
 <dd><dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
 <dd class="field-odd"><p><a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a></p>
@@ -1772,7 +1772,7 @@ <h2><span class="hidden-section">Exponential</span><a class="headerlink" href="#
 <h2><span class="hidden-section">FisherSnedecor</span><a class="headerlink" href="#fishersnedecor" title="Permalink to this heading">¶</a></h2>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributions.fishersnedecor.FisherSnedecor">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.fishersnedecor.</span></span><span class="sig-name descname"><span class="pre">FisherSnedecor</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">df1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">df2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">validate_args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/fishersnedecor.html#FisherSnedecor"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/fishersnedecor.py#L14"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.fishersnedecor.FisherSnedecor" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.fishersnedecor.</span></span><span class="sig-name descname"><span class="pre">FisherSnedecor</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">df1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">df2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">validate_args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/fishersnedecor.html#FisherSnedecor"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/fishersnedecor.py#L14"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.fishersnedecor.FisherSnedecor" title="Permalink to this definition">¶</a></dt>
 <dd><p>Bases: <a class="reference internal" href="#torch.distributions.distribution.Distribution" title="torch.distributions.distribution.Distribution"><code class="xref py py-class docutils literal notranslate"><span class="pre">Distribution</span></code></a></p>
 <p>Creates a Fisher-Snedecor distribution parameterized by <code class="xref py py-attr docutils literal notranslate"><span class="pre">df1</span></code> and <code class="xref py py-attr docutils literal notranslate"><span class="pre">df2</span></code>.</p>
 <p>Example:</p>
@@ -1796,7 +1796,7 @@ <h2><span class="hidden-section">FisherSnedecor</span><a class="headerlink" href
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.fishersnedecor.FisherSnedecor.expand">
-<span class="sig-name descname"><span class="pre">expand</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">batch_shape</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_instance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/fishersnedecor.html#FisherSnedecor.expand"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/fishersnedecor.py#L44"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.fishersnedecor.FisherSnedecor.expand" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">expand</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">batch_shape</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_instance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/fishersnedecor.html#FisherSnedecor.expand"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/fishersnedecor.py#L44"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.fishersnedecor.FisherSnedecor.expand" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py attribute">
@@ -1806,7 +1806,7 @@ <h2><span class="hidden-section">FisherSnedecor</span><a class="headerlink" href
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.fishersnedecor.FisherSnedecor.log_prob">
-<span class="sig-name descname"><span class="pre">log_prob</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/fishersnedecor.html#FisherSnedecor.log_prob"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/fishersnedecor.py#L90"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.fishersnedecor.FisherSnedecor.log_prob" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">log_prob</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/fishersnedecor.html#FisherSnedecor.log_prob"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/fishersnedecor.py#L90"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.fishersnedecor.FisherSnedecor.log_prob" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py property">
@@ -1821,7 +1821,7 @@ <h2><span class="hidden-section">FisherSnedecor</span><a class="headerlink" href
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.fishersnedecor.FisherSnedecor.rsample">
-<span class="sig-name descname"><span class="pre">rsample</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sample_shape</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.Size([])</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/fishersnedecor.html#FisherSnedecor.rsample"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/fishersnedecor.py#L78"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.fishersnedecor.FisherSnedecor.rsample" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">rsample</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sample_shape</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.Size([])</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/fishersnedecor.html#FisherSnedecor.rsample"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/fishersnedecor.py#L78"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.fishersnedecor.FisherSnedecor.rsample" title="Permalink to this definition">¶</a></dt>
 <dd><dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
 <dd class="field-odd"><p><a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a></p>
@@ -1846,7 +1846,7 @@ <h2><span class="hidden-section">FisherSnedecor</span><a class="headerlink" href
 <h2><span class="hidden-section">Gamma</span><a class="headerlink" href="#gamma" title="Permalink to this heading">¶</a></h2>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributions.gamma.Gamma">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.gamma.</span></span><span class="sig-name descname"><span class="pre">Gamma</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">concentration</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">rate</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">validate_args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/gamma.html#Gamma"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/gamma.py#L17"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.gamma.Gamma" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.gamma.</span></span><span class="sig-name descname"><span class="pre">Gamma</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">concentration</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">rate</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">validate_args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/gamma.html#Gamma"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/gamma.py#L17"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.gamma.Gamma" title="Permalink to this definition">¶</a></dt>
 <dd><p>Bases: <a class="reference internal" href="#torch.distributions.exp_family.ExponentialFamily" title="torch.distributions.exp_family.ExponentialFamily"><code class="xref py py-class docutils literal notranslate"><span class="pre">ExponentialFamily</span></code></a></p>
 <p>Creates a Gamma distribution parameterized by shape <code class="xref py py-attr docutils literal notranslate"><span class="pre">concentration</span></code> and <code class="xref py py-attr docutils literal notranslate"><span class="pre">rate</span></code>.</p>
 <p>Example:</p>
@@ -1872,17 +1872,17 @@ <h2><span class="hidden-section">Gamma</span><a class="headerlink" href="#gamma"
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.gamma.Gamma.cdf">
-<span class="sig-name descname"><span class="pre">cdf</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/gamma.html#Gamma.cdf"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/gamma.py#L107"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.gamma.Gamma.cdf" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">cdf</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/gamma.html#Gamma.cdf"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/gamma.py#L107"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.gamma.Gamma.cdf" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.gamma.Gamma.entropy">
-<span class="sig-name descname"><span class="pre">entropy</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/gamma.html#Gamma.entropy"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/gamma.py#L92"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.gamma.Gamma.entropy" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">entropy</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/gamma.html#Gamma.entropy"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/gamma.py#L92"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.gamma.Gamma.entropy" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.gamma.Gamma.expand">
-<span class="sig-name descname"><span class="pre">expand</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">batch_shape</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_instance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/gamma.html#Gamma.expand"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/gamma.py#L62"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.gamma.Gamma.expand" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">expand</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">batch_shape</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_instance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/gamma.html#Gamma.expand"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/gamma.py#L62"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.gamma.Gamma.expand" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py attribute">
@@ -1892,7 +1892,7 @@ <h2><span class="hidden-section">Gamma</span><a class="headerlink" href="#gamma"
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.gamma.Gamma.log_prob">
-<span class="sig-name descname"><span class="pre">log_prob</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/gamma.html#Gamma.log_prob"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/gamma.py#L81"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.gamma.Gamma.log_prob" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">log_prob</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/gamma.html#Gamma.log_prob"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/gamma.py#L81"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.gamma.Gamma.log_prob" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py property">
@@ -1907,7 +1907,7 @@ <h2><span class="hidden-section">Gamma</span><a class="headerlink" href="#gamma"
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.gamma.Gamma.rsample">
-<span class="sig-name descname"><span class="pre">rsample</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sample_shape</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.Size([])</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/gamma.html#Gamma.rsample"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/gamma.py#L71"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.gamma.Gamma.rsample" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">rsample</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sample_shape</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.Size([])</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/gamma.html#Gamma.rsample"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/gamma.py#L71"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.gamma.Gamma.rsample" title="Permalink to this definition">¶</a></dt>
 <dd><dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
 <dd class="field-odd"><p><a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a></p>
@@ -1932,7 +1932,7 @@ <h2><span class="hidden-section">Gamma</span><a class="headerlink" href="#gamma"
 <h2><span class="hidden-section">Geometric</span><a class="headerlink" href="#geometric" title="Permalink to this heading">¶</a></h2>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributions.geometric.Geometric">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.geometric.</span></span><span class="sig-name descname"><span class="pre">Geometric</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">probs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">logits</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">validate_args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/geometric.html#Geometric"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/geometric.py#L19"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.geometric.Geometric" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.geometric.</span></span><span class="sig-name descname"><span class="pre">Geometric</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">probs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">logits</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">validate_args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/geometric.html#Geometric"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/geometric.py#L19"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.geometric.Geometric" title="Permalink to this definition">¶</a></dt>
 <dd><p>Bases: <a class="reference internal" href="#torch.distributions.distribution.Distribution" title="torch.distributions.distribution.Distribution"><code class="xref py py-class docutils literal notranslate"><span class="pre">Distribution</span></code></a></p>
 <p>Creates a Geometric distribution parameterized by <a class="reference internal" href="#torch.distributions.geometric.Geometric.probs" title="torch.distributions.geometric.Geometric.probs"><code class="xref py py-attr docutils literal notranslate"><span class="pre">probs</span></code></a>,
 where <a class="reference internal" href="#torch.distributions.geometric.Geometric.probs" title="torch.distributions.geometric.Geometric.probs"><code class="xref py py-attr docutils literal notranslate"><span class="pre">probs</span></code></a> is the probability of success of Bernoulli trials.</p>
@@ -1964,17 +1964,17 @@ <h2><span class="hidden-section">Geometric</span><a class="headerlink" href="#ge
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.geometric.Geometric.entropy">
-<span class="sig-name descname"><span class="pre">entropy</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/geometric.html#Geometric.entropy"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/geometric.py#L126"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.geometric.Geometric.entropy" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">entropy</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/geometric.html#Geometric.entropy"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/geometric.py#L126"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.geometric.Geometric.entropy" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.geometric.Geometric.expand">
-<span class="sig-name descname"><span class="pre">expand</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">batch_shape</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_instance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/geometric.html#Geometric.expand"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/geometric.py#L75"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.geometric.Geometric.expand" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">expand</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">batch_shape</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_instance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/geometric.html#Geometric.expand"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/geometric.py#L75"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.geometric.Geometric.expand" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.geometric.Geometric.log_prob">
-<span class="sig-name descname"><span class="pre">log_prob</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/geometric.html#Geometric.log_prob"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/geometric.py#L118"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.geometric.Geometric.log_prob" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">log_prob</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/geometric.html#Geometric.log_prob"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/geometric.py#L118"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.geometric.Geometric.log_prob" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py property">
@@ -1999,7 +1999,7 @@ <h2><span class="hidden-section">Geometric</span><a class="headerlink" href="#ge
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.geometric.Geometric.sample">
-<span class="sig-name descname"><span class="pre">sample</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sample_shape</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.Size([])</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/geometric.html#Geometric.sample"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/geometric.py#L106"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.geometric.Geometric.sample" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">sample</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sample_shape</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.Size([])</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/geometric.html#Geometric.sample"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/geometric.py#L106"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.geometric.Geometric.sample" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py attribute">
@@ -2019,7 +2019,7 @@ <h2><span class="hidden-section">Geometric</span><a class="headerlink" href="#ge
 <h2><span class="hidden-section">Gumbel</span><a class="headerlink" href="#gumbel" title="Permalink to this heading">¶</a></h2>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributions.gumbel.Gumbel">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.gumbel.</span></span><span class="sig-name descname"><span class="pre">Gumbel</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">loc</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">validate_args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/gumbel.html#Gumbel"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/gumbel.py#L17"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.gumbel.Gumbel" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.gumbel.</span></span><span class="sig-name descname"><span class="pre">Gumbel</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">loc</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">validate_args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/gumbel.html#Gumbel"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/gumbel.py#L17"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.gumbel.Gumbel" title="Permalink to this definition">¶</a></dt>
 <dd><p>Bases: <a class="reference internal" href="#torch.distributions.transformed_distribution.TransformedDistribution" title="torch.distributions.transformed_distribution.TransformedDistribution"><code class="xref py py-class docutils literal notranslate"><span class="pre">TransformedDistribution</span></code></a></p>
 <p>Samples from a Gumbel Distribution.</p>
 <p>Examples:</p>
@@ -2043,17 +2043,17 @@ <h2><span class="hidden-section">Gumbel</span><a class="headerlink" href="#gumbe
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.gumbel.Gumbel.entropy">
-<span class="sig-name descname"><span class="pre">entropy</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/gumbel.html#Gumbel.entropy"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/gumbel.py#L83"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.gumbel.Gumbel.entropy" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">entropy</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/gumbel.html#Gumbel.entropy"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/gumbel.py#L83"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.gumbel.Gumbel.entropy" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.gumbel.Gumbel.expand">
-<span class="sig-name descname"><span class="pre">expand</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">batch_shape</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_instance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/gumbel.html#Gumbel.expand"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/gumbel.py#L54"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.gumbel.Gumbel.expand" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">expand</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">batch_shape</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_instance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/gumbel.html#Gumbel.expand"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/gumbel.py#L54"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.gumbel.Gumbel.expand" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.gumbel.Gumbel.log_prob">
-<span class="sig-name descname"><span class="pre">log_prob</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/gumbel.html#Gumbel.log_prob"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/gumbel.py#L61"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.gumbel.Gumbel.log_prob" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">log_prob</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/gumbel.html#Gumbel.log_prob"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/gumbel.py#L61"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.gumbel.Gumbel.log_prob" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py property">
@@ -2088,7 +2088,7 @@ <h2><span class="hidden-section">Gumbel</span><a class="headerlink" href="#gumbe
 <h2><span class="hidden-section">HalfCauchy</span><a class="headerlink" href="#halfcauchy" title="Permalink to this heading">¶</a></h2>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributions.half_cauchy.HalfCauchy">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.half_cauchy.</span></span><span class="sig-name descname"><span class="pre">HalfCauchy</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">scale</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">validate_args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/half_cauchy.html#HalfCauchy"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/half_cauchy.py#L15"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.half_cauchy.HalfCauchy" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.half_cauchy.</span></span><span class="sig-name descname"><span class="pre">HalfCauchy</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">scale</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">validate_args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/half_cauchy.html#HalfCauchy"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/half_cauchy.py#L15"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.half_cauchy.HalfCauchy" title="Permalink to this definition">¶</a></dt>
 <dd><p>Bases: <a class="reference internal" href="#torch.distributions.transformed_distribution.TransformedDistribution" title="torch.distributions.transformed_distribution.TransformedDistribution"><code class="xref py py-class docutils literal notranslate"><span class="pre">TransformedDistribution</span></code></a></p>
 <p>Creates a half-Cauchy distribution parameterized by <cite>scale</cite> where:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">X</span> <span class="o">~</span> <span class="n">Cauchy</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">scale</span><span class="p">)</span>
@@ -2113,17 +2113,17 @@ <h2><span class="hidden-section">HalfCauchy</span><a class="headerlink" href="#h
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.half_cauchy.HalfCauchy.cdf">
-<span class="sig-name descname"><span class="pre">cdf</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/half_cauchy.html#HalfCauchy.cdf"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/half_cauchy.py#L75"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.half_cauchy.HalfCauchy.cdf" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">cdf</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/half_cauchy.html#HalfCauchy.cdf"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/half_cauchy.py#L75"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.half_cauchy.HalfCauchy.cdf" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.half_cauchy.HalfCauchy.entropy">
-<span class="sig-name descname"><span class="pre">entropy</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/half_cauchy.html#HalfCauchy.entropy"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/half_cauchy.py#L83"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.half_cauchy.HalfCauchy.entropy" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">entropy</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/half_cauchy.html#HalfCauchy.entropy"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/half_cauchy.py#L83"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.half_cauchy.HalfCauchy.entropy" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.half_cauchy.HalfCauchy.expand">
-<span class="sig-name descname"><span class="pre">expand</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">batch_shape</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_instance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/half_cauchy.html#HalfCauchy.expand"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/half_cauchy.py#L40"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.half_cauchy.HalfCauchy.expand" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">expand</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">batch_shape</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_instance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/half_cauchy.html#HalfCauchy.expand"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/half_cauchy.py#L40"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.half_cauchy.HalfCauchy.expand" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py attribute">
@@ -2133,12 +2133,12 @@ <h2><span class="hidden-section">HalfCauchy</span><a class="headerlink" href="#h
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.half_cauchy.HalfCauchy.icdf">
-<span class="sig-name descname"><span class="pre">icdf</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">prob</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/half_cauchy.html#HalfCauchy.icdf"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/half_cauchy.py#L80"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.half_cauchy.HalfCauchy.icdf" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">icdf</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">prob</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/half_cauchy.html#HalfCauchy.icdf"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/half_cauchy.py#L80"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.half_cauchy.HalfCauchy.icdf" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.half_cauchy.HalfCauchy.log_prob">
-<span class="sig-name descname"><span class="pre">log_prob</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/half_cauchy.html#HalfCauchy.log_prob"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/half_cauchy.py#L65"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.half_cauchy.HalfCauchy.log_prob" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">log_prob</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/half_cauchy.html#HalfCauchy.log_prob"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/half_cauchy.py#L65"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.half_cauchy.HalfCauchy.log_prob" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py property">
@@ -2173,7 +2173,7 @@ <h2><span class="hidden-section">HalfCauchy</span><a class="headerlink" href="#h
 <h2><span class="hidden-section">HalfNormal</span><a class="headerlink" href="#halfnormal" title="Permalink to this heading">¶</a></h2>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributions.half_normal.HalfNormal">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.half_normal.</span></span><span class="sig-name descname"><span class="pre">HalfNormal</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">scale</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">validate_args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/half_normal.html#HalfNormal"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/half_normal.py#L15"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.half_normal.HalfNormal" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.half_normal.</span></span><span class="sig-name descname"><span class="pre">HalfNormal</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">scale</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">validate_args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/half_normal.html#HalfNormal"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/half_normal.py#L15"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.half_normal.HalfNormal" title="Permalink to this definition">¶</a></dt>
 <dd><p>Bases: <a class="reference internal" href="#torch.distributions.transformed_distribution.TransformedDistribution" title="torch.distributions.transformed_distribution.TransformedDistribution"><code class="xref py py-class docutils literal notranslate"><span class="pre">TransformedDistribution</span></code></a></p>
 <p>Creates a half-normal distribution parameterized by <cite>scale</cite> where:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">X</span> <span class="o">~</span> <span class="n">Normal</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">scale</span><span class="p">)</span>
@@ -2198,17 +2198,17 @@ <h2><span class="hidden-section">HalfNormal</span><a class="headerlink" href="#h
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.half_normal.HalfNormal.cdf">
-<span class="sig-name descname"><span class="pre">cdf</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/half_normal.html#HalfNormal.cdf"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/half_normal.py#L67"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.half_normal.HalfNormal.cdf" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">cdf</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/half_normal.html#HalfNormal.cdf"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/half_normal.py#L67"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.half_normal.HalfNormal.cdf" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.half_normal.HalfNormal.entropy">
-<span class="sig-name descname"><span class="pre">entropy</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/half_normal.html#HalfNormal.entropy"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/half_normal.py#L75"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.half_normal.HalfNormal.entropy" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">entropy</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/half_normal.html#HalfNormal.entropy"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/half_normal.py#L75"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.half_normal.HalfNormal.entropy" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.half_normal.HalfNormal.expand">
-<span class="sig-name descname"><span class="pre">expand</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">batch_shape</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_instance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/half_normal.html#HalfNormal.expand"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/half_normal.py#L40"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.half_normal.HalfNormal.expand" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">expand</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">batch_shape</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_instance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/half_normal.html#HalfNormal.expand"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/half_normal.py#L40"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.half_normal.HalfNormal.expand" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py attribute">
@@ -2218,12 +2218,12 @@ <h2><span class="hidden-section">HalfNormal</span><a class="headerlink" href="#h
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.half_normal.HalfNormal.icdf">
-<span class="sig-name descname"><span class="pre">icdf</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">prob</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/half_normal.html#HalfNormal.icdf"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/half_normal.py#L72"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.half_normal.HalfNormal.icdf" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">icdf</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">prob</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/half_normal.html#HalfNormal.icdf"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/half_normal.py#L72"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.half_normal.HalfNormal.icdf" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.half_normal.HalfNormal.log_prob">
-<span class="sig-name descname"><span class="pre">log_prob</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/half_normal.html#HalfNormal.log_prob"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/half_normal.py#L60"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.half_normal.HalfNormal.log_prob" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">log_prob</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/half_normal.html#HalfNormal.log_prob"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/half_normal.py#L60"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.half_normal.HalfNormal.log_prob" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py property">
@@ -2258,7 +2258,7 @@ <h2><span class="hidden-section">HalfNormal</span><a class="headerlink" href="#h
 <h2><span class="hidden-section">Independent</span><a class="headerlink" href="#independent" title="Permalink to this heading">¶</a></h2>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributions.independent.Independent">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.independent.</span></span><span class="sig-name descname"><span class="pre">Independent</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">base_distribution</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reinterpreted_batch_ndims</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">validate_args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/independent.html#Independent"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/independent.py#L14"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.independent.Independent" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.independent.</span></span><span class="sig-name descname"><span class="pre">Independent</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">base_distribution</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reinterpreted_batch_ndims</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">validate_args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/independent.html#Independent"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/independent.py#L14"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.independent.Independent" title="Permalink to this definition">¶</a></dt>
 <dd><p>Bases: <a class="reference internal" href="#torch.distributions.distribution.Distribution" title="torch.distributions.distribution.Distribution"><code class="xref py py-class docutils literal notranslate"><span class="pre">Distribution</span></code></a></p>
 <p>Reinterprets some of the batch dims of a distribution as event dims.</p>
 <p>This is mainly useful for changing the shape of the result of
@@ -2297,17 +2297,17 @@ <h2><span class="hidden-section">Independent</span><a class="headerlink" href="#
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.independent.Independent.entropy">
-<span class="sig-name descname"><span class="pre">entropy</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/independent.html#Independent.entropy"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/independent.py#L113"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.independent.Independent.entropy" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">entropy</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/independent.html#Independent.entropy"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/independent.py#L113"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.independent.Independent.entropy" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.independent.Independent.enumerate_support">
-<span class="sig-name descname"><span class="pre">enumerate_support</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">expand</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/independent.html#Independent.enumerate_support"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/independent.py#L117"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.independent.Independent.enumerate_support" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">enumerate_support</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">expand</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/independent.html#Independent.enumerate_support"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/independent.py#L117"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.independent.Independent.enumerate_support" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.independent.Independent.expand">
-<span class="sig-name descname"><span class="pre">expand</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">batch_shape</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_instance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/independent.html#Independent.expand"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/independent.py#L61"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.independent.Independent.expand" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">expand</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">batch_shape</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_instance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/independent.html#Independent.expand"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/independent.py#L61"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.independent.Independent.expand" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py property">
@@ -2322,7 +2322,7 @@ <h2><span class="hidden-section">Independent</span><a class="headerlink" href="#
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.independent.Independent.log_prob">
-<span class="sig-name descname"><span class="pre">log_prob</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/independent.html#Independent.log_prob"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/independent.py#L109"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.independent.Independent.log_prob" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">log_prob</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/independent.html#Independent.log_prob"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/independent.py#L109"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.independent.Independent.log_prob" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py property">
@@ -2337,7 +2337,7 @@ <h2><span class="hidden-section">Independent</span><a class="headerlink" href="#
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.independent.Independent.rsample">
-<span class="sig-name descname"><span class="pre">rsample</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sample_shape</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.Size([])</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/independent.html#Independent.rsample"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/independent.py#L106"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.independent.Independent.rsample" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">rsample</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sample_shape</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.Size([])</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/independent.html#Independent.rsample"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/independent.py#L106"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.independent.Independent.rsample" title="Permalink to this definition">¶</a></dt>
 <dd><dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
 <dd class="field-odd"><p><a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a></p>
@@ -2347,7 +2347,7 @@ <h2><span class="hidden-section">Independent</span><a class="headerlink" href="#
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.independent.Independent.sample">
-<span class="sig-name descname"><span class="pre">sample</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sample_shape</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.Size([])</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/independent.html#Independent.sample"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/independent.py#L103"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.independent.Independent.sample" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">sample</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sample_shape</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.Size([])</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/independent.html#Independent.sample"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/independent.py#L103"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.independent.Independent.sample" title="Permalink to this definition">¶</a></dt>
 <dd><dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
 <dd class="field-odd"><p><a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a></p>
@@ -2377,7 +2377,7 @@ <h2><span class="hidden-section">Independent</span><a class="headerlink" href="#
 <h2><span class="hidden-section">InverseGamma</span><a class="headerlink" href="#inversegamma" title="Permalink to this heading">¶</a></h2>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributions.inverse_gamma.InverseGamma">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.inverse_gamma.</span></span><span class="sig-name descname"><span class="pre">InverseGamma</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">concentration</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">rate</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">validate_args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/inverse_gamma.html#InverseGamma"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/inverse_gamma.py#L13"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.inverse_gamma.InverseGamma" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.inverse_gamma.</span></span><span class="sig-name descname"><span class="pre">InverseGamma</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">concentration</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">rate</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">validate_args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/inverse_gamma.html#InverseGamma"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/inverse_gamma.py#L13"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.inverse_gamma.InverseGamma" title="Permalink to this definition">¶</a></dt>
 <dd><p>Bases: <a class="reference internal" href="#torch.distributions.transformed_distribution.TransformedDistribution" title="torch.distributions.transformed_distribution.TransformedDistribution"><code class="xref py py-class docutils literal notranslate"><span class="pre">TransformedDistribution</span></code></a></p>
 <p>Creates an inverse gamma distribution parameterized by <a class="reference internal" href="#torch.distributions.inverse_gamma.InverseGamma.concentration" title="torch.distributions.inverse_gamma.InverseGamma.concentration"><code class="xref py py-attr docutils literal notranslate"><span class="pre">concentration</span></code></a> and <a class="reference internal" href="#torch.distributions.inverse_gamma.InverseGamma.rate" title="torch.distributions.inverse_gamma.InverseGamma.rate"><code class="xref py py-attr docutils literal notranslate"><span class="pre">rate</span></code></a>
 where:</p>
@@ -2413,12 +2413,12 @@ <h2><span class="hidden-section">InverseGamma</span><a class="headerlink" href="
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.inverse_gamma.InverseGamma.entropy">
-<span class="sig-name descname"><span class="pre">entropy</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/inverse_gamma.html#InverseGamma.entropy"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/inverse_gamma.py#L76"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.inverse_gamma.InverseGamma.entropy" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">entropy</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/inverse_gamma.html#InverseGamma.entropy"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/inverse_gamma.py#L76"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.inverse_gamma.InverseGamma.entropy" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.inverse_gamma.InverseGamma.expand">
-<span class="sig-name descname"><span class="pre">expand</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">batch_shape</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_instance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/inverse_gamma.html#InverseGamma.expand"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/inverse_gamma.py#L48"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.inverse_gamma.InverseGamma.expand" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">expand</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">batch_shape</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_instance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/inverse_gamma.html#InverseGamma.expand"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/inverse_gamma.py#L48"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.inverse_gamma.InverseGamma.expand" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py attribute">
@@ -2458,7 +2458,7 @@ <h2><span class="hidden-section">InverseGamma</span><a class="headerlink" href="
 <h2><span class="hidden-section">Kumaraswamy</span><a class="headerlink" href="#kumaraswamy" title="Permalink to this heading">¶</a></h2>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributions.kumaraswamy.Kumaraswamy">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.kumaraswamy.</span></span><span class="sig-name descname"><span class="pre">Kumaraswamy</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">concentration1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">concentration0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">validate_args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/kumaraswamy.html#Kumaraswamy"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/kumaraswamy.py#L23"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.kumaraswamy.Kumaraswamy" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.kumaraswamy.</span></span><span class="sig-name descname"><span class="pre">Kumaraswamy</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">concentration1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">concentration0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">validate_args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/kumaraswamy.html#Kumaraswamy"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/kumaraswamy.py#L23"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.kumaraswamy.Kumaraswamy" title="Permalink to this definition">¶</a></dt>
 <dd><p>Bases: <a class="reference internal" href="#torch.distributions.transformed_distribution.TransformedDistribution" title="torch.distributions.transformed_distribution.TransformedDistribution"><code class="xref py py-class docutils literal notranslate"><span class="pre">TransformedDistribution</span></code></a></p>
 <p>Samples from a Kumaraswamy distribution.</p>
 <p>Example:</p>
@@ -2484,12 +2484,12 @@ <h2><span class="hidden-section">Kumaraswamy</span><a class="headerlink" href="#
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.kumaraswamy.Kumaraswamy.entropy">
-<span class="sig-name descname"><span class="pre">entropy</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/kumaraswamy.html#Kumaraswamy.entropy"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/kumaraswamy.py#L89"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.kumaraswamy.Kumaraswamy.entropy" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">entropy</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/kumaraswamy.html#Kumaraswamy.entropy"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/kumaraswamy.py#L89"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.kumaraswamy.Kumaraswamy.entropy" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.kumaraswamy.Kumaraswamy.expand">
-<span class="sig-name descname"><span class="pre">expand</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">batch_shape</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_instance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/kumaraswamy.html#Kumaraswamy.expand"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/kumaraswamy.py#L63"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.kumaraswamy.Kumaraswamy.expand" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">expand</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">batch_shape</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_instance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/kumaraswamy.html#Kumaraswamy.expand"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/kumaraswamy.py#L63"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.kumaraswamy.Kumaraswamy.expand" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py attribute">
@@ -2524,7 +2524,7 @@ <h2><span class="hidden-section">Kumaraswamy</span><a class="headerlink" href="#
 <h2><span class="hidden-section">LKJCholesky</span><a class="headerlink" href="#lkjcholesky" title="Permalink to this heading">¶</a></h2>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributions.lkj_cholesky.LKJCholesky">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.lkj_cholesky.</span></span><span class="sig-name descname"><span class="pre">LKJCholesky</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dim</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">concentration</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">validate_args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/lkj_cholesky.html#LKJCholesky"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/lkj_cholesky.py#L22"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.lkj_cholesky.LKJCholesky" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.lkj_cholesky.</span></span><span class="sig-name descname"><span class="pre">LKJCholesky</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dim</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">concentration</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">validate_args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/lkj_cholesky.html#LKJCholesky"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/lkj_cholesky.py#L22"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.lkj_cholesky.LKJCholesky" title="Permalink to this definition">¶</a></dt>
 <dd><p>Bases: <a class="reference internal" href="#torch.distributions.distribution.Distribution" title="torch.distributions.distribution.Distribution"><code class="xref py py-class docutils literal notranslate"><span class="pre">Distribution</span></code></a></p>
 <p>LKJ distribution for lower Cholesky factor of correlation matrices.
 The distribution is controlled by <code class="docutils literal notranslate"><span class="pre">concentration</span></code> parameter <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>η</mi></mrow><annotation encoding="application/x-tex">\eta</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.625em;vertical-align:-0.1944em;"></span><span class="mord mathnormal" style="margin-right:0.03588em;">η</span></span></span></span></span>
@@ -2569,17 +2569,17 @@ <h2><span class="hidden-section">LKJCholesky</span><a class="headerlink" href="#
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.lkj_cholesky.LKJCholesky.expand">
-<span class="sig-name descname"><span class="pre">expand</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">batch_shape</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_instance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/lkj_cholesky.html#LKJCholesky.expand"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/lkj_cholesky.py#L85"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.lkj_cholesky.LKJCholesky.expand" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">expand</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">batch_shape</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_instance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/lkj_cholesky.html#LKJCholesky.expand"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/lkj_cholesky.py#L85"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.lkj_cholesky.LKJCholesky.expand" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.lkj_cholesky.LKJCholesky.log_prob">
-<span class="sig-name descname"><span class="pre">log_prob</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/lkj_cholesky.html#LKJCholesky.log_prob"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/lkj_cholesky.py#L118"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.lkj_cholesky.LKJCholesky.log_prob" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">log_prob</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/lkj_cholesky.html#LKJCholesky.log_prob"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/lkj_cholesky.py#L118"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.lkj_cholesky.LKJCholesky.log_prob" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.lkj_cholesky.LKJCholesky.sample">
-<span class="sig-name descname"><span class="pre">sample</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sample_shape</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.Size([])</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/lkj_cholesky.html#LKJCholesky.sample"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/lkj_cholesky.py#L97"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.lkj_cholesky.LKJCholesky.sample" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">sample</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sample_shape</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.Size([])</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/lkj_cholesky.html#LKJCholesky.sample"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/lkj_cholesky.py#L97"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.lkj_cholesky.LKJCholesky.sample" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py attribute">
@@ -2594,7 +2594,7 @@ <h2><span class="hidden-section">LKJCholesky</span><a class="headerlink" href="#
 <h2><span class="hidden-section">Laplace</span><a class="headerlink" href="#laplace" title="Permalink to this heading">¶</a></h2>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributions.laplace.Laplace">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.laplace.</span></span><span class="sig-name descname"><span class="pre">Laplace</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">loc</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">validate_args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/laplace.html#Laplace"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/laplace.py#L13"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.laplace.Laplace" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.laplace.</span></span><span class="sig-name descname"><span class="pre">Laplace</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">loc</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">validate_args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/laplace.html#Laplace"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/laplace.py#L13"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.laplace.Laplace" title="Permalink to this definition">¶</a></dt>
 <dd><p>Bases: <a class="reference internal" href="#torch.distributions.distribution.Distribution" title="torch.distributions.distribution.Distribution"><code class="xref py py-class docutils literal notranslate"><span class="pre">Distribution</span></code></a></p>
 <p>Creates a Laplace distribution parameterized by <code class="xref py py-attr docutils literal notranslate"><span class="pre">loc</span></code> and <code class="xref py py-attr docutils literal notranslate"><span class="pre">scale</span></code>.</p>
 <p>Example:</p>
@@ -2618,17 +2618,17 @@ <h2><span class="hidden-section">Laplace</span><a class="headerlink" href="#lapl
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.laplace.Laplace.cdf">
-<span class="sig-name descname"><span class="pre">cdf</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/laplace.html#Laplace.cdf"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/laplace.py#L84"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.laplace.Laplace.cdf" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">cdf</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/laplace.html#Laplace.cdf"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/laplace.py#L84"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.laplace.Laplace.cdf" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.laplace.Laplace.entropy">
-<span class="sig-name descname"><span class="pre">entropy</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/laplace.html#Laplace.entropy"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/laplace.py#L95"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.laplace.Laplace.entropy" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">entropy</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/laplace.html#Laplace.entropy"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/laplace.py#L95"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.laplace.Laplace.entropy" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.laplace.Laplace.expand">
-<span class="sig-name descname"><span class="pre">expand</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">batch_shape</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_instance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/laplace.html#Laplace.expand"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/laplace.py#L56"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.laplace.Laplace.expand" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">expand</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">batch_shape</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_instance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/laplace.html#Laplace.expand"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/laplace.py#L56"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.laplace.Laplace.expand" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py attribute">
@@ -2638,12 +2638,12 @@ <h2><span class="hidden-section">Laplace</span><a class="headerlink" href="#lapl
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.laplace.Laplace.icdf">
-<span class="sig-name descname"><span class="pre">icdf</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/laplace.html#Laplace.icdf"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/laplace.py#L91"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.laplace.Laplace.icdf" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">icdf</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/laplace.html#Laplace.icdf"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/laplace.py#L91"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.laplace.Laplace.icdf" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.laplace.Laplace.log_prob">
-<span class="sig-name descname"><span class="pre">log_prob</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/laplace.html#Laplace.log_prob"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/laplace.py#L79"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.laplace.Laplace.log_prob" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">log_prob</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/laplace.html#Laplace.log_prob"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/laplace.py#L79"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.laplace.Laplace.log_prob" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py property">
@@ -2658,7 +2658,7 @@ <h2><span class="hidden-section">Laplace</span><a class="headerlink" href="#lapl
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.laplace.Laplace.rsample">
-<span class="sig-name descname"><span class="pre">rsample</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sample_shape</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.Size([])</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/laplace.html#Laplace.rsample"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/laplace.py#L65"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.laplace.Laplace.rsample" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">rsample</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sample_shape</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.Size([])</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/laplace.html#Laplace.rsample"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/laplace.py#L65"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.laplace.Laplace.rsample" title="Permalink to this definition">¶</a></dt>
 <dd><dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
 <dd class="field-odd"><p><a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a></p>
@@ -2688,7 +2688,7 @@ <h2><span class="hidden-section">Laplace</span><a class="headerlink" href="#lapl
 <h2><span class="hidden-section">LogNormal</span><a class="headerlink" href="#lognormal" title="Permalink to this heading">¶</a></h2>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributions.log_normal.LogNormal">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.log_normal.</span></span><span class="sig-name descname"><span class="pre">LogNormal</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">loc</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">validate_args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/log_normal.html#LogNormal"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/log_normal.py#L12"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.log_normal.LogNormal" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.log_normal.</span></span><span class="sig-name descname"><span class="pre">LogNormal</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">loc</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">validate_args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/log_normal.html#LogNormal"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/log_normal.py#L12"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.log_normal.LogNormal" title="Permalink to this definition">¶</a></dt>
 <dd><p>Bases: <a class="reference internal" href="#torch.distributions.transformed_distribution.TransformedDistribution" title="torch.distributions.transformed_distribution.TransformedDistribution"><code class="xref py py-class docutils literal notranslate"><span class="pre">TransformedDistribution</span></code></a></p>
 <p>Creates a log-normal distribution parameterized by
 <a class="reference internal" href="#torch.distributions.log_normal.LogNormal.loc" title="torch.distributions.log_normal.LogNormal.loc"><code class="xref py py-attr docutils literal notranslate"><span class="pre">loc</span></code></a> and <a class="reference internal" href="#torch.distributions.log_normal.LogNormal.scale" title="torch.distributions.log_normal.LogNormal.scale"><code class="xref py py-attr docutils literal notranslate"><span class="pre">scale</span></code></a> where:</p>
@@ -2717,12 +2717,12 @@ <h2><span class="hidden-section">LogNormal</span><a class="headerlink" href="#lo
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.log_normal.LogNormal.entropy">
-<span class="sig-name descname"><span class="pre">entropy</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/log_normal.html#LogNormal.entropy"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/log_normal.py#L64"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.log_normal.LogNormal.entropy" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">entropy</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/log_normal.html#LogNormal.entropy"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/log_normal.py#L64"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.log_normal.LogNormal.entropy" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.log_normal.LogNormal.expand">
-<span class="sig-name descname"><span class="pre">expand</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">batch_shape</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_instance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/log_normal.html#LogNormal.expand"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/log_normal.py#L39"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.log_normal.LogNormal.expand" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">expand</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">batch_shape</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_instance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/log_normal.html#LogNormal.expand"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/log_normal.py#L39"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.log_normal.LogNormal.expand" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py attribute">
@@ -2767,7 +2767,7 @@ <h2><span class="hidden-section">LogNormal</span><a class="headerlink" href="#lo
 <h2><span class="hidden-section">LowRankMultivariateNormal</span><a class="headerlink" href="#lowrankmultivariatenormal" title="Permalink to this heading">¶</a></h2>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributions.lowrank_multivariate_normal.LowRankMultivariateNormal">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.lowrank_multivariate_normal.</span></span><span class="sig-name descname"><span class="pre">LowRankMultivariateNormal</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">loc</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cov_factor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cov_diag</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">validate_args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/lowrank_multivariate_normal.html#LowRankMultivariateNormal"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/lowrank_multivariate_normal.py#L54"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.lowrank_multivariate_normal.LowRankMultivariateNormal" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.lowrank_multivariate_normal.</span></span><span class="sig-name descname"><span class="pre">LowRankMultivariateNormal</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">loc</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cov_factor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cov_diag</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">validate_args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/lowrank_multivariate_normal.html#LowRankMultivariateNormal"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/lowrank_multivariate_normal.py#L54"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.lowrank_multivariate_normal.LowRankMultivariateNormal" title="Permalink to this definition">¶</a></dt>
 <dd><p>Bases: <a class="reference internal" href="#torch.distributions.distribution.Distribution" title="torch.distributions.distribution.Distribution"><code class="xref py py-class docutils literal notranslate"><span class="pre">Distribution</span></code></a></p>
 <p>Creates a multivariate normal distribution with covariance matrix having a low-rank form
 parameterized by <code class="xref py py-attr docutils literal notranslate"><span class="pre">cov_factor</span></code> and <code class="xref py py-attr docutils literal notranslate"><span class="pre">cov_diag</span></code>:</p>
@@ -2814,12 +2814,12 @@ <h2><span class="hidden-section">LowRankMultivariateNormal</span><a class="heade
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.lowrank_multivariate_normal.LowRankMultivariateNormal.entropy">
-<span class="sig-name descname"><span class="pre">entropy</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/lowrank_multivariate_normal.html#LowRankMultivariateNormal.entropy"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/lowrank_multivariate_normal.py#L231"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.lowrank_multivariate_normal.LowRankMultivariateNormal.entropy" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">entropy</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/lowrank_multivariate_normal.html#LowRankMultivariateNormal.entropy"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/lowrank_multivariate_normal.py#L231"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.lowrank_multivariate_normal.LowRankMultivariateNormal.entropy" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.lowrank_multivariate_normal.LowRankMultivariateNormal.expand">
-<span class="sig-name descname"><span class="pre">expand</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">batch_shape</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_instance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/lowrank_multivariate_normal.html#LowRankMultivariateNormal.expand"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/lowrank_multivariate_normal.py#L130"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.lowrank_multivariate_normal.LowRankMultivariateNormal.expand" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">expand</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">batch_shape</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_instance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/lowrank_multivariate_normal.html#LowRankMultivariateNormal.expand"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/lowrank_multivariate_normal.py#L130"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.lowrank_multivariate_normal.LowRankMultivariateNormal.expand" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py attribute">
@@ -2829,7 +2829,7 @@ <h2><span class="hidden-section">LowRankMultivariateNormal</span><a class="heade
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.lowrank_multivariate_normal.LowRankMultivariateNormal.log_prob">
-<span class="sig-name descname"><span class="pre">log_prob</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/lowrank_multivariate_normal.html#LowRankMultivariateNormal.log_prob"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/lowrank_multivariate_normal.py#L214"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.lowrank_multivariate_normal.LowRankMultivariateNormal.log_prob" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">log_prob</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/lowrank_multivariate_normal.html#LowRankMultivariateNormal.log_prob"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/lowrank_multivariate_normal.py#L214"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.lowrank_multivariate_normal.LowRankMultivariateNormal.log_prob" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py property">
@@ -2849,7 +2849,7 @@ <h2><span class="hidden-section">LowRankMultivariateNormal</span><a class="heade
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.lowrank_multivariate_normal.LowRankMultivariateNormal.rsample">
-<span class="sig-name descname"><span class="pre">rsample</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sample_shape</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.Size([])</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/lowrank_multivariate_normal.html#LowRankMultivariateNormal.rsample"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/lowrank_multivariate_normal.py#L203"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.lowrank_multivariate_normal.LowRankMultivariateNormal.rsample" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">rsample</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sample_shape</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.Size([])</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/lowrank_multivariate_normal.html#LowRankMultivariateNormal.rsample"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/lowrank_multivariate_normal.py#L203"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.lowrank_multivariate_normal.LowRankMultivariateNormal.rsample" title="Permalink to this definition">¶</a></dt>
 <dd><dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
 <dd class="field-odd"><p><a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a></p>
@@ -2879,7 +2879,7 @@ <h2><span class="hidden-section">LowRankMultivariateNormal</span><a class="heade
 <h2><span class="hidden-section">MixtureSameFamily</span><a class="headerlink" href="#mixturesamefamily" title="Permalink to this heading">¶</a></h2>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributions.mixture_same_family.MixtureSameFamily">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.mixture_same_family.</span></span><span class="sig-name descname"><span class="pre">MixtureSameFamily</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mixture_distribution</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">component_distribution</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">validate_args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/mixture_same_family.html#MixtureSameFamily"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/mixture_same_family.py#L12"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.mixture_same_family.MixtureSameFamily" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.mixture_same_family.</span></span><span class="sig-name descname"><span class="pre">MixtureSameFamily</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mixture_distribution</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">component_distribution</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">validate_args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/mixture_same_family.html#MixtureSameFamily"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/mixture_same_family.py#L12"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.mixture_same_family.MixtureSameFamily" title="Permalink to this definition">¶</a></dt>
 <dd><p>Bases: <a class="reference internal" href="#torch.distributions.distribution.Distribution" title="torch.distributions.distribution.Distribution"><code class="xref py py-class docutils literal notranslate"><span class="pre">Distribution</span></code></a></p>
 <p>The <cite>MixtureSameFamily</cite> distribution implements a (batch of) mixture
 distribution where all component are from different parameterizations of
@@ -2930,7 +2930,7 @@ <h2><span class="hidden-section">MixtureSameFamily</span><a class="headerlink" h
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.mixture_same_family.MixtureSameFamily.cdf">
-<span class="sig-name descname"><span class="pre">cdf</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/mixture_same_family.html#MixtureSameFamily.cdf"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/mixture_same_family.py#L157"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.mixture_same_family.MixtureSameFamily.cdf" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">cdf</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/mixture_same_family.html#MixtureSameFamily.cdf"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/mixture_same_family.py#L157"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.mixture_same_family.MixtureSameFamily.cdf" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py property">
@@ -2940,7 +2940,7 @@ <h2><span class="hidden-section">MixtureSameFamily</span><a class="headerlink" h
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.mixture_same_family.MixtureSameFamily.expand">
-<span class="sig-name descname"><span class="pre">expand</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">batch_shape</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_instance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/mixture_same_family.html#MixtureSameFamily.expand"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/mixture_same_family.py#L106"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.mixture_same_family.MixtureSameFamily.expand" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">expand</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">batch_shape</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_instance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/mixture_same_family.html#MixtureSameFamily.expand"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/mixture_same_family.py#L106"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.mixture_same_family.MixtureSameFamily.expand" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py attribute">
@@ -2950,7 +2950,7 @@ <h2><span class="hidden-section">MixtureSameFamily</span><a class="headerlink" h
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.mixture_same_family.MixtureSameFamily.log_prob">
-<span class="sig-name descname"><span class="pre">log_prob</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/mixture_same_family.html#MixtureSameFamily.log_prob"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/mixture_same_family.py#L164"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.mixture_same_family.MixtureSameFamily.log_prob" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">log_prob</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/mixture_same_family.html#MixtureSameFamily.log_prob"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/mixture_same_family.py#L164"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.mixture_same_family.MixtureSameFamily.log_prob" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py property">
@@ -2965,7 +2965,7 @@ <h2><span class="hidden-section">MixtureSameFamily</span><a class="headerlink" h
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.mixture_same_family.MixtureSameFamily.sample">
-<span class="sig-name descname"><span class="pre">sample</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sample_shape</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.Size([])</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/mixture_same_family.html#MixtureSameFamily.sample"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/mixture_same_family.py#L174"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.mixture_same_family.MixtureSameFamily.sample" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">sample</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sample_shape</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.Size([])</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/mixture_same_family.html#MixtureSameFamily.sample"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/mixture_same_family.py#L174"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.mixture_same_family.MixtureSameFamily.sample" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py property">
@@ -2990,7 +2990,7 @@ <h2><span class="hidden-section">MixtureSameFamily</span><a class="headerlink" h
 <h2><span class="hidden-section">Multinomial</span><a class="headerlink" href="#multinomial" title="Permalink to this heading">¶</a></h2>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributions.multinomial.Multinomial">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.multinomial.</span></span><span class="sig-name descname"><span class="pre">Multinomial</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">total_count</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">probs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">logits</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">validate_args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/multinomial.html#Multinomial"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/multinomial.py#L13"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.multinomial.Multinomial" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.multinomial.</span></span><span class="sig-name descname"><span class="pre">Multinomial</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">total_count</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">probs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">logits</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">validate_args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/multinomial.html#Multinomial"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/multinomial.py#L13"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.multinomial.Multinomial" title="Permalink to this definition">¶</a></dt>
 <dd><p>Bases: <a class="reference internal" href="#torch.distributions.distribution.Distribution" title="torch.distributions.distribution.Distribution"><code class="xref py py-class docutils literal notranslate"><span class="pre">Distribution</span></code></a></p>
 <p>Creates a Multinomial distribution parameterized by <a class="reference internal" href="#torch.distributions.multinomial.Multinomial.total_count" title="torch.distributions.multinomial.Multinomial.total_count"><code class="xref py py-attr docutils literal notranslate"><span class="pre">total_count</span></code></a> and
 either <a class="reference internal" href="#torch.distributions.multinomial.Multinomial.probs" title="torch.distributions.multinomial.Multinomial.probs"><code class="xref py py-attr docutils literal notranslate"><span class="pre">probs</span></code></a> or <a class="reference internal" href="#torch.distributions.multinomial.Multinomial.logits" title="torch.distributions.multinomial.Multinomial.logits"><code class="xref py py-attr docutils literal notranslate"><span class="pre">logits</span></code></a> (but not both). The innermost dimension of
@@ -3038,17 +3038,17 @@ <h2><span class="hidden-section">Multinomial</span><a class="headerlink" href="#
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.multinomial.Multinomial.entropy">
-<span class="sig-name descname"><span class="pre">entropy</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/multinomial.html#Multinomial.entropy"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/multinomial.py#L115"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.multinomial.Multinomial.entropy" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">entropy</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/multinomial.html#Multinomial.entropy"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/multinomial.py#L115"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.multinomial.Multinomial.entropy" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.multinomial.Multinomial.expand">
-<span class="sig-name descname"><span class="pre">expand</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">batch_shape</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_instance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/multinomial.html#Multinomial.expand"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/multinomial.py#L71"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.multinomial.Multinomial.expand" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">expand</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">batch_shape</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_instance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/multinomial.html#Multinomial.expand"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/multinomial.py#L71"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.multinomial.Multinomial.expand" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.multinomial.Multinomial.log_prob">
-<span class="sig-name descname"><span class="pre">log_prob</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/multinomial.html#Multinomial.log_prob"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/multinomial.py#L128"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.multinomial.Multinomial.log_prob" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">log_prob</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/multinomial.html#Multinomial.log_prob"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/multinomial.py#L128"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.multinomial.Multinomial.log_prob" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py property">
@@ -3073,7 +3073,7 @@ <h2><span class="hidden-section">Multinomial</span><a class="headerlink" href="#
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.multinomial.Multinomial.sample">
-<span class="sig-name descname"><span class="pre">sample</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sample_shape</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.Size([])</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/multinomial.html#Multinomial.sample"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/multinomial.py#L101"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.multinomial.Multinomial.sample" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">sample</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sample_shape</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.Size([])</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/multinomial.html#Multinomial.sample"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/multinomial.py#L101"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.multinomial.Multinomial.sample" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py property">
@@ -3103,7 +3103,7 @@ <h2><span class="hidden-section">Multinomial</span><a class="headerlink" href="#
 <h2><span class="hidden-section">MultivariateNormal</span><a class="headerlink" href="#multivariatenormal" title="Permalink to this heading">¶</a></h2>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributions.multivariate_normal.MultivariateNormal">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.multivariate_normal.</span></span><span class="sig-name descname"><span class="pre">MultivariateNormal</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">loc</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">covariance_matrix</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">precision_matrix</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale_tril</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">validate_args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/multivariate_normal.html#MultivariateNormal"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/multivariate_normal.py#L88"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.multivariate_normal.MultivariateNormal" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.multivariate_normal.</span></span><span class="sig-name descname"><span class="pre">MultivariateNormal</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">loc</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">covariance_matrix</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">precision_matrix</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale_tril</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">validate_args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/multivariate_normal.html#MultivariateNormal"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/multivariate_normal.py#L88"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.multivariate_normal.MultivariateNormal" title="Permalink to this definition">¶</a></dt>
 <dd><p>Bases: <a class="reference internal" href="#torch.distributions.distribution.Distribution" title="torch.distributions.distribution.Distribution"><code class="xref py py-class docutils literal notranslate"><span class="pre">Distribution</span></code></a></p>
 <p>Creates a multivariate normal (also called Gaussian) distribution
 parameterized by a mean vector and a covariance matrix.</p>
@@ -3151,12 +3151,12 @@ <h2><span class="hidden-section">MultivariateNormal</span><a class="headerlink"
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.multivariate_normal.MultivariateNormal.entropy">
-<span class="sig-name descname"><span class="pre">entropy</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/multivariate_normal.html#MultivariateNormal.entropy"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/multivariate_normal.py#L258"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.multivariate_normal.MultivariateNormal.entropy" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">entropy</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/multivariate_normal.html#MultivariateNormal.entropy"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/multivariate_normal.py#L258"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.multivariate_normal.MultivariateNormal.entropy" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.multivariate_normal.MultivariateNormal.expand">
-<span class="sig-name descname"><span class="pre">expand</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">batch_shape</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_instance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/multivariate_normal.html#MultivariateNormal.expand"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/multivariate_normal.py#L190"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.multivariate_normal.MultivariateNormal.expand" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">expand</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">batch_shape</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_instance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/multivariate_normal.html#MultivariateNormal.expand"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/multivariate_normal.py#L190"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.multivariate_normal.MultivariateNormal.expand" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py attribute">
@@ -3166,7 +3166,7 @@ <h2><span class="hidden-section">MultivariateNormal</span><a class="headerlink"
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.multivariate_normal.MultivariateNormal.log_prob">
-<span class="sig-name descname"><span class="pre">log_prob</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/multivariate_normal.html#MultivariateNormal.log_prob"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/multivariate_normal.py#L248"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.multivariate_normal.MultivariateNormal.log_prob" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">log_prob</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/multivariate_normal.html#MultivariateNormal.log_prob"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/multivariate_normal.py#L248"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.multivariate_normal.MultivariateNormal.log_prob" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py property">
@@ -3186,7 +3186,7 @@ <h2><span class="hidden-section">MultivariateNormal</span><a class="headerlink"
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.multivariate_normal.MultivariateNormal.rsample">
-<span class="sig-name descname"><span class="pre">rsample</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sample_shape</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.Size([])</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/multivariate_normal.html#MultivariateNormal.rsample"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/multivariate_normal.py#L243"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.multivariate_normal.MultivariateNormal.rsample" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">rsample</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sample_shape</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.Size([])</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/multivariate_normal.html#MultivariateNormal.rsample"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/multivariate_normal.py#L243"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.multivariate_normal.MultivariateNormal.rsample" title="Permalink to this definition">¶</a></dt>
 <dd><dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
 <dd class="field-odd"><p><a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a></p>
@@ -3216,7 +3216,7 @@ <h2><span class="hidden-section">MultivariateNormal</span><a class="headerlink"
 <h2><span class="hidden-section">NegativeBinomial</span><a class="headerlink" href="#negativebinomial" title="Permalink to this heading">¶</a></h2>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributions.negative_binomial.NegativeBinomial">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.negative_binomial.</span></span><span class="sig-name descname"><span class="pre">NegativeBinomial</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">total_count</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">probs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">logits</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">validate_args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/negative_binomial.html#NegativeBinomial"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/negative_binomial.py#L19"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.negative_binomial.NegativeBinomial" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.negative_binomial.</span></span><span class="sig-name descname"><span class="pre">NegativeBinomial</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">total_count</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">probs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">logits</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">validate_args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/negative_binomial.html#NegativeBinomial"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/negative_binomial.py#L19"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.negative_binomial.NegativeBinomial" title="Permalink to this definition">¶</a></dt>
 <dd><p>Bases: <a class="reference internal" href="#torch.distributions.distribution.Distribution" title="torch.distributions.distribution.Distribution"><code class="xref py py-class docutils literal notranslate"><span class="pre">Distribution</span></code></a></p>
 <p>Creates a Negative Binomial distribution, i.e. distribution
 of the number of successful independent and identical Bernoulli trials
@@ -3240,12 +3240,12 @@ <h2><span class="hidden-section">NegativeBinomial</span><a class="headerlink" hr
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.negative_binomial.NegativeBinomial.expand">
-<span class="sig-name descname"><span class="pre">expand</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">batch_shape</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_instance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/negative_binomial.html#NegativeBinomial.expand"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/negative_binomial.py#L62"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.negative_binomial.NegativeBinomial.expand" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">expand</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">batch_shape</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_instance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/negative_binomial.html#NegativeBinomial.expand"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/negative_binomial.py#L62"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.negative_binomial.NegativeBinomial.expand" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.negative_binomial.NegativeBinomial.log_prob">
-<span class="sig-name descname"><span class="pre">log_prob</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/negative_binomial.html#NegativeBinomial.log_prob"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/negative_binomial.py#L117"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.negative_binomial.NegativeBinomial.log_prob" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">log_prob</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/negative_binomial.html#NegativeBinomial.log_prob"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/negative_binomial.py#L117"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.negative_binomial.NegativeBinomial.log_prob" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py property">
@@ -3275,7 +3275,7 @@ <h2><span class="hidden-section">NegativeBinomial</span><a class="headerlink" hr
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.negative_binomial.NegativeBinomial.sample">
-<span class="sig-name descname"><span class="pre">sample</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sample_shape</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.Size([])</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/negative_binomial.html#NegativeBinomial.sample"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/negative_binomial.py#L112"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.negative_binomial.NegativeBinomial.sample" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">sample</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sample_shape</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.Size([])</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/negative_binomial.html#NegativeBinomial.sample"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/negative_binomial.py#L112"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.negative_binomial.NegativeBinomial.sample" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py attribute">
@@ -3295,7 +3295,7 @@ <h2><span class="hidden-section">NegativeBinomial</span><a class="headerlink" hr
 <h2><span class="hidden-section">Normal</span><a class="headerlink" href="#normal" title="Permalink to this heading">¶</a></h2>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributions.normal.Normal">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.normal.</span></span><span class="sig-name descname"><span class="pre">Normal</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">loc</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">validate_args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/normal.html#Normal"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/normal.py#L15"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.normal.Normal" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.normal.</span></span><span class="sig-name descname"><span class="pre">Normal</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">loc</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">validate_args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/normal.html#Normal"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/normal.py#L15"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.normal.Normal" title="Permalink to this definition">¶</a></dt>
 <dd><p>Bases: <a class="reference internal" href="#torch.distributions.exp_family.ExponentialFamily" title="torch.distributions.exp_family.ExponentialFamily"><code class="xref py py-class docutils literal notranslate"><span class="pre">ExponentialFamily</span></code></a></p>
 <p>Creates a normal (also called Gaussian) distribution parameterized by
 <code class="xref py py-attr docutils literal notranslate"><span class="pre">loc</span></code> and <code class="xref py py-attr docutils literal notranslate"><span class="pre">scale</span></code>.</p>
@@ -3321,17 +3321,17 @@ <h2><span class="hidden-section">Normal</span><a class="headerlink" href="#norma
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.normal.Normal.cdf">
-<span class="sig-name descname"><span class="pre">cdf</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/normal.html#Normal.cdf"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/normal.py#L96"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.normal.Normal.cdf" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">cdf</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/normal.html#Normal.cdf"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/normal.py#L96"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.normal.Normal.cdf" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.normal.Normal.entropy">
-<span class="sig-name descname"><span class="pre">entropy</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/normal.html#Normal.entropy"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/normal.py#L106"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.normal.Normal.entropy" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">entropy</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/normal.html#Normal.entropy"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/normal.py#L106"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.normal.Normal.entropy" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.normal.Normal.expand">
-<span class="sig-name descname"><span class="pre">expand</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">batch_shape</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_instance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/normal.html#Normal.expand"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/normal.py#L61"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.normal.Normal.expand" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">expand</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">batch_shape</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_instance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/normal.html#Normal.expand"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/normal.py#L61"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.normal.Normal.expand" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py attribute">
@@ -3341,12 +3341,12 @@ <h2><span class="hidden-section">Normal</span><a class="headerlink" href="#norma
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.normal.Normal.icdf">
-<span class="sig-name descname"><span class="pre">icdf</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/normal.html#Normal.icdf"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/normal.py#L103"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.normal.Normal.icdf" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">icdf</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/normal.html#Normal.icdf"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/normal.py#L103"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.normal.Normal.icdf" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.normal.Normal.log_prob">
-<span class="sig-name descname"><span class="pre">log_prob</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/normal.html#Normal.log_prob"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/normal.py#L80"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.normal.Normal.log_prob" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">log_prob</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/normal.html#Normal.log_prob"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/normal.py#L80"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.normal.Normal.log_prob" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py property">
@@ -3361,7 +3361,7 @@ <h2><span class="hidden-section">Normal</span><a class="headerlink" href="#norma
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.normal.Normal.rsample">
-<span class="sig-name descname"><span class="pre">rsample</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sample_shape</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.Size([])</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/normal.html#Normal.rsample"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/normal.py#L75"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.normal.Normal.rsample" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">rsample</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sample_shape</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.Size([])</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/normal.html#Normal.rsample"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/normal.py#L75"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.normal.Normal.rsample" title="Permalink to this definition">¶</a></dt>
 <dd><dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
 <dd class="field-odd"><p><a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a></p>
@@ -3371,7 +3371,7 @@ <h2><span class="hidden-section">Normal</span><a class="headerlink" href="#norma
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.normal.Normal.sample">
-<span class="sig-name descname"><span class="pre">sample</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sample_shape</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.Size([])</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/normal.html#Normal.sample"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/normal.py#L70"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.normal.Normal.sample" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">sample</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sample_shape</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.Size([])</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/normal.html#Normal.sample"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/normal.py#L70"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.normal.Normal.sample" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py property">
@@ -3396,7 +3396,7 @@ <h2><span class="hidden-section">Normal</span><a class="headerlink" href="#norma
 <h2><span class="hidden-section">OneHotCategorical</span><a class="headerlink" href="#onehotcategorical" title="Permalink to this heading">¶</a></h2>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributions.one_hot_categorical.OneHotCategorical">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.one_hot_categorical.</span></span><span class="sig-name descname"><span class="pre">OneHotCategorical</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">probs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">logits</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">validate_args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/one_hot_categorical.html#OneHotCategorical"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/one_hot_categorical.py#L13"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.one_hot_categorical.OneHotCategorical" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.one_hot_categorical.</span></span><span class="sig-name descname"><span class="pre">OneHotCategorical</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">probs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">logits</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">validate_args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/one_hot_categorical.html#OneHotCategorical"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/one_hot_categorical.py#L13"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.one_hot_categorical.OneHotCategorical" title="Permalink to this definition">¶</a></dt>
 <dd><p>Bases: <a class="reference internal" href="#torch.distributions.distribution.Distribution" title="torch.distributions.distribution.Distribution"><code class="xref py py-class docutils literal notranslate"><span class="pre">Distribution</span></code></a></p>
 <p>Creates a one-hot categorical distribution parameterized by <a class="reference internal" href="#torch.distributions.one_hot_categorical.OneHotCategorical.probs" title="torch.distributions.one_hot_categorical.OneHotCategorical.probs"><code class="xref py py-attr docutils literal notranslate"><span class="pre">probs</span></code></a> or
 <a class="reference internal" href="#torch.distributions.one_hot_categorical.OneHotCategorical.logits" title="torch.distributions.one_hot_categorical.OneHotCategorical.logits"><code class="xref py py-attr docutils literal notranslate"><span class="pre">logits</span></code></a>.</p>
@@ -3434,17 +3434,17 @@ <h2><span class="hidden-section">OneHotCategorical</span><a class="headerlink" h
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.one_hot_categorical.OneHotCategorical.entropy">
-<span class="sig-name descname"><span class="pre">entropy</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/one_hot_categorical.html#OneHotCategorical.entropy"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/one_hot_categorical.py#L108"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.one_hot_categorical.OneHotCategorical.entropy" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">entropy</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/one_hot_categorical.html#OneHotCategorical.entropy"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/one_hot_categorical.py#L108"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.one_hot_categorical.OneHotCategorical.entropy" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.one_hot_categorical.OneHotCategorical.enumerate_support">
-<span class="sig-name descname"><span class="pre">enumerate_support</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">expand</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/one_hot_categorical.html#OneHotCategorical.enumerate_support"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/one_hot_categorical.py#L111"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.one_hot_categorical.OneHotCategorical.enumerate_support" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">enumerate_support</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">expand</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/one_hot_categorical.html#OneHotCategorical.enumerate_support"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/one_hot_categorical.py#L111"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.one_hot_categorical.OneHotCategorical.enumerate_support" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.one_hot_categorical.OneHotCategorical.expand">
-<span class="sig-name descname"><span class="pre">expand</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">batch_shape</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_instance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/one_hot_categorical.html#OneHotCategorical.expand"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/one_hot_categorical.py#L52"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.one_hot_categorical.OneHotCategorical.expand" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">expand</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">batch_shape</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_instance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/one_hot_categorical.html#OneHotCategorical.expand"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/one_hot_categorical.py#L52"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.one_hot_categorical.OneHotCategorical.expand" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py attribute">
@@ -3454,7 +3454,7 @@ <h2><span class="hidden-section">OneHotCategorical</span><a class="headerlink" h
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.one_hot_categorical.OneHotCategorical.log_prob">
-<span class="sig-name descname"><span class="pre">log_prob</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/one_hot_categorical.html#OneHotCategorical.log_prob"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/one_hot_categorical.py#L102"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.one_hot_categorical.OneHotCategorical.log_prob" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">log_prob</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/one_hot_categorical.html#OneHotCategorical.log_prob"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/one_hot_categorical.py#L102"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.one_hot_categorical.OneHotCategorical.log_prob" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py property">
@@ -3484,7 +3484,7 @@ <h2><span class="hidden-section">OneHotCategorical</span><a class="headerlink" h
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.one_hot_categorical.OneHotCategorical.sample">
-<span class="sig-name descname"><span class="pre">sample</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sample_shape</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.Size([])</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/one_hot_categorical.html#OneHotCategorical.sample"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/one_hot_categorical.py#L95"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.one_hot_categorical.OneHotCategorical.sample" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">sample</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sample_shape</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.Size([])</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/one_hot_categorical.html#OneHotCategorical.sample"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/one_hot_categorical.py#L95"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.one_hot_categorical.OneHotCategorical.sample" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py attribute">
@@ -3504,7 +3504,7 @@ <h2><span class="hidden-section">OneHotCategorical</span><a class="headerlink" h
 <h2><span class="hidden-section">Pareto</span><a class="headerlink" href="#pareto" title="Permalink to this heading">¶</a></h2>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributions.pareto.Pareto">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.pareto.</span></span><span class="sig-name descname"><span class="pre">Pareto</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">scale</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alpha</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">validate_args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/pareto.html#Pareto"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/pareto.py#L15"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.pareto.Pareto" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.pareto.</span></span><span class="sig-name descname"><span class="pre">Pareto</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">scale</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alpha</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">validate_args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/pareto.html#Pareto"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/pareto.py#L15"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.pareto.Pareto" title="Permalink to this definition">¶</a></dt>
 <dd><p>Bases: <a class="reference internal" href="#torch.distributions.transformed_distribution.TransformedDistribution" title="torch.distributions.transformed_distribution.TransformedDistribution"><code class="xref py py-class docutils literal notranslate"><span class="pre">TransformedDistribution</span></code></a></p>
 <p>Samples from a Pareto Type 1 distribution.</p>
 <p>Example:</p>
@@ -3528,7 +3528,7 @@ <h2><span class="hidden-section">Pareto</span><a class="headerlink" href="#paret
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.pareto.Pareto.entropy">
-<span class="sig-name descname"><span class="pre">entropy</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/pareto.html#Pareto.entropy"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/pareto.py#L68"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.pareto.Pareto.entropy" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">entropy</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/pareto.html#Pareto.entropy"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/pareto.py#L68"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.pareto.Pareto.entropy" title="Permalink to this definition">¶</a></dt>
 <dd><dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
 <dd class="field-odd"><p><a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a></p>
@@ -3538,7 +3538,7 @@ <h2><span class="hidden-section">Pareto</span><a class="headerlink" href="#paret
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.pareto.Pareto.expand">
-<span class="sig-name descname"><span class="pre">expand</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">batch_shape</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_instance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/pareto.html#Pareto.expand"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/pareto.py#L40"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.pareto.Pareto.expand" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">expand</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">batch_shape</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_instance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/pareto.html#Pareto.expand"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/pareto.py#L40"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.pareto.Pareto.expand" title="Permalink to this definition">¶</a></dt>
 <dd><dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
 <dd class="field-odd"><p><a class="reference internal" href="#torch.distributions.pareto.Pareto" title="torch.distributions.pareto.Pareto"><em>Pareto</em></a></p>
@@ -3578,7 +3578,7 @@ <h2><span class="hidden-section">Pareto</span><a class="headerlink" href="#paret
 <h2><span class="hidden-section">Poisson</span><a class="headerlink" href="#poisson" title="Permalink to this heading">¶</a></h2>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributions.poisson.Poisson">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.poisson.</span></span><span class="sig-name descname"><span class="pre">Poisson</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">rate</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">validate_args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/poisson.html#Poisson"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/poisson.py#L13"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.poisson.Poisson" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.poisson.</span></span><span class="sig-name descname"><span class="pre">Poisson</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">rate</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">validate_args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/poisson.html#Poisson"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/poisson.py#L13"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.poisson.Poisson" title="Permalink to this definition">¶</a></dt>
 <dd><p>Bases: <a class="reference internal" href="#torch.distributions.exp_family.ExponentialFamily" title="torch.distributions.exp_family.ExponentialFamily"><code class="xref py py-class docutils literal notranslate"><span class="pre">ExponentialFamily</span></code></a></p>
 <p>Creates a Poisson distribution parameterized by <code class="xref py py-attr docutils literal notranslate"><span class="pre">rate</span></code>, the rate parameter.</p>
 <p>Samples are nonnegative integers, with a pmf given by</p>
@@ -3603,12 +3603,12 @@ <h2><span class="hidden-section">Poisson</span><a class="headerlink" href="#pois
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.poisson.Poisson.expand">
-<span class="sig-name descname"><span class="pre">expand</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">batch_shape</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_instance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/poisson.html#Poisson.expand"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/poisson.py#L55"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.poisson.Poisson.expand" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">expand</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">batch_shape</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_instance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/poisson.html#Poisson.expand"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/poisson.py#L55"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.poisson.Poisson.expand" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.poisson.Poisson.log_prob">
-<span class="sig-name descname"><span class="pre">log_prob</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/poisson.html#Poisson.log_prob"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/poisson.py#L68"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.poisson.Poisson.log_prob" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">log_prob</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/poisson.html#Poisson.log_prob"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/poisson.py#L68"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.poisson.Poisson.log_prob" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py property">
@@ -3623,7 +3623,7 @@ <h2><span class="hidden-section">Poisson</span><a class="headerlink" href="#pois
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.poisson.Poisson.sample">
-<span class="sig-name descname"><span class="pre">sample</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sample_shape</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.Size([])</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/poisson.html#Poisson.sample"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/poisson.py#L63"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.poisson.Poisson.sample" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">sample</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sample_shape</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.Size([])</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/poisson.html#Poisson.sample"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/poisson.py#L63"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.poisson.Poisson.sample" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py attribute">
@@ -3643,7 +3643,7 @@ <h2><span class="hidden-section">Poisson</span><a class="headerlink" href="#pois
 <h2><span class="hidden-section">RelaxedBernoulli</span><a class="headerlink" href="#relaxedbernoulli" title="Permalink to this heading">¶</a></h2>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributions.relaxed_bernoulli.RelaxedBernoulli">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.relaxed_bernoulli.</span></span><span class="sig-name descname"><span class="pre">RelaxedBernoulli</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">temperature</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">probs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">logits</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">validate_args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/relaxed_bernoulli.html#RelaxedBernoulli"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/relaxed_bernoulli.py#L109"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.relaxed_bernoulli.RelaxedBernoulli" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.relaxed_bernoulli.</span></span><span class="sig-name descname"><span class="pre">RelaxedBernoulli</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">temperature</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">probs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">logits</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">validate_args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/relaxed_bernoulli.html#RelaxedBernoulli"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/relaxed_bernoulli.py#L109"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.relaxed_bernoulli.RelaxedBernoulli" title="Permalink to this definition">¶</a></dt>
 <dd><p>Bases: <a class="reference internal" href="#torch.distributions.transformed_distribution.TransformedDistribution" title="torch.distributions.transformed_distribution.TransformedDistribution"><code class="xref py py-class docutils literal notranslate"><span class="pre">TransformedDistribution</span></code></a></p>
 <p>Creates a RelaxedBernoulli distribution, parametrized by
 <a class="reference internal" href="#torch.distributions.relaxed_bernoulli.RelaxedBernoulli.temperature" title="torch.distributions.relaxed_bernoulli.RelaxedBernoulli.temperature"><code class="xref py py-attr docutils literal notranslate"><span class="pre">temperature</span></code></a>, and either <a class="reference internal" href="#torch.distributions.relaxed_bernoulli.RelaxedBernoulli.probs" title="torch.distributions.relaxed_bernoulli.RelaxedBernoulli.probs"><code class="xref py py-attr docutils literal notranslate"><span class="pre">probs</span></code></a> or <a class="reference internal" href="#torch.distributions.relaxed_bernoulli.RelaxedBernoulli.logits" title="torch.distributions.relaxed_bernoulli.RelaxedBernoulli.logits"><code class="xref py py-attr docutils literal notranslate"><span class="pre">logits</span></code></a>
@@ -3672,7 +3672,7 @@ <h2><span class="hidden-section">RelaxedBernoulli</span><a class="headerlink" hr
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.relaxed_bernoulli.RelaxedBernoulli.expand">
-<span class="sig-name descname"><span class="pre">expand</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">batch_shape</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_instance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/relaxed_bernoulli.html#RelaxedBernoulli.expand"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/relaxed_bernoulli.py#L137"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.relaxed_bernoulli.RelaxedBernoulli.expand" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">expand</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">batch_shape</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_instance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/relaxed_bernoulli.html#RelaxedBernoulli.expand"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/relaxed_bernoulli.py#L137"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.relaxed_bernoulli.RelaxedBernoulli.expand" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py attribute">
@@ -3707,7 +3707,7 @@ <h2><span class="hidden-section">RelaxedBernoulli</span><a class="headerlink" hr
 <h2><span class="hidden-section">LogitRelaxedBernoulli</span><a class="headerlink" href="#logitrelaxedbernoulli" title="Permalink to this heading">¶</a></h2>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributions.relaxed_bernoulli.LogitRelaxedBernoulli">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.relaxed_bernoulli.</span></span><span class="sig-name descname"><span class="pre">LogitRelaxedBernoulli</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">temperature</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">probs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">logits</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">validate_args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/relaxed_bernoulli.html#LogitRelaxedBernoulli"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/relaxed_bernoulli.py#L21"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.relaxed_bernoulli.LogitRelaxedBernoulli" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.relaxed_bernoulli.</span></span><span class="sig-name descname"><span class="pre">LogitRelaxedBernoulli</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">temperature</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">probs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">logits</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">validate_args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/relaxed_bernoulli.html#LogitRelaxedBernoulli"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/relaxed_bernoulli.py#L21"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.relaxed_bernoulli.LogitRelaxedBernoulli" title="Permalink to this definition">¶</a></dt>
 <dd><p>Bases: <a class="reference internal" href="#torch.distributions.distribution.Distribution" title="torch.distributions.distribution.Distribution"><code class="xref py py-class docutils literal notranslate"><span class="pre">Distribution</span></code></a></p>
 <p>Creates a LogitRelaxedBernoulli distribution parameterized by <a class="reference internal" href="#torch.distributions.relaxed_bernoulli.LogitRelaxedBernoulli.probs" title="torch.distributions.relaxed_bernoulli.LogitRelaxedBernoulli.probs"><code class="xref py py-attr docutils literal notranslate"><span class="pre">probs</span></code></a>
 or <a class="reference internal" href="#torch.distributions.relaxed_bernoulli.LogitRelaxedBernoulli.logits" title="torch.distributions.relaxed_bernoulli.LogitRelaxedBernoulli.logits"><code class="xref py py-attr docutils literal notranslate"><span class="pre">logits</span></code></a> (but not both), which is the logit of a RelaxedBernoulli
@@ -3733,12 +3733,12 @@ <h2><span class="hidden-section">LogitRelaxedBernoulli</span><a class="headerlin
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.relaxed_bernoulli.LogitRelaxedBernoulli.expand">
-<span class="sig-name descname"><span class="pre">expand</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">batch_shape</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_instance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/relaxed_bernoulli.html#LogitRelaxedBernoulli.expand"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/relaxed_bernoulli.py#L62"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.relaxed_bernoulli.LogitRelaxedBernoulli.expand" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">expand</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">batch_shape</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_instance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/relaxed_bernoulli.html#LogitRelaxedBernoulli.expand"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/relaxed_bernoulli.py#L62"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.relaxed_bernoulli.LogitRelaxedBernoulli.expand" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.relaxed_bernoulli.LogitRelaxedBernoulli.log_prob">
-<span class="sig-name descname"><span class="pre">log_prob</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/relaxed_bernoulli.html#LogitRelaxedBernoulli.log_prob"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/relaxed_bernoulli.py#L101"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.relaxed_bernoulli.LogitRelaxedBernoulli.log_prob" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">log_prob</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/relaxed_bernoulli.html#LogitRelaxedBernoulli.log_prob"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/relaxed_bernoulli.py#L101"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.relaxed_bernoulli.LogitRelaxedBernoulli.log_prob" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py property">
@@ -3758,7 +3758,7 @@ <h2><span class="hidden-section">LogitRelaxedBernoulli</span><a class="headerlin
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.relaxed_bernoulli.LogitRelaxedBernoulli.rsample">
-<span class="sig-name descname"><span class="pre">rsample</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sample_shape</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.Size([])</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/relaxed_bernoulli.html#LogitRelaxedBernoulli.rsample"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/relaxed_bernoulli.py#L91"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.relaxed_bernoulli.LogitRelaxedBernoulli.rsample" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">rsample</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sample_shape</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.Size([])</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/relaxed_bernoulli.html#LogitRelaxedBernoulli.rsample"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/relaxed_bernoulli.py#L91"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.relaxed_bernoulli.LogitRelaxedBernoulli.rsample" title="Permalink to this definition">¶</a></dt>
 <dd><dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
 <dd class="field-odd"><p><a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a></p>
@@ -3778,7 +3778,7 @@ <h2><span class="hidden-section">LogitRelaxedBernoulli</span><a class="headerlin
 <h2><span class="hidden-section">RelaxedOneHotCategorical</span><a class="headerlink" href="#relaxedonehotcategorical" title="Permalink to this heading">¶</a></h2>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributions.relaxed_categorical.RelaxedOneHotCategorical">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.relaxed_categorical.</span></span><span class="sig-name descname"><span class="pre">RelaxedOneHotCategorical</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">temperature</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">probs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">logits</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">validate_args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/relaxed_categorical.html#RelaxedOneHotCategorical"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/relaxed_categorical.py#L99"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.relaxed_categorical.RelaxedOneHotCategorical" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.relaxed_categorical.</span></span><span class="sig-name descname"><span class="pre">RelaxedOneHotCategorical</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">temperature</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">probs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">logits</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">validate_args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/relaxed_categorical.html#RelaxedOneHotCategorical"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/relaxed_categorical.py#L99"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.relaxed_categorical.RelaxedOneHotCategorical" title="Permalink to this definition">¶</a></dt>
 <dd><p>Bases: <a class="reference internal" href="#torch.distributions.transformed_distribution.TransformedDistribution" title="torch.distributions.transformed_distribution.TransformedDistribution"><code class="xref py py-class docutils literal notranslate"><span class="pre">TransformedDistribution</span></code></a></p>
 <p>Creates a RelaxedOneHotCategorical distribution parametrized by
 <a class="reference internal" href="#torch.distributions.relaxed_categorical.RelaxedOneHotCategorical.temperature" title="torch.distributions.relaxed_categorical.RelaxedOneHotCategorical.temperature"><code class="xref py py-attr docutils literal notranslate"><span class="pre">temperature</span></code></a>, and either <a class="reference internal" href="#torch.distributions.relaxed_categorical.RelaxedOneHotCategorical.probs" title="torch.distributions.relaxed_categorical.RelaxedOneHotCategorical.probs"><code class="xref py py-attr docutils literal notranslate"><span class="pre">probs</span></code></a> or <a class="reference internal" href="#torch.distributions.relaxed_categorical.RelaxedOneHotCategorical.logits" title="torch.distributions.relaxed_categorical.RelaxedOneHotCategorical.logits"><code class="xref py py-attr docutils literal notranslate"><span class="pre">logits</span></code></a>.
@@ -3807,7 +3807,7 @@ <h2><span class="hidden-section">RelaxedOneHotCategorical</span><a class="header
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.relaxed_categorical.RelaxedOneHotCategorical.expand">
-<span class="sig-name descname"><span class="pre">expand</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">batch_shape</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_instance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/relaxed_categorical.html#RelaxedOneHotCategorical.expand"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/relaxed_categorical.py#L129"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.relaxed_categorical.RelaxedOneHotCategorical.expand" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">expand</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">batch_shape</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_instance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/relaxed_categorical.html#RelaxedOneHotCategorical.expand"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/relaxed_categorical.py#L129"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.relaxed_categorical.RelaxedOneHotCategorical.expand" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py attribute">
@@ -3842,7 +3842,7 @@ <h2><span class="hidden-section">RelaxedOneHotCategorical</span><a class="header
 <h2><span class="hidden-section">StudentT</span><a class="headerlink" href="#studentt" title="Permalink to this heading">¶</a></h2>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributions.studentT.StudentT">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.studentT.</span></span><span class="sig-name descname"><span class="pre">StudentT</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">df</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">loc</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">validate_args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/studentT.html#StudentT"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/studentT.py#L15"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.studentT.StudentT" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.studentT.</span></span><span class="sig-name descname"><span class="pre">StudentT</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">df</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">loc</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">validate_args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/studentT.html#StudentT"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/studentT.py#L15"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.studentT.StudentT" title="Permalink to this definition">¶</a></dt>
 <dd><p>Bases: <a class="reference internal" href="#torch.distributions.distribution.Distribution" title="torch.distributions.distribution.Distribution"><code class="xref py py-class docutils literal notranslate"><span class="pre">Distribution</span></code></a></p>
 <p>Creates a Student’s t-distribution parameterized by degree of
 freedom <code class="xref py py-attr docutils literal notranslate"><span class="pre">df</span></code>, mean <code class="xref py py-attr docutils literal notranslate"><span class="pre">loc</span></code> and scale <code class="xref py py-attr docutils literal notranslate"><span class="pre">scale</span></code>.</p>
@@ -3868,12 +3868,12 @@ <h2><span class="hidden-section">StudentT</span><a class="headerlink" href="#stu
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.studentT.StudentT.entropy">
-<span class="sig-name descname"><span class="pre">entropy</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/studentT.html#StudentT.entropy"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/studentT.py#L106"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.studentT.StudentT.entropy" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">entropy</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/studentT.html#StudentT.entropy"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/studentT.py#L106"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.studentT.StudentT.entropy" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.studentT.StudentT.expand">
-<span class="sig-name descname"><span class="pre">expand</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">batch_shape</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_instance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/studentT.html#StudentT.expand"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/studentT.py#L68"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.studentT.StudentT.expand" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">expand</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">batch_shape</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_instance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/studentT.html#StudentT.expand"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/studentT.py#L68"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.studentT.StudentT.expand" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py attribute">
@@ -3883,7 +3883,7 @@ <h2><span class="hidden-section">StudentT</span><a class="headerlink" href="#stu
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.studentT.StudentT.log_prob">
-<span class="sig-name descname"><span class="pre">log_prob</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/studentT.html#StudentT.log_prob"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/studentT.py#L93"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.studentT.StudentT.log_prob" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">log_prob</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/studentT.html#StudentT.log_prob"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/studentT.py#L93"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.studentT.StudentT.log_prob" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py property">
@@ -3898,7 +3898,7 @@ <h2><span class="hidden-section">StudentT</span><a class="headerlink" href="#stu
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.studentT.StudentT.rsample">
-<span class="sig-name descname"><span class="pre">rsample</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sample_shape</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.Size([])</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/studentT.html#StudentT.rsample"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/studentT.py#L79"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.studentT.StudentT.rsample" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">rsample</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sample_shape</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.Size([])</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/studentT.html#StudentT.rsample"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/studentT.py#L79"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.studentT.StudentT.rsample" title="Permalink to this definition">¶</a></dt>
 <dd><dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
 <dd class="field-odd"><p><a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a></p>
@@ -3923,7 +3923,7 @@ <h2><span class="hidden-section">StudentT</span><a class="headerlink" href="#stu
 <h2><span class="hidden-section">TransformedDistribution</span><a class="headerlink" href="#transformeddistribution" title="Permalink to this heading">¶</a></h2>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributions.transformed_distribution.TransformedDistribution">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.transformed_distribution.</span></span><span class="sig-name descname"><span class="pre">TransformedDistribution</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">base_distribution</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transforms</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">validate_args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/transformed_distribution.html#TransformedDistribution"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/transformed_distribution.py#L16"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.transformed_distribution.TransformedDistribution" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.transformed_distribution.</span></span><span class="sig-name descname"><span class="pre">TransformedDistribution</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">base_distribution</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transforms</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">validate_args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/transformed_distribution.html#TransformedDistribution"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/transformed_distribution.py#L16"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.transformed_distribution.TransformedDistribution" title="Permalink to this definition">¶</a></dt>
 <dd><p>Bases: <a class="reference internal" href="#torch.distributions.distribution.Distribution" title="torch.distributions.distribution.Distribution"><code class="xref py py-class docutils literal notranslate"><span class="pre">Distribution</span></code></a></p>
 <p>Extension of the Distribution class, which applies a sequence of Transforms
 to a base distribution.  Let f be the composition of transforms applied:</p>
@@ -3961,14 +3961,14 @@ <h2><span class="hidden-section">TransformedDistribution</span><a class="headerl
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.transformed_distribution.TransformedDistribution.cdf">
-<span class="sig-name descname"><span class="pre">cdf</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/transformed_distribution.html#TransformedDistribution.cdf"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/transformed_distribution.py#L194"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.transformed_distribution.TransformedDistribution.cdf" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">cdf</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/transformed_distribution.html#TransformedDistribution.cdf"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/transformed_distribution.py#L194"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.transformed_distribution.TransformedDistribution.cdf" title="Permalink to this definition">¶</a></dt>
 <dd><p>Computes the cumulative distribution function by inverting the
 transform(s) and computing the score of the base distribution.</p>
 </dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.transformed_distribution.TransformedDistribution.expand">
-<span class="sig-name descname"><span class="pre">expand</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">batch_shape</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_instance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/transformed_distribution.html#TransformedDistribution.expand"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/transformed_distribution.py#L103"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.transformed_distribution.TransformedDistribution.expand" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">expand</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">batch_shape</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_instance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/transformed_distribution.html#TransformedDistribution.expand"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/transformed_distribution.py#L103"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.transformed_distribution.TransformedDistribution.expand" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py property">
@@ -3978,21 +3978,21 @@ <h2><span class="hidden-section">TransformedDistribution</span><a class="headerl
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.transformed_distribution.TransformedDistribution.icdf">
-<span class="sig-name descname"><span class="pre">icdf</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/transformed_distribution.html#TransformedDistribution.icdf"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/transformed_distribution.py#L207"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.transformed_distribution.TransformedDistribution.icdf" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">icdf</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/transformed_distribution.html#TransformedDistribution.icdf"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/transformed_distribution.py#L207"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.transformed_distribution.TransformedDistribution.icdf" title="Permalink to this definition">¶</a></dt>
 <dd><p>Computes the inverse cumulative distribution function using
 transform(s) and computing the score of the base distribution.</p>
 </dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.transformed_distribution.TransformedDistribution.log_prob">
-<span class="sig-name descname"><span class="pre">log_prob</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/transformed_distribution.html#TransformedDistribution.log_prob"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/transformed_distribution.py#L158"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.transformed_distribution.TransformedDistribution.log_prob" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">log_prob</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/transformed_distribution.html#TransformedDistribution.log_prob"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/transformed_distribution.py#L158"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.transformed_distribution.TransformedDistribution.log_prob" title="Permalink to this definition">¶</a></dt>
 <dd><p>Scores the sample by inverting the transform(s) and computing the score
 using the score of the base distribution and the log abs det jacobian.</p>
 </dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.transformed_distribution.TransformedDistribution.rsample">
-<span class="sig-name descname"><span class="pre">rsample</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sample_shape</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.Size([])</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/transformed_distribution.html#TransformedDistribution.rsample"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/transformed_distribution.py#L146"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.transformed_distribution.TransformedDistribution.rsample" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">rsample</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sample_shape</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.Size([])</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/transformed_distribution.html#TransformedDistribution.rsample"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/transformed_distribution.py#L146"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.transformed_distribution.TransformedDistribution.rsample" title="Permalink to this definition">¶</a></dt>
 <dd><p>Generates a sample_shape shaped reparameterized sample or sample_shape
 shaped batch of reparameterized samples if the distribution parameters
 are batched. Samples first from base distribution and applies
@@ -4006,7 +4006,7 @@ <h2><span class="hidden-section">TransformedDistribution</span><a class="headerl
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.transformed_distribution.TransformedDistribution.sample">
-<span class="sig-name descname"><span class="pre">sample</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sample_shape</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.Size([])</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/transformed_distribution.html#TransformedDistribution.sample"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/transformed_distribution.py#L133"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.transformed_distribution.TransformedDistribution.sample" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">sample</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sample_shape</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.Size([])</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/transformed_distribution.html#TransformedDistribution.sample"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/transformed_distribution.py#L133"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.transformed_distribution.TransformedDistribution.sample" title="Permalink to this definition">¶</a></dt>
 <dd><p>Generates a sample_shape shaped sample or sample_shape shaped batch of
 samples if the distribution parameters are batched. Samples first from
 base distribution and applies <cite>transform()</cite> for every transform in the
@@ -4030,7 +4030,7 @@ <h2><span class="hidden-section">TransformedDistribution</span><a class="headerl
 <h2><span class="hidden-section">Uniform</span><a class="headerlink" href="#uniform" title="Permalink to this heading">¶</a></h2>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributions.uniform.Uniform">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.uniform.</span></span><span class="sig-name descname"><span class="pre">Uniform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">low</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">high</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">validate_args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/uniform.html#Uniform"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/uniform.py#L13"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.uniform.Uniform" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.uniform.</span></span><span class="sig-name descname"><span class="pre">Uniform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">low</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">high</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">validate_args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/uniform.html#Uniform"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/uniform.py#L13"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.uniform.Uniform" title="Permalink to this definition">¶</a></dt>
 <dd><p>Bases: <a class="reference internal" href="#torch.distributions.distribution.Distribution" title="torch.distributions.distribution.Distribution"><code class="xref py py-class docutils literal notranslate"><span class="pre">Distribution</span></code></a></p>
 <p>Generates uniformly distributed random samples from the half-open interval
 <code class="docutils literal notranslate"><span class="pre">[low,</span> <span class="pre">high)</span></code>.</p>
@@ -4055,17 +4055,17 @@ <h2><span class="hidden-section">Uniform</span><a class="headerlink" href="#unif
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.uniform.Uniform.cdf">
-<span class="sig-name descname"><span class="pre">cdf</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/uniform.html#Uniform.cdf"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/uniform.py#L89"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.uniform.Uniform.cdf" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">cdf</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/uniform.html#Uniform.cdf"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/uniform.py#L89"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.uniform.Uniform.cdf" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.uniform.Uniform.entropy">
-<span class="sig-name descname"><span class="pre">entropy</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/uniform.html#Uniform.entropy"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/uniform.py#L99"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.uniform.Uniform.entropy" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">entropy</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/uniform.html#Uniform.entropy"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/uniform.py#L99"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.uniform.Uniform.entropy" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.uniform.Uniform.expand">
-<span class="sig-name descname"><span class="pre">expand</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">batch_shape</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_instance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/uniform.html#Uniform.expand"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/uniform.py#L64"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.uniform.Uniform.expand" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">expand</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">batch_shape</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_instance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/uniform.html#Uniform.expand"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/uniform.py#L64"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.uniform.Uniform.expand" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py attribute">
@@ -4075,12 +4075,12 @@ <h2><span class="hidden-section">Uniform</span><a class="headerlink" href="#unif
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.uniform.Uniform.icdf">
-<span class="sig-name descname"><span class="pre">icdf</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/uniform.html#Uniform.icdf"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/uniform.py#L95"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.uniform.Uniform.icdf" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">icdf</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/uniform.html#Uniform.icdf"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/uniform.py#L95"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.uniform.Uniform.icdf" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.uniform.Uniform.log_prob">
-<span class="sig-name descname"><span class="pre">log_prob</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/uniform.html#Uniform.log_prob"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/uniform.py#L82"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.uniform.Uniform.log_prob" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">log_prob</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/uniform.html#Uniform.log_prob"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/uniform.py#L82"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.uniform.Uniform.log_prob" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py property">
@@ -4095,7 +4095,7 @@ <h2><span class="hidden-section">Uniform</span><a class="headerlink" href="#unif
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.uniform.Uniform.rsample">
-<span class="sig-name descname"><span class="pre">rsample</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sample_shape</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.Size([])</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/uniform.html#Uniform.rsample"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/uniform.py#L77"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.uniform.Uniform.rsample" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">rsample</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sample_shape</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.Size([])</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/uniform.html#Uniform.rsample"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/uniform.py#L77"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.uniform.Uniform.rsample" title="Permalink to this definition">¶</a></dt>
 <dd><dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
 <dd class="field-odd"><p><a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a></p>
@@ -4130,7 +4130,7 @@ <h2><span class="hidden-section">Uniform</span><a class="headerlink" href="#unif
 <h2><span class="hidden-section">VonMises</span><a class="headerlink" href="#vonmises" title="Permalink to this heading">¶</a></h2>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributions.von_mises.VonMises">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.von_mises.</span></span><span class="sig-name descname"><span class="pre">VonMises</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">loc</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">concentration</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">validate_args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/von_mises.html#VonMises"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/von_mises.py#L107"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.von_mises.VonMises" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.von_mises.</span></span><span class="sig-name descname"><span class="pre">VonMises</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">loc</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">concentration</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">validate_args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/von_mises.html#VonMises"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/von_mises.py#L107"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.von_mises.VonMises" title="Permalink to this definition">¶</a></dt>
 <dd><p>Bases: <a class="reference internal" href="#torch.distributions.distribution.Distribution" title="torch.distributions.distribution.Distribution"><code class="xref py py-class docutils literal notranslate"><span class="pre">Distribution</span></code></a></p>
 <p>A circular von Mises distribution.</p>
 <p>This implementation uses polar coordinates. The <code class="docutils literal notranslate"><span class="pre">loc</span></code> and <code class="docutils literal notranslate"><span class="pre">value</span></code> args
@@ -4159,7 +4159,7 @@ <h2><span class="hidden-section">VonMises</span><a class="headerlink" href="#von
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.von_mises.VonMises.expand">
-<span class="sig-name descname"><span class="pre">expand</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">batch_shape</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_instance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/von_mises.html#VonMises.expand"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/von_mises.py#L181"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.von_mises.VonMises.expand" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">expand</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">batch_shape</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_instance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/von_mises.html#VonMises.expand"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/von_mises.py#L181"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.von_mises.VonMises.expand" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py attribute">
@@ -4169,7 +4169,7 @@ <h2><span class="hidden-section">VonMises</span><a class="headerlink" href="#von
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.von_mises.VonMises.log_prob">
-<span class="sig-name descname"><span class="pre">log_prob</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/von_mises.html#VonMises.log_prob"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/von_mises.py#L135"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.von_mises.VonMises.log_prob" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">log_prob</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/von_mises.html#VonMises.log_prob"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/von_mises.py#L135"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.von_mises.VonMises.log_prob" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py property">
@@ -4185,7 +4185,7 @@ <h2><span class="hidden-section">VonMises</span><a class="headerlink" href="#von
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.von_mises.VonMises.sample">
-<span class="sig-name descname"><span class="pre">sample</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sample_shape</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.Size([])</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/von_mises.html#VonMises.sample"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/von_mises.py#L164"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.von_mises.VonMises.sample" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">sample</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sample_shape</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.Size([])</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/von_mises.html#VonMises.sample"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/von_mises.py#L164"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.von_mises.VonMises.sample" title="Permalink to this definition">¶</a></dt>
 <dd><p>The sampling algorithm for the von Mises distribution is based on the
 following paper: D.J. Best and N.I. Fisher, “Efficient simulation of the
 von Mises distribution.” Applied Statistics (1979): 152-157.</p>
@@ -4212,7 +4212,7 @@ <h2><span class="hidden-section">VonMises</span><a class="headerlink" href="#von
 <h2><span class="hidden-section">Weibull</span><a class="headerlink" href="#weibull" title="Permalink to this heading">¶</a></h2>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributions.weibull.Weibull">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.weibull.</span></span><span class="sig-name descname"><span class="pre">Weibull</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">scale</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">concentration</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">validate_args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/weibull.html#Weibull"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/weibull.py#L15"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.weibull.Weibull" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.weibull.</span></span><span class="sig-name descname"><span class="pre">Weibull</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">scale</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">concentration</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">validate_args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/weibull.html#Weibull"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/weibull.py#L15"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.weibull.Weibull" title="Permalink to this definition">¶</a></dt>
 <dd><p>Bases: <a class="reference internal" href="#torch.distributions.transformed_distribution.TransformedDistribution" title="torch.distributions.transformed_distribution.TransformedDistribution"><code class="xref py py-class docutils literal notranslate"><span class="pre">TransformedDistribution</span></code></a></p>
 <p>Samples from a two-parameter Weibull distribution.</p>
 <p class="rubric">Example</p>
@@ -4236,12 +4236,12 @@ <h2><span class="hidden-section">Weibull</span><a class="headerlink" href="#weib
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.weibull.Weibull.entropy">
-<span class="sig-name descname"><span class="pre">entropy</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/weibull.html#Weibull.entropy"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/weibull.py#L81"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.weibull.Weibull.entropy" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">entropy</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/weibull.html#Weibull.entropy"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/weibull.py#L81"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.weibull.Weibull.entropy" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.weibull.Weibull.expand">
-<span class="sig-name descname"><span class="pre">expand</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">batch_shape</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_instance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/weibull.html#Weibull.expand"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/weibull.py#L48"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.weibull.Weibull.expand" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">expand</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">batch_shape</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_instance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/weibull.html#Weibull.expand"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/weibull.py#L48"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.weibull.Weibull.expand" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py property">
@@ -4271,7 +4271,7 @@ <h2><span class="hidden-section">Weibull</span><a class="headerlink" href="#weib
 <h2><span class="hidden-section">Wishart</span><a class="headerlink" href="#wishart" title="Permalink to this heading">¶</a></h2>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributions.wishart.Wishart">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.wishart.</span></span><span class="sig-name descname"><span class="pre">Wishart</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">df</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">covariance_matrix</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">precision_matrix</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale_tril</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">validate_args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/wishart.html#Wishart"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/wishart.py#L33"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.wishart.Wishart" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.wishart.</span></span><span class="sig-name descname"><span class="pre">Wishart</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">df</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">covariance_matrix</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">precision_matrix</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale_tril</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">validate_args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/wishart.html#Wishart"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/wishart.py#L33"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.wishart.Wishart" title="Permalink to this definition">¶</a></dt>
 <dd><p>Bases: <a class="reference internal" href="#torch.distributions.exp_family.ExponentialFamily" title="torch.distributions.exp_family.ExponentialFamily"><code class="xref py py-class docutils literal notranslate"><span class="pre">ExponentialFamily</span></code></a></p>
 <p>Creates a Wishart distribution parameterized by a symmetric positive definite matrix <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi mathvariant="normal">Σ</mi></mrow><annotation encoding="application/x-tex">\Sigma</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.6833em;"></span><span class="mord">Σ</span></span></span></span></span>,
 or its Cholesky decomposition <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi mathvariant="bold">Σ</mi><mo>=</mo><mi mathvariant="bold">L</mi><msup><mi mathvariant="bold">L</mi><mi mathvariant="normal">⊤</mi></msup></mrow><annotation encoding="application/x-tex">\mathbf{\Sigma} = \mathbf{L}\mathbf{L}^\top</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.6861em;"></span><span class="mord mathbf">Σ</span><span class="mspace" style="margin-right:0.2778em;"></span><span class="mrel">=</span><span class="mspace" style="margin-right:0.2778em;"></span></span><span class="base"><span class="strut" style="height:0.8491em;"></span><span class="mord mathbf">L</span><span class="mord"><span class="mord mathbf">L</span><span class="msupsub"><span class="vlist-t"><span class="vlist-r"><span class="vlist" style="height:0.8491em;"><span style="top:-3.063em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight">⊤</span></span></span></span></span></span></span></span></span></span></span></span></p>
@@ -4319,12 +4319,12 @@ <h2><span class="hidden-section">Wishart</span><a class="headerlink" href="#wish
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.wishart.Wishart.entropy">
-<span class="sig-name descname"><span class="pre">entropy</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/wishart.html#Wishart.entropy"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/wishart.py#L311"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.wishart.Wishart.entropy" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">entropy</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/wishart.html#Wishart.entropy"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/wishart.py#L311"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.wishart.Wishart.entropy" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.wishart.Wishart.expand">
-<span class="sig-name descname"><span class="pre">expand</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">batch_shape</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_instance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/wishart.html#Wishart.expand"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/wishart.py#L147"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.wishart.Wishart.expand" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">expand</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">batch_shape</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_instance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/wishart.html#Wishart.expand"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/wishart.py#L147"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.wishart.Wishart.expand" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py attribute">
@@ -4334,7 +4334,7 @@ <h2><span class="hidden-section">Wishart</span><a class="headerlink" href="#wish
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.wishart.Wishart.log_prob">
-<span class="sig-name descname"><span class="pre">log_prob</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/wishart.html#Wishart.log_prob"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/wishart.py#L290"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.wishart.Wishart.log_prob" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">log_prob</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/wishart.html#Wishart.log_prob"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/wishart.py#L290"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.wishart.Wishart.log_prob" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py property">
@@ -4354,7 +4354,7 @@ <h2><span class="hidden-section">Wishart</span><a class="headerlink" href="#wish
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.wishart.Wishart.rsample">
-<span class="sig-name descname"><span class="pre">rsample</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sample_shape</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.Size([])</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_try_correction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/wishart.html#Wishart.rsample"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/wishart.py#L238"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.wishart.Wishart.rsample" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">rsample</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sample_shape</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.Size([])</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_try_correction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/wishart.html#Wishart.rsample"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/wishart.py#L238"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.wishart.Wishart.rsample" title="Permalink to this definition">¶</a></dt>
 <dd><div class="admonition warning">
 <p class="admonition-title">Warning</p>
 <p>In some cases, sampling algorithm based on Bartlett decomposition may return singular matrix samples.
@@ -4392,7 +4392,7 @@ <h2><span class="hidden-section">Wishart</span><a class="headerlink" href="#wish
 <span id="kl-divergence"></span><h2><cite>KL Divergence</cite><a class="headerlink" href="#module-torch.distributions.kl" title="Permalink to this heading">¶</a></h2>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.distributions.kl.kl_divergence">
-<span class="sig-prename descclassname"><span class="pre">torch.distributions.kl.</span></span><span class="sig-name descname"><span class="pre">kl_divergence</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">p</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">q</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/kl.html#kl_divergence"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/kl.py#L164"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.kl.kl_divergence" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributions.kl.</span></span><span class="sig-name descname"><span class="pre">kl_divergence</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">p</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">q</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/kl.html#kl_divergence"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/kl.py#L164"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.kl.kl_divergence" title="Permalink to this definition">¶</a></dt>
 <dd><p>Compute Kullback-Leibler divergence <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>K</mi><mi>L</mi><mo stretchy="false">(</mo><mi>p</mi><mi mathvariant="normal">∥</mi><mi>q</mi><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">KL(p \| q)</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mord mathnormal" style="margin-right:0.07153em;">K</span><span class="mord mathnormal">L</span><span class="mopen">(</span><span class="mord mathnormal">p</span><span class="mord">∥</span><span class="mord mathnormal" style="margin-right:0.03588em;">q</span><span class="mclose">)</span></span></span></span></span> between two distributions.</p>
 <div class="math">
 <span class="katex-display"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML" display="block"><semantics><mrow><mi>K</mi><mi>L</mi><mo stretchy="false">(</mo><mi>p</mi><mi mathvariant="normal">∥</mi><mi>q</mi><mo stretchy="false">)</mo><mo>=</mo><mo>∫</mo><mi>p</mi><mo stretchy="false">(</mo><mi>x</mi><mo stretchy="false">)</mo><mi>log</mi><mo>⁡</mo><mfrac><mrow><mi>p</mi><mo stretchy="false">(</mo><mi>x</mi><mo stretchy="false">)</mo></mrow><mrow><mi>q</mi><mo stretchy="false">(</mo><mi>x</mi><mo stretchy="false">)</mo></mrow></mfrac><mtext> </mtext><mi>d</mi><mi>x</mi></mrow><annotation encoding="application/x-tex">KL(p \| q) = \int p(x) \log\frac {p(x)} {q(x)} \,dx</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mord mathnormal" style="margin-right:0.07153em;">K</span><span class="mord mathnormal">L</span><span class="mopen">(</span><span class="mord mathnormal">p</span><span class="mord">∥</span><span class="mord mathnormal" style="margin-right:0.03588em;">q</span><span class="mclose">)</span><span class="mspace" style="margin-right:0.2778em;"></span><span class="mrel">=</span><span class="mspace" style="margin-right:0.2778em;"></span></span><span class="base"><span class="strut" style="height:2.363em;vertical-align:-0.936em;"></span><span class="mop op-symbol large-op" style="margin-right:0.44445em;position:relative;top:-0.0011em;">∫</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord mathnormal">p</span><span class="mopen">(</span><span class="mord mathnormal">x</span><span class="mclose">)</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mop">lo<span style="margin-right:0.01389em;">g</span></span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord"><span class="mopen nulldelimiter"></span><span class="mfrac"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:1.427em;"><span style="top:-2.314em;"><span class="pstrut" style="height:3em;"></span><span class="mord"><span class="mord mathnormal" style="margin-right:0.03588em;">q</span><span class="mopen">(</span><span class="mord mathnormal">x</span><span class="mclose">)</span></span></span><span style="top:-3.23em;"><span class="pstrut" style="height:3em;"></span><span class="frac-line" style="border-bottom-width:0.04em;"></span></span><span style="top:-3.677em;"><span class="pstrut" style="height:3em;"></span><span class="mord"><span class="mord mathnormal">p</span><span class="mopen">(</span><span class="mord mathnormal">x</span><span class="mclose">)</span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.936em;"><span></span></span></span></span></span><span class="mclose nulldelimiter"></span></span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord mathnormal">d</span><span class="mord mathnormal">x</span></span></span></span></span></div><dl class="field-list simple">
@@ -4509,7 +4509,7 @@ <h2><span class="hidden-section">Wishart</span><a class="headerlink" href="#wish
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.distributions.kl.register_kl">
-<span class="sig-prename descclassname"><span class="pre">torch.distributions.kl.</span></span><span class="sig-name descname"><span class="pre">register_kl</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">type_p</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">type_q</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/kl.html#register_kl"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/kl.py#L51"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.kl.register_kl" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributions.kl.</span></span><span class="sig-name descname"><span class="pre">register_kl</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">type_p</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">type_q</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/kl.html#register_kl"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/kl.py#L51"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.kl.register_kl" title="Permalink to this definition">¶</a></dt>
 <dd><p>Decorator to register a pairwise function with <a class="reference internal" href="#torch.distributions.kl.kl_divergence" title="torch.distributions.kl.kl_divergence"><code class="xref py py-meth docutils literal notranslate"><span class="pre">kl_divergence()</span></code></a>.
 Usage:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="nd">@register_kl</span><span class="p">(</span><span class="n">Normal</span><span class="p">,</span> <span class="n">Normal</span><span class="p">)</span>
@@ -4545,13 +4545,13 @@ <h2><span class="hidden-section">Wishart</span><a class="headerlink" href="#wish
 <span id="transforms"></span><h2><cite>Transforms</cite><a class="headerlink" href="#module-torch.distributions.transforms" title="Permalink to this heading">¶</a></h2>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributions.transforms.AbsTransform">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.transforms.</span></span><span class="sig-name descname"><span class="pre">AbsTransform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">cache_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/transforms.html#AbsTransform"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/transforms.py#L694"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.transforms.AbsTransform" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.transforms.</span></span><span class="sig-name descname"><span class="pre">AbsTransform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">cache_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/transforms.html#AbsTransform"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/transforms.py#L694"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.transforms.AbsTransform" title="Permalink to this definition">¶</a></dt>
 <dd><p>Transform via the mapping <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>y</mi><mo>=</mo><mi mathvariant="normal">∣</mi><mi>x</mi><mi mathvariant="normal">∣</mi></mrow><annotation encoding="application/x-tex">y = |x|</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.625em;vertical-align:-0.1944em;"></span><span class="mord mathnormal" style="margin-right:0.03588em;">y</span><span class="mspace" style="margin-right:0.2778em;"></span><span class="mrel">=</span><span class="mspace" style="margin-right:0.2778em;"></span></span><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mord">∣</span><span class="mord mathnormal">x</span><span class="mord">∣</span></span></span></span></span>.</p>
 </dd></dl>
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributions.transforms.AffineTransform">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.transforms.</span></span><span class="sig-name descname"><span class="pre">AffineTransform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">loc</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">event_dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cache_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/transforms.html#AffineTransform"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/transforms.py#L711"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.transforms.AffineTransform" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.transforms.</span></span><span class="sig-name descname"><span class="pre">AffineTransform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">loc</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">event_dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cache_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/transforms.html#AffineTransform"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/transforms.py#L711"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.transforms.AffineTransform" title="Permalink to this definition">¶</a></dt>
 <dd><p>Transform via the pointwise affine mapping <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>y</mi><mo>=</mo><mtext>loc</mtext><mo>+</mo><mtext>scale</mtext><mo>×</mo><mi>x</mi></mrow><annotation encoding="application/x-tex">y = \text{loc} + \text{scale} \times x</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.625em;vertical-align:-0.1944em;"></span><span class="mord mathnormal" style="margin-right:0.03588em;">y</span><span class="mspace" style="margin-right:0.2778em;"></span><span class="mrel">=</span><span class="mspace" style="margin-right:0.2778em;"></span></span><span class="base"><span class="strut" style="height:0.7778em;vertical-align:-0.0833em;"></span><span class="mord text"><span class="mord">loc</span></span><span class="mspace" style="margin-right:0.2222em;"></span><span class="mbin">+</span><span class="mspace" style="margin-right:0.2222em;"></span></span><span class="base"><span class="strut" style="height:0.7778em;vertical-align:-0.0833em;"></span><span class="mord text"><span class="mord">scale</span></span><span class="mspace" style="margin-right:0.2222em;"></span><span class="mbin">×</span><span class="mspace" style="margin-right:0.2222em;"></span></span><span class="base"><span class="strut" style="height:0.4306em;"></span><span class="mord mathnormal">x</span></span></span></span></span>.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -4568,7 +4568,7 @@ <h2><span class="hidden-section">Wishart</span><a class="headerlink" href="#wish
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributions.transforms.CatTransform">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.transforms.</span></span><span class="sig-name descname"><span class="pre">CatTransform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tseq</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lengths</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cache_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/transforms.html#CatTransform"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/transforms.py#L1024"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.transforms.CatTransform" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.transforms.</span></span><span class="sig-name descname"><span class="pre">CatTransform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tseq</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lengths</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cache_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/transforms.html#CatTransform"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/transforms.py#L1024"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.transforms.CatTransform" title="Permalink to this definition">¶</a></dt>
 <dd><p>Transform functor that applies a sequence of transforms <cite>tseq</cite>
 component-wise to each submatrix at <cite>dim</cite>, of length <cite>lengths[dim]</cite>,
 in a way compatible with <a class="reference internal" href="generated/torch.cat.html#torch.cat" title="torch.cat"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.cat()</span></code></a>.</p>
@@ -4584,7 +4584,7 @@ <h2><span class="hidden-section">Wishart</span><a class="headerlink" href="#wish
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributions.transforms.ComposeTransform">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.transforms.</span></span><span class="sig-name descname"><span class="pre">ComposeTransform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">parts</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cache_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/transforms.html#ComposeTransform"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/transforms.py#L277"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.transforms.ComposeTransform" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.transforms.</span></span><span class="sig-name descname"><span class="pre">ComposeTransform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">parts</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cache_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/transforms.html#ComposeTransform"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/transforms.py#L277"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.transforms.ComposeTransform" title="Permalink to this definition">¶</a></dt>
 <dd><p>Composes multiple transforms in a chain.
 The transforms being composed are responsible for caching.</p>
 <dl class="field-list simple">
@@ -4600,7 +4600,7 @@ <h2><span class="hidden-section">Wishart</span><a class="headerlink" href="#wish
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributions.transforms.CorrCholeskyTransform">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.transforms.</span></span><span class="sig-name descname"><span class="pre">CorrCholeskyTransform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">cache_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/transforms.html#CorrCholeskyTransform"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/transforms.py#L809"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.transforms.CorrCholeskyTransform" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.transforms.</span></span><span class="sig-name descname"><span class="pre">CorrCholeskyTransform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">cache_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/transforms.html#CorrCholeskyTransform"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/transforms.py#L809"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.transforms.CorrCholeskyTransform" title="Permalink to this definition">¶</a></dt>
 <dd><p>Transforms an uncontrained real vector <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>x</mi></mrow><annotation encoding="application/x-tex">x</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.4306em;"></span><span class="mord mathnormal">x</span></span></span></span></span> with length <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>D</mi><mo>∗</mo><mo stretchy="false">(</mo><mi>D</mi><mo>−</mo><mn>1</mn><mo stretchy="false">)</mo><mi mathvariant="normal">/</mi><mn>2</mn></mrow><annotation encoding="application/x-tex">D*(D-1)/2</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.6833em;"></span><span class="mord mathnormal" style="margin-right:0.02778em;">D</span><span class="mspace" style="margin-right:0.2222em;"></span><span class="mbin">∗</span><span class="mspace" style="margin-right:0.2222em;"></span></span><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mopen">(</span><span class="mord mathnormal" style="margin-right:0.02778em;">D</span><span class="mspace" style="margin-right:0.2222em;"></span><span class="mbin">−</span><span class="mspace" style="margin-right:0.2222em;"></span></span><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mord">1</span><span class="mclose">)</span><span class="mord">/2</span></span></span></span></span> into the
 Cholesky factor of a D-dimension correlation matrix. This Cholesky factor is a lower
 triangular matrix with positive diagonals and unit Euclidean norm for each row.
@@ -4632,7 +4632,7 @@ <h2><span class="hidden-section">Wishart</span><a class="headerlink" href="#wish
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributions.transforms.CumulativeDistributionTransform">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.transforms.</span></span><span class="sig-name descname"><span class="pre">CumulativeDistributionTransform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">distribution</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cache_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/transforms.html#CumulativeDistributionTransform"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/transforms.py#L1202"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.transforms.CumulativeDistributionTransform" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.transforms.</span></span><span class="sig-name descname"><span class="pre">CumulativeDistributionTransform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">distribution</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cache_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/transforms.html#CumulativeDistributionTransform"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/transforms.py#L1202"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.transforms.CumulativeDistributionTransform" title="Permalink to this definition">¶</a></dt>
 <dd><p>Transform via the cumulative distribution function of a probability distribution.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -4654,13 +4654,13 @@ <h2><span class="hidden-section">Wishart</span><a class="headerlink" href="#wish
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributions.transforms.ExpTransform">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.transforms.</span></span><span class="sig-name descname"><span class="pre">ExpTransform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">cache_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/transforms.html#ExpTransform"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/transforms.py#L542"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.transforms.ExpTransform" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.transforms.</span></span><span class="sig-name descname"><span class="pre">ExpTransform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">cache_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/transforms.html#ExpTransform"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/transforms.py#L542"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.transforms.ExpTransform" title="Permalink to this definition">¶</a></dt>
 <dd><p>Transform via the mapping <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>y</mi><mo>=</mo><mi>exp</mi><mo>⁡</mo><mo stretchy="false">(</mo><mi>x</mi><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">y = \exp(x)</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.625em;vertical-align:-0.1944em;"></span><span class="mord mathnormal" style="margin-right:0.03588em;">y</span><span class="mspace" style="margin-right:0.2778em;"></span><span class="mrel">=</span><span class="mspace" style="margin-right:0.2778em;"></span></span><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mop">exp</span><span class="mopen">(</span><span class="mord mathnormal">x</span><span class="mclose">)</span></span></span></span></span>.</p>
 </dd></dl>
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributions.transforms.IndependentTransform">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.transforms.</span></span><span class="sig-name descname"><span class="pre">IndependentTransform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">base_transform</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reinterpreted_batch_ndims</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cache_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/transforms.html#IndependentTransform"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/transforms.py#L402"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.transforms.IndependentTransform" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.transforms.</span></span><span class="sig-name descname"><span class="pre">IndependentTransform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">base_transform</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reinterpreted_batch_ndims</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cache_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/transforms.html#IndependentTransform"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/transforms.py#L402"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.transforms.IndependentTransform" title="Permalink to this definition">¶</a></dt>
 <dd><p>Wrapper around another transform to treat
 <code class="docutils literal notranslate"><span class="pre">reinterpreted_batch_ndims</span></code>-many extra of the right most dimensions as
 dependent. This has no effect on the forward or backward transforms, but
@@ -4679,7 +4679,7 @@ <h2><span class="hidden-section">Wishart</span><a class="headerlink" href="#wish
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributions.transforms.LowerCholeskyTransform">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.transforms.</span></span><span class="sig-name descname"><span class="pre">LowerCholeskyTransform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">cache_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/transforms.html#LowerCholeskyTransform"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/transforms.py#L982"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.transforms.LowerCholeskyTransform" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.transforms.</span></span><span class="sig-name descname"><span class="pre">LowerCholeskyTransform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">cache_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/transforms.html#LowerCholeskyTransform"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/transforms.py#L982"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.transforms.LowerCholeskyTransform" title="Permalink to this definition">¶</a></dt>
 <dd><p>Transform from unconstrained matrices to lower-triangular matrices with
 nonnegative diagonal entries.</p>
 <p>This is useful for parameterizing positive definite matrices in terms of
@@ -4688,19 +4688,19 @@ <h2><span class="hidden-section">Wishart</span><a class="headerlink" href="#wish
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributions.transforms.PositiveDefiniteTransform">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.transforms.</span></span><span class="sig-name descname"><span class="pre">PositiveDefiniteTransform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">cache_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/transforms.html#PositiveDefiniteTransform"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/transforms.py#L1004"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.transforms.PositiveDefiniteTransform" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.transforms.</span></span><span class="sig-name descname"><span class="pre">PositiveDefiniteTransform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">cache_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/transforms.html#PositiveDefiniteTransform"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/transforms.py#L1004"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.transforms.PositiveDefiniteTransform" title="Permalink to this definition">¶</a></dt>
 <dd><p>Transform from unconstrained matrices to positive-definite matrices.</p>
 </dd></dl>
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributions.transforms.PowerTransform">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.transforms.</span></span><span class="sig-name descname"><span class="pre">PowerTransform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">exponent</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cache_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/transforms.html#PowerTransform"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/transforms.py#L564"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.transforms.PowerTransform" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.transforms.</span></span><span class="sig-name descname"><span class="pre">PowerTransform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">exponent</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cache_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/transforms.html#PowerTransform"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/transforms.py#L564"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.transforms.PowerTransform" title="Permalink to this definition">¶</a></dt>
 <dd><p>Transform via the mapping <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>y</mi><mo>=</mo><msup><mi>x</mi><mtext>exponent</mtext></msup></mrow><annotation encoding="application/x-tex">y = x^{\text{exponent}}</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.625em;vertical-align:-0.1944em;"></span><span class="mord mathnormal" style="margin-right:0.03588em;">y</span><span class="mspace" style="margin-right:0.2778em;"></span><span class="mrel">=</span><span class="mspace" style="margin-right:0.2778em;"></span></span><span class="base"><span class="strut" style="height:0.7936em;"></span><span class="mord"><span class="mord mathnormal">x</span><span class="msupsub"><span class="vlist-t"><span class="vlist-r"><span class="vlist" style="height:0.7936em;"><span style="top:-3.063em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mord text mtight"><span class="mord mtight">exponent</span></span></span></span></span></span></span></span></span></span></span></span></span></span>.</p>
 </dd></dl>
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributions.transforms.ReshapeTransform">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.transforms.</span></span><span class="sig-name descname"><span class="pre">ReshapeTransform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">in_shape</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out_shape</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cache_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/transforms.html#ReshapeTransform"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/transforms.py#L473"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.transforms.ReshapeTransform" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.transforms.</span></span><span class="sig-name descname"><span class="pre">ReshapeTransform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">in_shape</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out_shape</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cache_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/transforms.html#ReshapeTransform"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/transforms.py#L473"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.transforms.ReshapeTransform" title="Permalink to this definition">¶</a></dt>
 <dd><p>Unit Jacobian transform to reshape the rightmost part of a tensor.</p>
 <p>Note that <code class="docutils literal notranslate"><span class="pre">in_shape</span></code> and <code class="docutils literal notranslate"><span class="pre">out_shape</span></code> must have the same number of
 elements, just as for <a class="reference internal" href="generated/torch.Tensor.reshape.html#torch.Tensor.reshape" title="torch.Tensor.reshape"><code class="xref py py-meth docutils literal notranslate"><span class="pre">torch.Tensor.reshape()</span></code></a>.</p>
@@ -4718,20 +4718,20 @@ <h2><span class="hidden-section">Wishart</span><a class="headerlink" href="#wish
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributions.transforms.SigmoidTransform">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.transforms.</span></span><span class="sig-name descname"><span class="pre">SigmoidTransform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">cache_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/transforms.html#SigmoidTransform"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/transforms.py#L611"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.transforms.SigmoidTransform" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.transforms.</span></span><span class="sig-name descname"><span class="pre">SigmoidTransform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">cache_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/transforms.html#SigmoidTransform"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/transforms.py#L611"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.transforms.SigmoidTransform" title="Permalink to this definition">¶</a></dt>
 <dd><p>Transform via the mapping <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>y</mi><mo>=</mo><mfrac><mn>1</mn><mrow><mn>1</mn><mo>+</mo><mi>exp</mi><mo>⁡</mo><mo stretchy="false">(</mo><mo>−</mo><mi>x</mi><mo stretchy="false">)</mo></mrow></mfrac></mrow><annotation encoding="application/x-tex">y = \frac{1}{1 + \exp(-x)}</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.625em;vertical-align:-0.1944em;"></span><span class="mord mathnormal" style="margin-right:0.03588em;">y</span><span class="mspace" style="margin-right:0.2778em;"></span><span class="mrel">=</span><span class="mspace" style="margin-right:0.2778em;"></span></span><span class="base"><span class="strut" style="height:1.3651em;vertical-align:-0.52em;"></span><span class="mord"><span class="mopen nulldelimiter"></span><span class="mfrac"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.8451em;"><span style="top:-2.655em;"><span class="pstrut" style="height:3em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mord mtight">1</span><span class="mbin mtight">+</span><span class="mop mtight"><span class="mtight">e</span><span class="mtight">x</span><span class="mtight">p</span></span><span class="mopen mtight">(</span><span class="mord mtight">−</span><span class="mord mathnormal mtight">x</span><span class="mclose mtight">)</span></span></span></span><span style="top:-3.23em;"><span class="pstrut" style="height:3em;"></span><span class="frac-line" style="border-bottom-width:0.04em;"></span></span><span style="top:-3.394em;"><span class="pstrut" style="height:3em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mord mtight">1</span></span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.52em;"><span></span></span></span></span></span><span class="mclose nulldelimiter"></span></span></span></span></span></span> and <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>x</mi><mo>=</mo><mtext>logit</mtext><mo stretchy="false">(</mo><mi>y</mi><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">x = \text{logit}(y)</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.4306em;"></span><span class="mord mathnormal">x</span><span class="mspace" style="margin-right:0.2778em;"></span><span class="mrel">=</span><span class="mspace" style="margin-right:0.2778em;"></span></span><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mord text"><span class="mord">logit</span></span><span class="mopen">(</span><span class="mord mathnormal" style="margin-right:0.03588em;">y</span><span class="mclose">)</span></span></span></span></span>.</p>
 </dd></dl>
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributions.transforms.SoftplusTransform">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.transforms.</span></span><span class="sig-name descname"><span class="pre">SoftplusTransform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">cache_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/transforms.html#SoftplusTransform"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/transforms.py#L635"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.transforms.SoftplusTransform" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.transforms.</span></span><span class="sig-name descname"><span class="pre">SoftplusTransform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">cache_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/transforms.html#SoftplusTransform"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/transforms.py#L635"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.transforms.SoftplusTransform" title="Permalink to this definition">¶</a></dt>
 <dd><p>Transform via the mapping <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mtext>Softplus</mtext><mo stretchy="false">(</mo><mi>x</mi><mo stretchy="false">)</mo><mo>=</mo><mi>log</mi><mo>⁡</mo><mo stretchy="false">(</mo><mn>1</mn><mo>+</mo><mi>exp</mi><mo>⁡</mo><mo stretchy="false">(</mo><mi>x</mi><mo stretchy="false">)</mo><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">\text{Softplus}(x) = \log(1 + \exp(x))</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mord text"><span class="mord">Softplus</span></span><span class="mopen">(</span><span class="mord mathnormal">x</span><span class="mclose">)</span><span class="mspace" style="margin-right:0.2778em;"></span><span class="mrel">=</span><span class="mspace" style="margin-right:0.2778em;"></span></span><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mop">lo<span style="margin-right:0.01389em;">g</span></span><span class="mopen">(</span><span class="mord">1</span><span class="mspace" style="margin-right:0.2222em;"></span><span class="mbin">+</span><span class="mspace" style="margin-right:0.2222em;"></span></span><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mop">exp</span><span class="mopen">(</span><span class="mord mathnormal">x</span><span class="mclose">))</span></span></span></span></span>.
 The implementation reverts to the linear function when <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>x</mi><mo>&gt;</mo><mn>20</mn></mrow><annotation encoding="application/x-tex">x &gt; 20</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.5782em;vertical-align:-0.0391em;"></span><span class="mord mathnormal">x</span><span class="mspace" style="margin-right:0.2778em;"></span><span class="mrel">&gt;</span><span class="mspace" style="margin-right:0.2778em;"></span></span><span class="base"><span class="strut" style="height:0.6444em;"></span><span class="mord">20</span></span></span></span></span>.</p>
 </dd></dl>
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributions.transforms.TanhTransform">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.transforms.</span></span><span class="sig-name descname"><span class="pre">TanhTransform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">cache_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/transforms.html#TanhTransform"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/transforms.py#L658"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.transforms.TanhTransform" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.transforms.</span></span><span class="sig-name descname"><span class="pre">TanhTransform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">cache_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/transforms.html#TanhTransform"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/transforms.py#L658"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.transforms.TanhTransform" title="Permalink to this definition">¶</a></dt>
 <dd><p>Transform via the mapping <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>y</mi><mo>=</mo><mi>tanh</mi><mo>⁡</mo><mo stretchy="false">(</mo><mi>x</mi><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">y = \tanh(x)</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.625em;vertical-align:-0.1944em;"></span><span class="mord mathnormal" style="margin-right:0.03588em;">y</span><span class="mspace" style="margin-right:0.2778em;"></span><span class="mrel">=</span><span class="mspace" style="margin-right:0.2778em;"></span></span><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mop">tanh</span><span class="mopen">(</span><span class="mord mathnormal">x</span><span class="mclose">)</span></span></span></span></span>.</p>
 <p>It is equivalent to
 <code class="docutils literal notranslate"><span class="pre">`</span>
@@ -4744,7 +4744,7 @@ <h2><span class="hidden-section">Wishart</span><a class="headerlink" href="#wish
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributions.transforms.SoftmaxTransform">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.transforms.</span></span><span class="sig-name descname"><span class="pre">SoftmaxTransform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">cache_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/transforms.html#SoftmaxTransform"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/transforms.py#L891"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.transforms.SoftmaxTransform" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.transforms.</span></span><span class="sig-name descname"><span class="pre">SoftmaxTransform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">cache_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/transforms.html#SoftmaxTransform"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/transforms.py#L891"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.transforms.SoftmaxTransform" title="Permalink to this definition">¶</a></dt>
 <dd><p>Transform from unconstrained space to the simplex via <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>y</mi><mo>=</mo><mi>exp</mi><mo>⁡</mo><mo stretchy="false">(</mo><mi>x</mi><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">y = \exp(x)</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.625em;vertical-align:-0.1944em;"></span><span class="mord mathnormal" style="margin-right:0.03588em;">y</span><span class="mspace" style="margin-right:0.2778em;"></span><span class="mrel">=</span><span class="mspace" style="margin-right:0.2778em;"></span></span><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mop">exp</span><span class="mopen">(</span><span class="mord mathnormal">x</span><span class="mclose">)</span></span></span></span></span> then
 normalizing.</p>
 <p>This is not bijective and cannot be used for HMC. However this acts mostly
@@ -4754,7 +4754,7 @@ <h2><span class="hidden-section">Wishart</span><a class="headerlink" href="#wish
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributions.transforms.StackTransform">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.transforms.</span></span><span class="sig-name descname"><span class="pre">StackTransform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tseq</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cache_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/transforms.html#StackTransform"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/transforms.py#L1130"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.transforms.StackTransform" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.transforms.</span></span><span class="sig-name descname"><span class="pre">StackTransform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tseq</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cache_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/transforms.html#StackTransform"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/transforms.py#L1130"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.transforms.StackTransform" title="Permalink to this definition">¶</a></dt>
 <dd><p>Transform functor that applies a sequence of transforms <cite>tseq</cite>
 component-wise to each submatrix at <cite>dim</cite>
 in a way compatible with <a class="reference internal" href="generated/torch.stack.html#torch.stack" title="torch.stack"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.stack()</span></code></a>.</p>
@@ -4768,7 +4768,7 @@ <h2><span class="hidden-section">Wishart</span><a class="headerlink" href="#wish
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributions.transforms.StickBreakingTransform">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.transforms.</span></span><span class="sig-name descname"><span class="pre">StickBreakingTransform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">cache_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/transforms.html#StickBreakingTransform"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/transforms.py#L926"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.transforms.StickBreakingTransform" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.transforms.</span></span><span class="sig-name descname"><span class="pre">StickBreakingTransform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">cache_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/transforms.html#StickBreakingTransform"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/transforms.py#L926"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.transforms.StickBreakingTransform" title="Permalink to this definition">¶</a></dt>
 <dd><p>Transform from unconstrained space to the simplex of one additional
 dimension via a stick-breaking process.</p>
 <p>This transform arises as an iterated sigmoid transform in a stick-breaking
@@ -4781,7 +4781,7 @@ <h2><span class="hidden-section">Wishart</span><a class="headerlink" href="#wish
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributions.transforms.Transform">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.transforms.</span></span><span class="sig-name descname"><span class="pre">Transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">cache_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/transforms.html#Transform"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/transforms.py#L46"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.transforms.Transform" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.transforms.</span></span><span class="sig-name descname"><span class="pre">Transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">cache_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/transforms.html#Transform"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/transforms.py#L46"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.transforms.Transform" title="Permalink to this definition">¶</a></dt>
 <dd><p>Abstract class for invertable transformations with computable log
 det jacobians. They are primarily used in
 <code class="xref py py-class docutils literal notranslate"><span class="pre">torch.distributions.TransformedDistribution</span></code>.</p>
@@ -4840,20 +4840,20 @@ <h2><span class="hidden-section">Wishart</span><a class="headerlink" href="#wish
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.transforms.Transform.log_abs_det_jacobian">
-<span class="sig-name descname"><span class="pre">log_abs_det_jacobian</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/transforms.html#Transform.log_abs_det_jacobian"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/transforms.py#L191"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.transforms.Transform.log_abs_det_jacobian" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">log_abs_det_jacobian</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/transforms.html#Transform.log_abs_det_jacobian"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/transforms.py#L191"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.transforms.Transform.log_abs_det_jacobian" title="Permalink to this definition">¶</a></dt>
 <dd><p>Computes the log det jacobian <cite>log |dy/dx|</cite> given input and output.</p>
 </dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.transforms.Transform.forward_shape">
-<span class="sig-name descname"><span class="pre">forward_shape</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">shape</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/transforms.html#Transform.forward_shape"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/transforms.py#L200"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.transforms.Transform.forward_shape" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">forward_shape</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">shape</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/transforms.html#Transform.forward_shape"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/transforms.py#L200"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.transforms.Transform.forward_shape" title="Permalink to this definition">¶</a></dt>
 <dd><p>Infers the shape of the forward computation, given the input shape.
 Defaults to preserving shape.</p>
 </dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.transforms.Transform.inverse_shape">
-<span class="sig-name descname"><span class="pre">inverse_shape</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">shape</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/transforms.html#Transform.inverse_shape"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/transforms.py#L207"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.transforms.Transform.inverse_shape" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">inverse_shape</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">shape</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/transforms.html#Transform.inverse_shape"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/transforms.py#L207"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.transforms.Transform.inverse_shape" title="Permalink to this definition">¶</a></dt>
 <dd><p>Infers the shapes of the inverse computation, given the output shape.
 Defaults to preserving shape.</p>
 </dd></dl>
@@ -4865,7 +4865,7 @@ <h2><span class="hidden-section">Wishart</span><a class="headerlink" href="#wish
 <span id="constraints"></span><h2><cite>Constraints</cite><a class="headerlink" href="#module-torch.distributions.constraints" title="Permalink to this heading">¶</a></h2>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributions.constraints.Constraint">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.constraints.</span></span><span class="sig-name descname"><span class="pre">Constraint</span></span><a class="reference internal" href="_modules/torch/distributions/constraints.html#Constraint"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/constraints.py#L77"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.constraints.Constraint" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.constraints.</span></span><span class="sig-name descname"><span class="pre">Constraint</span></span><a class="reference internal" href="_modules/torch/distributions/constraints.html#Constraint"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/constraints.py#L77"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.constraints.Constraint" title="Permalink to this definition">¶</a></dt>
 <dd><p>Abstract base class for constraints.</p>
 <p>A constraint object represents a region over which a variable is valid,
 e.g. within which a variable can be optimized.</p>
@@ -4882,7 +4882,7 @@ <h2><span class="hidden-section">Wishart</span><a class="headerlink" href="#wish
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.constraints.Constraint.check">
-<span class="sig-name descname"><span class="pre">check</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/constraints.html#Constraint.check"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/constraints.py#L95"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.constraints.Constraint.check" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">check</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/constraints.html#Constraint.check"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/constraints.py#L95"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.constraints.Constraint.check" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns a byte tensor of <code class="docutils literal notranslate"><span class="pre">sample_shape</span> <span class="pre">+</span> <span class="pre">batch_shape</span></code> indicating
 whether each event in value satisfies this constraint.</p>
 </dd></dl>
@@ -4891,55 +4891,55 @@ <h2><span class="hidden-section">Wishart</span><a class="headerlink" href="#wish
 
 <dl class="py attribute">
 <dt class="sig sig-object py" id="torch.distributions.constraints.cat">
-<span class="sig-prename descclassname"><span class="pre">torch.distributions.constraints.</span></span><span class="sig-name descname"><span class="pre">cat</span></span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/constraints.py#L592"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.constraints.cat" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributions.constraints.</span></span><span class="sig-name descname"><span class="pre">cat</span></span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/constraints.py#L592"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.constraints.cat" title="Permalink to this definition">¶</a></dt>
 <dd><p>alias of <code class="xref py py-class docutils literal notranslate"><span class="pre">_Cat</span></code></p>
 </dd></dl>
 
 <dl class="py attribute">
 <dt class="sig sig-object py" id="torch.distributions.constraints.dependent_property">
-<span class="sig-prename descclassname"><span class="pre">torch.distributions.constraints.</span></span><span class="sig-name descname"><span class="pre">dependent_property</span></span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/constraints.py#L179"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.constraints.dependent_property" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributions.constraints.</span></span><span class="sig-name descname"><span class="pre">dependent_property</span></span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/constraints.py#L179"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.constraints.dependent_property" title="Permalink to this definition">¶</a></dt>
 <dd><p>alias of <code class="xref py py-class docutils literal notranslate"><span class="pre">_DependentProperty</span></code></p>
 </dd></dl>
 
 <dl class="py attribute">
 <dt class="sig sig-object py" id="torch.distributions.constraints.greater_than">
-<span class="sig-prename descclassname"><span class="pre">torch.distributions.constraints.</span></span><span class="sig-name descname"><span class="pre">greater_than</span></span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/constraints.py#L367"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.constraints.greater_than" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributions.constraints.</span></span><span class="sig-name descname"><span class="pre">greater_than</span></span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/constraints.py#L367"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.constraints.greater_than" title="Permalink to this definition">¶</a></dt>
 <dd><p>alias of <code class="xref py py-class docutils literal notranslate"><span class="pre">_GreaterThan</span></code></p>
 </dd></dl>
 
 <dl class="py attribute">
 <dt class="sig sig-object py" id="torch.distributions.constraints.greater_than_eq">
-<span class="sig-prename descclassname"><span class="pre">torch.distributions.constraints.</span></span><span class="sig-name descname"><span class="pre">greater_than_eq</span></span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/constraints.py#L385"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.constraints.greater_than_eq" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributions.constraints.</span></span><span class="sig-name descname"><span class="pre">greater_than_eq</span></span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/constraints.py#L385"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.constraints.greater_than_eq" title="Permalink to this definition">¶</a></dt>
 <dd><p>alias of <code class="xref py py-class docutils literal notranslate"><span class="pre">_GreaterThanEq</span></code></p>
 </dd></dl>
 
 <dl class="py attribute">
 <dt class="sig sig-object py" id="torch.distributions.constraints.independent">
-<span class="sig-prename descclassname"><span class="pre">torch.distributions.constraints.</span></span><span class="sig-name descname"><span class="pre">independent</span></span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/constraints.py#L228"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.constraints.independent" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributions.constraints.</span></span><span class="sig-name descname"><span class="pre">independent</span></span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/constraints.py#L228"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.constraints.independent" title="Permalink to this definition">¶</a></dt>
 <dd><p>alias of <code class="xref py py-class docutils literal notranslate"><span class="pre">_IndependentConstraint</span></code></p>
 </dd></dl>
 
 <dl class="py attribute">
 <dt class="sig sig-object py" id="torch.distributions.constraints.integer_interval">
-<span class="sig-prename descclassname"><span class="pre">torch.distributions.constraints.</span></span><span class="sig-name descname"><span class="pre">integer_interval</span></span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/constraints.py#L293"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.constraints.integer_interval" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributions.constraints.</span></span><span class="sig-name descname"><span class="pre">integer_interval</span></span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/constraints.py#L293"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.constraints.integer_interval" title="Permalink to this definition">¶</a></dt>
 <dd><p>alias of <code class="xref py py-class docutils literal notranslate"><span class="pre">_IntegerInterval</span></code></p>
 </dd></dl>
 
 <dl class="py attribute">
 <dt class="sig sig-object py" id="torch.distributions.constraints.interval">
-<span class="sig-prename descclassname"><span class="pre">torch.distributions.constraints.</span></span><span class="sig-name descname"><span class="pre">interval</span></span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/constraints.py#L421"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.constraints.interval" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributions.constraints.</span></span><span class="sig-name descname"><span class="pre">interval</span></span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/constraints.py#L421"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.constraints.interval" title="Permalink to this definition">¶</a></dt>
 <dd><p>alias of <code class="xref py py-class docutils literal notranslate"><span class="pre">_Interval</span></code></p>
 </dd></dl>
 
 <dl class="py attribute">
 <dt class="sig sig-object py" id="torch.distributions.constraints.half_open_interval">
-<span class="sig-prename descclassname"><span class="pre">torch.distributions.constraints.</span></span><span class="sig-name descname"><span class="pre">half_open_interval</span></span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/constraints.py#L442"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.constraints.half_open_interval" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributions.constraints.</span></span><span class="sig-name descname"><span class="pre">half_open_interval</span></span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/constraints.py#L442"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.constraints.half_open_interval" title="Permalink to this definition">¶</a></dt>
 <dd><p>alias of <code class="xref py py-class docutils literal notranslate"><span class="pre">_HalfOpenInterval</span></code></p>
 </dd></dl>
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.distributions.constraints.is_dependent">
-<span class="sig-prename descclassname"><span class="pre">torch.distributions.constraints.</span></span><span class="sig-name descname"><span class="pre">is_dependent</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">constraint</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/constraints.html#is_dependent"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/constraints.py#L153"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.constraints.is_dependent" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributions.constraints.</span></span><span class="sig-name descname"><span class="pre">is_dependent</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">constraint</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/constraints.html#is_dependent"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/constraints.py#L153"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.constraints.is_dependent" title="Permalink to this definition">¶</a></dt>
 <dd><p>Checks if <code class="docutils literal notranslate"><span class="pre">constraint</span></code> is a <code class="docutils literal notranslate"><span class="pre">_Dependent</span></code> object.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -4972,19 +4972,19 @@ <h2><span class="hidden-section">Wishart</span><a class="headerlink" href="#wish
 
 <dl class="py attribute">
 <dt class="sig sig-object py" id="torch.distributions.constraints.less_than">
-<span class="sig-prename descclassname"><span class="pre">torch.distributions.constraints.</span></span><span class="sig-name descname"><span class="pre">less_than</span></span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/constraints.py#L403"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.constraints.less_than" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributions.constraints.</span></span><span class="sig-name descname"><span class="pre">less_than</span></span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/constraints.py#L403"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.constraints.less_than" title="Permalink to this definition">¶</a></dt>
 <dd><p>alias of <code class="xref py py-class docutils literal notranslate"><span class="pre">_LessThan</span></code></p>
 </dd></dl>
 
 <dl class="py attribute">
 <dt class="sig sig-object py" id="torch.distributions.constraints.multinomial">
-<span class="sig-prename descclassname"><span class="pre">torch.distributions.constraints.</span></span><span class="sig-name descname"><span class="pre">multinomial</span></span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/constraints.py#L475"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.constraints.multinomial" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributions.constraints.</span></span><span class="sig-name descname"><span class="pre">multinomial</span></span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/constraints.py#L475"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.constraints.multinomial" title="Permalink to this definition">¶</a></dt>
 <dd><p>alias of <code class="xref py py-class docutils literal notranslate"><span class="pre">_Multinomial</span></code></p>
 </dd></dl>
 
 <dl class="py attribute">
 <dt class="sig sig-object py" id="torch.distributions.constraints.stack">
-<span class="sig-prename descclassname"><span class="pre">torch.distributions.constraints.</span></span><span class="sig-name descname"><span class="pre">stack</span></span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/constraints.py#L628"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.constraints.stack" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributions.constraints.</span></span><span class="sig-name descname"><span class="pre">stack</span></span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/constraints.py#L628"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.constraints.stack" title="Permalink to this definition">¶</a></dt>
 <dd><p>alias of <code class="xref py py-class docutils literal notranslate"><span class="pre">_Stack</span></code></p>
 </dd></dl>
 
@@ -5057,11 +5057,11 @@ <h2><span class="hidden-section">Wishart</span><a class="headerlink" href="#wish
 object.</p>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributions.constraint_registry.ConstraintRegistry">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.constraint_registry.</span></span><span class="sig-name descname"><span class="pre">ConstraintRegistry</span></span><a class="reference internal" href="_modules/torch/distributions/constraint_registry.html#ConstraintRegistry"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/constraint_registry.py#L81"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.constraint_registry.ConstraintRegistry" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributions.constraint_registry.</span></span><span class="sig-name descname"><span class="pre">ConstraintRegistry</span></span><a class="reference internal" href="_modules/torch/distributions/constraint_registry.html#ConstraintRegistry"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/constraint_registry.py#L81"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.constraint_registry.ConstraintRegistry" title="Permalink to this definition">¶</a></dt>
 <dd><p>Registry to link constraints to transforms.</p>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributions.constraint_registry.ConstraintRegistry.register">
-<span class="sig-name descname"><span class="pre">register</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">constraint</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">factory</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/constraint_registry.html#ConstraintRegistry.register"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributions/constraint_registry.py#L90"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.constraint_registry.ConstraintRegistry.register" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">constraint</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">factory</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/constraint_registry.html#ConstraintRegistry.register"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributions/constraint_registry.py#L90"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributions.constraint_registry.ConstraintRegistry.register" title="Permalink to this definition">¶</a></dt>
 <dd><p>Registers a <a class="reference internal" href="#torch.distributions.constraints.Constraint" title="torch.distributions.constraints.Constraint"><code class="xref py py-class docutils literal notranslate"><span class="pre">Constraint</span></code></a>
 subclass in this registry. Usage:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="nd">@my_registry</span><span class="o">.</span><span class="n">register</span><span class="p">(</span><span class="n">MyConstraintClass</span><span class="p">)</span>
diff --git a/main/dlpack.html b/main/dlpack.html
index 53778d07629..aa40f20c6a6 100644
--- a/main/dlpack.html
+++ b/main/dlpack.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -610,7 +610,7 @@
 <h1>torch.utils.dlpack<a class="headerlink" href="#torch-utils-dlpack" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.utils.dlpack.from_dlpack">
-<span class="sig-prename descclassname"><span class="pre">torch.utils.dlpack.</span></span><span class="sig-name descname"><span class="pre">from_dlpack</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">ext_tensor</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><span class="pre">Tensor</span></a></span></span><a class="reference internal" href="_modules/torch/utils/dlpack.html#from_dlpack"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/utils/dlpack.py#L49"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.dlpack.from_dlpack" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.utils.dlpack.</span></span><span class="sig-name descname"><span class="pre">from_dlpack</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">ext_tensor</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><span class="pre">Tensor</span></a></span></span><a class="reference internal" href="_modules/torch/utils/dlpack.html#from_dlpack"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/utils/dlpack.py#L49"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.dlpack.from_dlpack" title="Permalink to this definition">¶</a></dt>
 <dd><p>Converts a tensor from an external library into a <code class="docutils literal notranslate"><span class="pre">torch.Tensor</span></code>.</p>
 <p>The returned PyTorch tensor will share the memory with the input tensor
 (which may have come from another library). Note that in-place operations
diff --git a/main/elastic/agent.html b/main/elastic/agent.html
index de984573108..91883e7d061 100644
--- a/main/elastic/agent.html
+++ b/main/elastic/agent.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -638,7 +638,7 @@ <h2>Concepts<a class="headerlink" href="#concepts" title="Permalink to this head
 are relevant to understanding the role of the <code class="docutils literal notranslate"><span class="pre">agent</span></code> in torchelastic.</p>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributed.elastic.agent.server.ElasticAgent">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.agent.server.</span></span><span class="sig-name descname"><span class="pre">ElasticAgent</span></span><a class="reference internal" href="../_modules/torch/distributed/elastic/agent/server/api.html#ElasticAgent"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/elastic/agent/server/api.py#L371"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.agent.server.ElasticAgent" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.agent.server.</span></span><span class="sig-name descname"><span class="pre">ElasticAgent</span></span><a class="reference internal" href="../_modules/torch/distributed/elastic/agent/server/api.html#ElasticAgent"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/elastic/agent/server/api.py#L371"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.agent.server.ElasticAgent" title="Permalink to this definition">¶</a></dt>
 <dd><p>An agent process responsible for managing one or more worker processes.</p>
 <p>The worker processes are assumed to be regular distributed PyTorch scripts.
 When the worker process is created by the agent, the agent provides the
@@ -670,7 +670,7 @@ <h2>Concepts<a class="headerlink" href="#concepts" title="Permalink to this head
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.elastic.agent.server.ElasticAgent.get_worker_group">
-<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">get_worker_group</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">role</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'default'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/agent/server/api.html#ElasticAgent.get_worker_group"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/elastic/agent/server/api.py#L421"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.agent.server.ElasticAgent.get_worker_group" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">get_worker_group</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">role</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'default'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/agent/server/api.html#ElasticAgent.get_worker_group"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/elastic/agent/server/api.py#L421"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.agent.server.ElasticAgent.get_worker_group" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the <code class="docutils literal notranslate"><span class="pre">WorkerGroup</span></code> for the given <code class="docutils literal notranslate"><span class="pre">role</span></code>.</p>
 <p>Note that the worker group is a mutable object and hence in a
 multi-threaded/process environment it may change state.
@@ -685,7 +685,7 @@ <h2>Concepts<a class="headerlink" href="#concepts" title="Permalink to this head
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.elastic.agent.server.ElasticAgent.run">
-<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">run</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">role</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'default'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/agent/server/api.html#ElasticAgent.run"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/elastic/agent/server/api.py#L406"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.agent.server.ElasticAgent.run" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">run</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">role</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'default'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/agent/server/api.html#ElasticAgent.run"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/elastic/agent/server/api.py#L406"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.agent.server.ElasticAgent.run" title="Permalink to this definition">¶</a></dt>
 <dd><p>Run the agent.</p>
 <p>Supports retrying the worker group on failures up to <code class="docutils literal notranslate"><span class="pre">max_restarts</span></code>.</p>
 <dl class="field-list simple">
@@ -706,7 +706,7 @@ <h2>Concepts<a class="headerlink" href="#concepts" title="Permalink to this head
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributed.elastic.agent.server.WorkerSpec">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.agent.server.</span></span><span class="sig-name descname"><span class="pre">WorkerSpec</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">role</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">local_world_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">rdzv_handler</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fn</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">entrypoint</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">()</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_restarts</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">3</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">monitor_interval</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">master_port</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">master_addr</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">local_addr</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/agent/server/api.html#WorkerSpec"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/elastic/agent/server/api.py#L47"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.agent.server.WorkerSpec" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.agent.server.</span></span><span class="sig-name descname"><span class="pre">WorkerSpec</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">role</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">local_world_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">rdzv_handler</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fn</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">entrypoint</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">()</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_restarts</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">3</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">monitor_interval</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">master_port</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">master_addr</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">local_addr</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/agent/server/api.html#WorkerSpec"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/elastic/agent/server/api.py#L47"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.agent.server.WorkerSpec" title="Permalink to this definition">¶</a></dt>
 <dd><p>Blueprint information about a particular type of worker.</p>
 <p>For a given role, there must only exist a single worker spec.
 Worker spec is expected to be homogeneous across all nodes (machine),
@@ -737,7 +737,7 @@ <h2>Concepts<a class="headerlink" href="#concepts" title="Permalink to this head
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.elastic.agent.server.WorkerSpec.get_entrypoint_name">
-<span class="sig-name descname"><span class="pre">get_entrypoint_name</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/agent/server/api.html#WorkerSpec.get_entrypoint_name"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/elastic/agent/server/api.py#L103"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.agent.server.WorkerSpec.get_entrypoint_name" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">get_entrypoint_name</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/agent/server/api.html#WorkerSpec.get_entrypoint_name"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/elastic/agent/server/api.py#L103"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.agent.server.WorkerSpec.get_entrypoint_name" title="Permalink to this definition">¶</a></dt>
 <dd><p>Get the entry point name.</p>
 <p>If the entrypoint is a function (e.g. <code class="docutils literal notranslate"><span class="pre">Callable</span></code>) returns its <code class="docutils literal notranslate"><span class="pre">__qualname__</span></code>
 else if the entrypoint is a binary (e.g. <code class="docutils literal notranslate"><span class="pre">str</span></code>), returns the binary name.</p>
@@ -747,7 +747,7 @@ <h2>Concepts<a class="headerlink" href="#concepts" title="Permalink to this head
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributed.elastic.agent.server.WorkerState">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.agent.server.</span></span><span class="sig-name descname"><span class="pre">WorkerState</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/agent/server/api.html#WorkerState"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/elastic/agent/server/api.py#L190"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.agent.server.WorkerState" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.agent.server.</span></span><span class="sig-name descname"><span class="pre">WorkerState</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/agent/server/api.html#WorkerState"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/elastic/agent/server/api.py#L190"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.agent.server.WorkerState" title="Permalink to this definition">¶</a></dt>
 <dd><p>A state of the <code class="docutils literal notranslate"><span class="pre">WorkerGroup</span></code>.</p>
 <p>Workers in a worker group change state as a unit. If a single worker
 in a worker group fails the entire set is considered failed:</p>
@@ -779,7 +779,7 @@ <h2>Concepts<a class="headerlink" href="#concepts" title="Permalink to this head
 self terminating and allowing the job manager to retry the node.</p>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.elastic.agent.server.WorkerState.is_running">
-<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">is_running</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/agent/server/api.html#WorkerState.is_running"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/elastic/agent/server/api.py#L233"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.agent.server.WorkerState.is_running" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">is_running</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/agent/server/api.html#WorkerState.is_running"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/elastic/agent/server/api.py#L233"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.agent.server.WorkerState.is_running" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the state of the Worker.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Returns</dt>
@@ -796,7 +796,7 @@ <h2>Concepts<a class="headerlink" href="#concepts" title="Permalink to this head
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributed.elastic.agent.server.Worker">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.agent.server.</span></span><span class="sig-name descname"><span class="pre">Worker</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">local_rank</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">global_rank</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">role_rank</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">world_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">role_world_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-1</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/agent/server/api.html#Worker"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/elastic/agent/server/api.py#L116"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.agent.server.Worker" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.agent.server.</span></span><span class="sig-name descname"><span class="pre">Worker</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">local_rank</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">global_rank</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">role_rank</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">world_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">role_world_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-1</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/agent/server/api.html#Worker"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/elastic/agent/server/api.py#L116"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.agent.server.Worker" title="Permalink to this definition">¶</a></dt>
 <dd><p>A worker instance.</p>
 <p>Contrast this with <code class="docutils literal notranslate"><span class="pre">WorkerSpec</span></code> that represents the specifications of a
 worker. A <code class="docutils literal notranslate"><span class="pre">Worker</span></code> is created from a <code class="docutils literal notranslate"><span class="pre">WorkerSpec</span></code>. A <code class="docutils literal notranslate"><span class="pre">Worker</span></code> is to
@@ -821,7 +821,7 @@ <h2>Concepts<a class="headerlink" href="#concepts" title="Permalink to this head
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributed.elastic.agent.server.WorkerGroup">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.agent.server.</span></span><span class="sig-name descname"><span class="pre">WorkerGroup</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">spec</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/agent/server/api.html#WorkerGroup"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/elastic/agent/server/api.py#L244"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.agent.server.WorkerGroup" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.agent.server.</span></span><span class="sig-name descname"><span class="pre">WorkerGroup</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">spec</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/agent/server/api.html#WorkerGroup"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/elastic/agent/server/api.py#L244"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.agent.server.WorkerGroup" title="Permalink to this definition">¶</a></dt>
 <dd><p>A set of <code class="docutils literal notranslate"><span class="pre">Worker</span></code> instances.</p>
 <p>The class defines a set of <code class="docutils literal notranslate"><span class="pre">Worker</span></code> instances for the given <code class="docutils literal notranslate"><span class="pre">WorkerSpec</span></code> managed by <code class="docutils literal notranslate"><span class="pre">ElasticAgent</span></code>. Whether the worker
 group contains cross instance workers or not depends on the implementation of the agent.</p>
@@ -835,7 +835,7 @@ <h2>Implementations<a class="headerlink" href="#implementations" title="Permalin
 <p>Below are the agent implementations provided by torchelastic.</p>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributed.elastic.agent.server.local_elastic_agent.LocalElasticAgent">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.agent.server.local_elastic_agent.</span></span><span class="sig-name descname"><span class="pre">LocalElasticAgent</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">spec</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">logs_specs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">start_method</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'spawn'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">exit_barrier_timeout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">300</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">log_line_prefix_template</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/agent/server/local_elastic_agent.html#LocalElasticAgent"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/elastic/agent/server/local_elastic_agent.py#L60"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.agent.server.local_elastic_agent.LocalElasticAgent" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.agent.server.local_elastic_agent.</span></span><span class="sig-name descname"><span class="pre">LocalElasticAgent</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">spec</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">logs_specs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">start_method</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'spawn'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">exit_barrier_timeout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">300</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">log_line_prefix_template</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/agent/server/local_elastic_agent.html#LocalElasticAgent"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/elastic/agent/server/local_elastic_agent.py#L60"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.agent.server.local_elastic_agent.LocalElasticAgent" title="Permalink to this definition">¶</a></dt>
 <dd><p>An implementation of <code class="xref py py-class docutils literal notranslate"><span class="pre">torchelastic.agent.server.ElasticAgent</span></code> that handles host-local workers.</p>
 <p>This agent is deployed per host and is configured to spawn <code class="docutils literal notranslate"><span class="pre">n</span></code> workers.
 When using GPUs, <code class="docutils literal notranslate"><span class="pre">n</span></code> maps to the number of GPUs available on the host.</p>
@@ -921,7 +921,7 @@ <h2>Extending the Agent<a class="headerlink" href="#extending-the-agent" title="
 to implement.</p>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributed.elastic.agent.server.SimpleElasticAgent">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.agent.server.</span></span><span class="sig-name descname"><span class="pre">SimpleElasticAgent</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">spec</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">exit_barrier_timeout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">300</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/agent/server/api.html#SimpleElasticAgent"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/elastic/agent/server/api.py#L433"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.agent.server.SimpleElasticAgent" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.agent.server.</span></span><span class="sig-name descname"><span class="pre">SimpleElasticAgent</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">spec</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">exit_barrier_timeout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">300</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/agent/server/api.html#SimpleElasticAgent"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/elastic/agent/server/api.py#L433"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.agent.server.SimpleElasticAgent" title="Permalink to this definition">¶</a></dt>
 <dd><p>An <code class="docutils literal notranslate"><span class="pre">ElasticAgent</span></code> that manages one particular type of worker role.</p>
 <p>An <code class="docutils literal notranslate"><span class="pre">ElasticAgent</span></code> that manages workers (<code class="docutils literal notranslate"><span class="pre">WorkerGroup</span></code>) for a single <code class="docutils literal notranslate"><span class="pre">WorkerSpec</span></code>
 such as one particular type of worker role.</p>
@@ -929,7 +929,7 @@ <h2>Extending the Agent<a class="headerlink" href="#extending-the-agent" title="
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.elastic.agent.server.SimpleElasticAgent._assign_worker_ranks">
-<span class="sig-name descname"><span class="pre">_assign_worker_ranks</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">store</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group_rank</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group_world_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">spec</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/agent/server/api.html#SimpleElasticAgent._assign_worker_ranks"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/elastic/agent/server/api.py#L554"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.agent.server.SimpleElasticAgent._assign_worker_ranks" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">_assign_worker_ranks</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">store</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group_rank</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group_world_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">spec</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/agent/server/api.html#SimpleElasticAgent._assign_worker_ranks"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/elastic/agent/server/api.py#L554"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.agent.server.SimpleElasticAgent._assign_worker_ranks" title="Permalink to this definition">¶</a></dt>
 <dd><p>Determine proper ranks for worker processes.</p>
 <p>Fast Path: when all workers have the same role and world size. We calculate
 the global rank to be group_rank * group_world_size + local_rank. And the
@@ -965,7 +965,7 @@ <h2>Extending the Agent<a class="headerlink" href="#extending-the-agent" title="
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.elastic.agent.server.SimpleElasticAgent._exit_barrier">
-<span class="sig-name descname"><span class="pre">_exit_barrier</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/agent/server/api.html#SimpleElasticAgent._exit_barrier"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/elastic/agent/server/api.py#L923"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.agent.server.SimpleElasticAgent._exit_barrier" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">_exit_barrier</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/agent/server/api.html#SimpleElasticAgent._exit_barrier"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/elastic/agent/server/api.py#L923"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.agent.server.SimpleElasticAgent._exit_barrier" title="Permalink to this definition">¶</a></dt>
 <dd><p>Define a barrier that keeps the agent process alive until all workers finish.</p>
 <p>Wait for <code class="docutils literal notranslate"><span class="pre">exit_barrier_timeout</span></code> seconds for all agents to finish
 executing their local workers (either successfully or not). This
@@ -975,7 +975,7 @@ <h2>Extending the Agent<a class="headerlink" href="#extending-the-agent" title="
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.elastic.agent.server.SimpleElasticAgent._initialize_workers">
-<span class="sig-name descname"><span class="pre">_initialize_workers</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">worker_group</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/agent/server/api.html#SimpleElasticAgent._initialize_workers"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/elastic/agent/server/api.py#L664"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.agent.server.SimpleElasticAgent._initialize_workers" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">_initialize_workers</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">worker_group</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/agent/server/api.html#SimpleElasticAgent._initialize_workers"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/elastic/agent/server/api.py#L664"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.agent.server.SimpleElasticAgent._initialize_workers" title="Permalink to this definition">¶</a></dt>
 <dd><p>Start a fresh set of workers for the worker_group.</p>
 <p>Essentially, a rendezvous followed by a <code class="docutils literal notranslate"><span class="pre">start_workers</span></code>.
 The caller should first call <code class="docutils literal notranslate"><span class="pre">_stop_workers()</span></code> to stop running workers
@@ -989,7 +989,7 @@ <h2>Extending the Agent<a class="headerlink" href="#extending-the-agent" title="
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.elastic.agent.server.SimpleElasticAgent._monitor_workers">
-<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">_monitor_workers</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">worker_group</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/agent/server/api.html#SimpleElasticAgent._monitor_workers"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/elastic/agent/server/api.py#L471"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.agent.server.SimpleElasticAgent._monitor_workers" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">_monitor_workers</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">worker_group</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/agent/server/api.html#SimpleElasticAgent._monitor_workers"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/elastic/agent/server/api.py#L471"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.agent.server.SimpleElasticAgent._monitor_workers" title="Permalink to this definition">¶</a></dt>
 <dd><p>Check on the workers for the <code class="docutils literal notranslate"><span class="pre">worker_group</span></code>.</p>
 <p>This function also returns the new state of the worker group.</p>
 <dl class="field-list simple">
@@ -1001,7 +1001,7 @@ <h2>Extending the Agent<a class="headerlink" href="#extending-the-agent" title="
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.elastic.agent.server.SimpleElasticAgent._rendezvous">
-<span class="sig-name descname"><span class="pre">_rendezvous</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">worker_group</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/agent/server/api.html#SimpleElasticAgent._rendezvous"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/elastic/agent/server/api.py#L490"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.agent.server.SimpleElasticAgent._rendezvous" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">_rendezvous</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">worker_group</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/agent/server/api.html#SimpleElasticAgent._rendezvous"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/elastic/agent/server/api.py#L490"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.agent.server.SimpleElasticAgent._rendezvous" title="Permalink to this definition">¶</a></dt>
 <dd><p>Run rendezvous for the workers specified by the worker spec.</p>
 <p>Assigns workers a new global rank and world size.
 Updates the rendezvous store for the worker group.</p>
@@ -1011,7 +1011,7 @@ <h2>Extending the Agent<a class="headerlink" href="#extending-the-agent" title="
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.elastic.agent.server.SimpleElasticAgent._restart_workers">
-<span class="sig-name descname"><span class="pre">_restart_workers</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">worker_group</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/agent/server/api.html#SimpleElasticAgent._restart_workers"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/elastic/agent/server/api.py#L695"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.agent.server.SimpleElasticAgent._restart_workers" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">_restart_workers</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">worker_group</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/agent/server/api.html#SimpleElasticAgent._restart_workers"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/elastic/agent/server/api.py#L695"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.agent.server.SimpleElasticAgent._restart_workers" title="Permalink to this definition">¶</a></dt>
 <dd><p>Restart (stops, rendezvous, starts) all local workers in the group.</p>
 <dl class="field-list simple">
 </dl>
@@ -1019,7 +1019,7 @@ <h2>Extending the Agent<a class="headerlink" href="#extending-the-agent" title="
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.elastic.agent.server.SimpleElasticAgent._shutdown">
-<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">_shutdown</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">death_sig</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">Signals.SIGTERM</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">is_restart</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/agent/server/api.html#SimpleElasticAgent._shutdown"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/elastic/agent/server/api.py#L479"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.agent.server.SimpleElasticAgent._shutdown" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">_shutdown</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">death_sig</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">Signals.SIGTERM</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">is_restart</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/agent/server/api.html#SimpleElasticAgent._shutdown"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/elastic/agent/server/api.py#L479"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.agent.server.SimpleElasticAgent._shutdown" title="Permalink to this definition">¶</a></dt>
 <dd><p>Clean up any resources that were allocated during the agent’s work.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -1030,7 +1030,7 @@ <h2>Extending the Agent<a class="headerlink" href="#extending-the-agent" title="
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.elastic.agent.server.SimpleElasticAgent._start_workers">
-<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">_start_workers</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">worker_group</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/agent/server/api.html#SimpleElasticAgent._start_workers"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/elastic/agent/server/api.py#L450"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.agent.server.SimpleElasticAgent._start_workers" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">_start_workers</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">worker_group</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/agent/server/api.html#SimpleElasticAgent._start_workers"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/elastic/agent/server/api.py#L450"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.agent.server.SimpleElasticAgent._start_workers" title="Permalink to this definition">¶</a></dt>
 <dd><p>Start <code class="docutils literal notranslate"><span class="pre">worker_group.spec.local_world_size</span></code> number of workers.</p>
 <p>This is according to worker spec for the worker group .
 Returns a map of <code class="docutils literal notranslate"><span class="pre">local_rank</span></code> to worker <code class="docutils literal notranslate"><span class="pre">id</span></code>.</p>
@@ -1043,7 +1043,7 @@ <h2>Extending the Agent<a class="headerlink" href="#extending-the-agent" title="
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.elastic.agent.server.SimpleElasticAgent._stop_workers">
-<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">_stop_workers</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">worker_group</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">is_restart</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/agent/server/api.html#SimpleElasticAgent._stop_workers"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/elastic/agent/server/api.py#L459"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.agent.server.SimpleElasticAgent._stop_workers" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">_stop_workers</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">worker_group</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">is_restart</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/agent/server/api.html#SimpleElasticAgent._stop_workers"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/elastic/agent/server/api.py#L459"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.agent.server.SimpleElasticAgent._stop_workers" title="Permalink to this definition">¶</a></dt>
 <dd><p>Stop all workers in the given worker group.</p>
 <p>Implementors must deal with workers in all states defined by
 <code class="docutils literal notranslate"><span class="pre">WorkerState</span></code>. That is, it must gracefully handle stopping
@@ -1056,7 +1056,7 @@ <h2>Extending the Agent<a class="headerlink" href="#extending-the-agent" title="
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributed.elastic.agent.server.api.RunResult">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.agent.server.api.</span></span><span class="sig-name descname"><span class="pre">RunResult</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_values=&lt;factory&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">failures=&lt;factory&gt;</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/agent/server/api.html#RunResult"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/elastic/agent/server/api.py#L333"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.agent.server.api.RunResult" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.agent.server.api.</span></span><span class="sig-name descname"><span class="pre">RunResult</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_values=&lt;factory&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">failures=&lt;factory&gt;</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/agent/server/api.html#RunResult"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/elastic/agent/server/api.py#L333"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.agent.server.api.RunResult" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return results of the worker executions.</p>
 <p>Run results follow an “all-or-nothing” policy where the run is successful if and
 only if ALL local workers managed by this agent complete successfully.</p>
@@ -1105,7 +1105,7 @@ <h2>Health Check Server<a class="headerlink" href="#health-check-server" title="
 Additionally, health check server will have callback to check watchdog is alive.</p>
 <span class="target" id="module-torch.distributed.elastic.agent.server.health_check_server"></span><dl class="py class">
 <dt class="sig sig-object py" id="torch.distributed.elastic.agent.server.health_check_server.HealthCheckServer">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.agent.server.health_check_server.</span></span><span class="sig-name descname"><span class="pre">HealthCheckServer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">alive_callback</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">port</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">timeout</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/agent/server/health_check_server.html#HealthCheckServer"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/elastic/agent/server/health_check_server.py#L19"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.agent.server.health_check_server.HealthCheckServer" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.agent.server.health_check_server.</span></span><span class="sig-name descname"><span class="pre">HealthCheckServer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">alive_callback</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">port</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">timeout</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/agent/server/health_check_server.html#HealthCheckServer"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/elastic/agent/server/health_check_server.py#L19"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.agent.server.health_check_server.HealthCheckServer" title="Permalink to this definition">¶</a></dt>
 <dd><p>Interface for health check monitoring server, which can be extended
 by starting tcp/http server on the specified port.</p>
 <dl class="field-list simple">
@@ -1119,7 +1119,7 @@ <h2>Health Check Server<a class="headerlink" href="#health-check-server" title="
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.elastic.agent.server.health_check_server.HealthCheckServer.start">
-<span class="sig-name descname"><span class="pre">start</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/agent/server/health_check_server.html#HealthCheckServer.start"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/elastic/agent/server/health_check_server.py#L44"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.agent.server.health_check_server.HealthCheckServer.start" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">start</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/agent/server/health_check_server.html#HealthCheckServer.start"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/elastic/agent/server/health_check_server.py#L44"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.agent.server.health_check_server.HealthCheckServer.start" title="Permalink to this definition">¶</a></dt>
 <dd><p>Unsupported functionality for Pytorch, doesn’t start any health check server</p>
 <dl class="field-list simple">
 </dl>
@@ -1127,7 +1127,7 @@ <h2>Health Check Server<a class="headerlink" href="#health-check-server" title="
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.elastic.agent.server.health_check_server.HealthCheckServer.stop">
-<span class="sig-name descname"><span class="pre">stop</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/agent/server/health_check_server.html#HealthCheckServer.stop"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/elastic/agent/server/health_check_server.py#L50"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.agent.server.health_check_server.HealthCheckServer.stop" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">stop</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/agent/server/health_check_server.html#HealthCheckServer.stop"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/elastic/agent/server/health_check_server.py#L50"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.agent.server.health_check_server.HealthCheckServer.stop" title="Permalink to this definition">¶</a></dt>
 <dd><p>Function to stop health check server</p>
 <dl class="field-list simple">
 </dl>
@@ -1137,7 +1137,7 @@ <h2>Health Check Server<a class="headerlink" href="#health-check-server" title="
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.distributed.elastic.agent.server.health_check_server.create_healthcheck_server">
-<span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.agent.server.health_check_server.</span></span><span class="sig-name descname"><span class="pre">create_healthcheck_server</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">alive_callback</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">port</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">timeout</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/agent/server/health_check_server.html#create_healthcheck_server"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/elastic/agent/server/health_check_server.py#L57"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.agent.server.health_check_server.create_healthcheck_server" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.agent.server.health_check_server.</span></span><span class="sig-name descname"><span class="pre">create_healthcheck_server</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">alive_callback</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">port</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">timeout</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/agent/server/health_check_server.html#create_healthcheck_server"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/elastic/agent/server/health_check_server.py#L57"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.agent.server.health_check_server.create_healthcheck_server" title="Permalink to this definition">¶</a></dt>
 <dd><p>creates health check server object</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
diff --git a/main/elastic/control_plane.html b/main/elastic/control_plane.html
index bfd837d4f04..5c125a267bd 100644
--- a/main/elastic/control_plane.html
+++ b/main/elastic/control_plane.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 into your application.</p>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.distributed.elastic.control_plane.worker_main">
-<span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.control_plane.</span></span><span class="sig-name descname"><span class="pre">worker_main</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/control_plane.html#worker_main"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/elastic/control_plane.py#L26"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.control_plane.worker_main" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.control_plane.</span></span><span class="sig-name descname"><span class="pre">worker_main</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/control_plane.html#worker_main"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/elastic/control_plane.py#L26"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.control_plane.worker_main" title="Permalink to this definition">¶</a></dt>
 <dd><p>This is a context manager that wraps your main entry function. This combines
 the existing <code class="docutils literal notranslate"><span class="pre">errors.record</span></code> logic as well as a new <code class="docutils literal notranslate"><span class="pre">_WorkerServer</span></code> that
 exposes handlers via a unix socket specified by
diff --git a/main/elastic/customization.html b/main/elastic/customization.html
index ef0d79015bf..c00a5277d2a 100644
--- a/main/elastic/customization.html
+++ b/main/elastic/customization.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/elastic/errors.html b/main/elastic/errors.html
index e6102b1802a..5615bbcd7bd 100644
--- a/main/elastic/errors.html
+++ b/main/elastic/errors.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -661,7 +661,7 @@
 <h2>Methods and Classes<a class="headerlink" href="#methods-and-classes" title="Permalink to this heading">¶</a></h2>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.distributed.elastic.multiprocessing.errors.record">
-<span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.multiprocessing.errors.</span></span><span class="sig-name descname"><span class="pre">record</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">fn</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">error_handler</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/multiprocessing/errors.html#record"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/elastic/multiprocessing/errors/__init__.py#L308"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.multiprocessing.errors.record" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.multiprocessing.errors.</span></span><span class="sig-name descname"><span class="pre">record</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">fn</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">error_handler</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/multiprocessing/errors.html#record"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/elastic/multiprocessing/errors/__init__.py#L308"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.multiprocessing.errors.record" title="Permalink to this definition">¶</a></dt>
 <dd><p>Syntactic sugar to record errors/exceptions that happened in the decorated
 function using the provided <code class="docutils literal notranslate"><span class="pre">error_handler</span></code>.</p>
 <p>Using this decorator is equivalent to:</p>
@@ -701,7 +701,7 @@ <h2>Methods and Classes<a class="headerlink" href="#methods-and-classes" title="
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributed.elastic.multiprocessing.errors.ChildFailedError">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.multiprocessing.errors.</span></span><span class="sig-name descname"><span class="pre">ChildFailedError</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">failures</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/multiprocessing/errors.html#ChildFailedError"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/elastic/multiprocessing/errors/__init__.py#L199"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.multiprocessing.errors.ChildFailedError" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.multiprocessing.errors.</span></span><span class="sig-name descname"><span class="pre">ChildFailedError</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">failures</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/multiprocessing/errors.html#ChildFailedError"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/elastic/multiprocessing/errors/__init__.py#L199"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.multiprocessing.errors.ChildFailedError" title="Permalink to this definition">¶</a></dt>
 <dd><p>Special exception type that can be raised from a function annotated with the
 <code class="docutils literal notranslate"><span class="pre">&#64;record</span></code> decorator to have the child process’ (root exception) propagate
 up the stack as-is (e.g. without being wrapped in the parent’s traceback).</p>
@@ -738,7 +738,7 @@ <h2>Methods and Classes<a class="headerlink" href="#methods-and-classes" title="
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributed.elastic.multiprocessing.errors.ErrorHandler">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.multiprocessing.errors.</span></span><span class="sig-name descname"><span class="pre">ErrorHandler</span></span><a class="reference internal" href="../_modules/torch/distributed/elastic/multiprocessing/errors/error_handler.html#ErrorHandler"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/elastic/multiprocessing/errors/error_handler.py#L24"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.multiprocessing.errors.ErrorHandler" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.multiprocessing.errors.</span></span><span class="sig-name descname"><span class="pre">ErrorHandler</span></span><a class="reference internal" href="../_modules/torch/distributed/elastic/multiprocessing/errors/error_handler.html#ErrorHandler"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/elastic/multiprocessing/errors/error_handler.py#L24"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.multiprocessing.errors.ErrorHandler" title="Permalink to this definition">¶</a></dt>
 <dd><p>Write the provided exception object along with some other metadata about
 the error in a structured way in JSON format to an error file specified by the
 environment variable: <code class="docutils literal notranslate"><span class="pre">TORCHELASTIC_ERROR_FILE</span></code>. If this environment
@@ -750,7 +750,7 @@ <h2>Methods and Classes<a class="headerlink" href="#methods-and-classes" title="
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributed.elastic.multiprocessing.errors.ProcessFailure">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.multiprocessing.errors.</span></span><span class="sig-name descname"><span class="pre">ProcessFailure</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">local_rank</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pid</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">exitcode</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">error_file</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/multiprocessing/errors.html#ProcessFailure"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/elastic/multiprocessing/errors/__init__.py#L89"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.multiprocessing.errors.ProcessFailure" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.multiprocessing.errors.</span></span><span class="sig-name descname"><span class="pre">ProcessFailure</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">local_rank</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pid</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">exitcode</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">error_file</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/multiprocessing/errors.html#ProcessFailure"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/elastic/multiprocessing/errors/__init__.py#L89"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.multiprocessing.errors.ProcessFailure" title="Permalink to this definition">¶</a></dt>
 <dd><p>Represent the failed process result. When the worker process fails, it may record failure root cause into the file.</p>
 <p>Tries to read the failure timestamp from the provided <code class="docutils literal notranslate"><span class="pre">error_file</span></code>,
 if the <code class="docutils literal notranslate"><span class="pre">error_file</span></code> does not exist, the timestamp is the current
diff --git a/main/elastic/events.html b/main/elastic/events.html
index 3b0a6434786..7fd0560a5e5 100644
--- a/main/elastic/events.html
+++ b/main/elastic/events.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -621,14 +621,14 @@
 <h2>API Methods<a class="headerlink" href="#api-methods" title="Permalink to this heading">¶</a></h2>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.distributed.elastic.events.record">
-<span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.events.</span></span><span class="sig-name descname"><span class="pre">record</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">event</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">destination</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'null'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/events.html#record"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/elastic/events/__init__.py#L72"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.events.record" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.events.</span></span><span class="sig-name descname"><span class="pre">record</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">event</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">destination</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'null'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/events.html#record"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/elastic/events/__init__.py#L72"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.events.record" title="Permalink to this definition">¶</a></dt>
 <dd><dl class="field-list simple">
 </dl>
 </dd></dl>
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.distributed.elastic.events.construct_and_record_rdzv_event">
-<span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.events.</span></span><span class="sig-name descname"><span class="pre">construct_and_record_rdzv_event</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">run_id</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">message</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">node_state</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">hostname</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pid</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">master_endpoint</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">local_id</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">rank</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/events.html#construct_and_record_rdzv_event"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/elastic/events/__init__.py#L80"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.events.construct_and_record_rdzv_event" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.events.</span></span><span class="sig-name descname"><span class="pre">construct_and_record_rdzv_event</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">run_id</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">message</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">node_state</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">hostname</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pid</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">master_endpoint</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">local_id</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">rank</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/events.html#construct_and_record_rdzv_event"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/elastic/events/__init__.py#L80"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.events.construct_and_record_rdzv_event" title="Permalink to this definition">¶</a></dt>
 <dd><p>Initialize rendezvous event object and record its operations.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -675,7 +675,7 @@ <h2>API Methods<a class="headerlink" href="#api-methods" title="Permalink to thi
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.distributed.elastic.events.get_logging_handler">
-<span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.events.</span></span><span class="sig-name descname"><span class="pre">get_logging_handler</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">destination</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'null'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/events/handlers.html#get_logging_handler"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/elastic/events/handlers.py#L19"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.events.get_logging_handler" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.events.</span></span><span class="sig-name descname"><span class="pre">get_logging_handler</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">destination</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'null'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/events/handlers.html#get_logging_handler"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/elastic/events/handlers.py#L19"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.events.get_logging_handler" title="Permalink to this definition">¶</a></dt>
 <dd><dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
 <dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/logging.html#logging.Handler" title="(in Python v3.13)"><em>Handler</em></a></p>
@@ -688,7 +688,7 @@ <h2>API Methods<a class="headerlink" href="#api-methods" title="Permalink to thi
 <h2>Event Objects<a class="headerlink" href="#event-objects" title="Permalink to this heading">¶</a></h2>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributed.elastic.events.api.Event">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.events.api.</span></span><span class="sig-name descname"><span class="pre">Event</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">source</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">timestamp=0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">metadata=&lt;factory&gt;</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/events/api.html#Event"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/elastic/events/api.py#L28"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.events.api.Event" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.events.api.</span></span><span class="sig-name descname"><span class="pre">Event</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">source</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">timestamp=0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">metadata=&lt;factory&gt;</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/events/api.html#Event"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/elastic/events/api.py#L28"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.events.api.Event" title="Permalink to this definition">¶</a></dt>
 <dd><p>The class represents the generic event that occurs during the torchelastic job execution.</p>
 <p>The event can be any kind of meaningful action.</p>
 <dl class="field-list simple">
@@ -705,7 +705,7 @@ <h2>Event Objects<a class="headerlink" href="#event-objects" title="Permalink to
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributed.elastic.events.api.EventSource">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.events.api.</span></span><span class="sig-name descname"><span class="pre">EventSource</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/events/api.html#EventSource"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/elastic/events/api.py#L21"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.events.api.EventSource" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.events.api.</span></span><span class="sig-name descname"><span class="pre">EventSource</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/events/api.html#EventSource"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/elastic/events/api.py#L21"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.events.api.EventSource" title="Permalink to this definition">¶</a></dt>
 <dd><p>Known identifiers of the event producers.</p>
 </dd></dl>
 
diff --git a/main/elastic/examples.html b/main/elastic/examples.html
index 77a1e59baba..0232e6192b4 100644
--- a/main/elastic/examples.html
+++ b/main/elastic/examples.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/elastic/kubernetes.html b/main/elastic/kubernetes.html
index 99df6864687..3f2989c228e 100644
--- a/main/elastic/kubernetes.html
+++ b/main/elastic/kubernetes.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/elastic/metrics.html b/main/elastic/metrics.html
index d475751ba9b..a3c7844e692 100644
--- a/main/elastic/metrics.html
+++ b/main/elastic/metrics.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -721,17 +721,17 @@ <h2>Metric Handlers<a class="headerlink" href="#metric-handlers" title="Permalin
 <p>Below are the metric handlers that come included with torchelastic.</p>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributed.elastic.metrics.api.MetricHandler">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.metrics.api.</span></span><span class="sig-name descname"><span class="pre">MetricHandler</span></span><a class="reference internal" href="../_modules/torch/distributed/elastic/metrics/api.html#MetricHandler"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/elastic/metrics/api.py#L46"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.metrics.api.MetricHandler" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.metrics.api.</span></span><span class="sig-name descname"><span class="pre">MetricHandler</span></span><a class="reference internal" href="../_modules/torch/distributed/elastic/metrics/api.html#MetricHandler"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/elastic/metrics/api.py#L46"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.metrics.api.MetricHandler" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributed.elastic.metrics.api.ConsoleMetricHandler">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.metrics.api.</span></span><span class="sig-name descname"><span class="pre">ConsoleMetricHandler</span></span><a class="reference internal" href="../_modules/torch/distributed/elastic/metrics/api.html#ConsoleMetricHandler"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/elastic/metrics/api.py#L52"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.metrics.api.ConsoleMetricHandler" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.metrics.api.</span></span><span class="sig-name descname"><span class="pre">ConsoleMetricHandler</span></span><a class="reference internal" href="../_modules/torch/distributed/elastic/metrics/api.html#ConsoleMetricHandler"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/elastic/metrics/api.py#L52"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.metrics.api.ConsoleMetricHandler" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributed.elastic.metrics.api.NullMetricHandler">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.metrics.api.</span></span><span class="sig-name descname"><span class="pre">NullMetricHandler</span></span><a class="reference internal" href="../_modules/torch/distributed/elastic/metrics/api.html#NullMetricHandler"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/elastic/metrics/api.py#L59"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.metrics.api.NullMetricHandler" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.metrics.api.</span></span><span class="sig-name descname"><span class="pre">NullMetricHandler</span></span><a class="reference internal" href="../_modules/torch/distributed/elastic/metrics/api.html#NullMetricHandler"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/elastic/metrics/api.py#L59"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.metrics.api.NullMetricHandler" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 </div>
@@ -739,14 +739,14 @@ <h2>Metric Handlers<a class="headerlink" href="#metric-handlers" title="Permalin
 <h2>Methods<a class="headerlink" href="#methods" title="Permalink to this heading">¶</a></h2>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.distributed.elastic.metrics.configure">
-<span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.metrics.</span></span><span class="sig-name descname"><span class="pre">configure</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">handler</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/metrics/api.html#configure"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/elastic/metrics/api.py#L80"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.metrics.configure" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.metrics.</span></span><span class="sig-name descname"><span class="pre">configure</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">handler</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/metrics/api.html#configure"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/elastic/metrics/api.py#L80"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.metrics.configure" title="Permalink to this definition">¶</a></dt>
 <dd><dl class="field-list simple">
 </dl>
 </dd></dl>
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.distributed.elastic.metrics.prof">
-<span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.metrics.</span></span><span class="sig-name descname"><span class="pre">prof</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">fn</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'torchelastic'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/metrics/api.html#prof"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/elastic/metrics/api.py#L111"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.metrics.prof" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.metrics.</span></span><span class="sig-name descname"><span class="pre">prof</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">fn</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'torchelastic'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/metrics/api.html#prof"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/elastic/metrics/api.py#L111"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.metrics.prof" title="Permalink to this definition">¶</a></dt>
 <dd><p>&#64;profile decorator publishes duration.ms, count, success, failure metrics for the function that it decorates.</p>
 <p>The metric name defaults to the qualified name (<code class="docutils literal notranslate"><span class="pre">class_name.def_name</span></code>) of the function.
 If the function does not belong to a class, it uses the leaf module name instead.</p>
@@ -766,7 +766,7 @@ <h2>Methods<a class="headerlink" href="#methods" title="Permalink to this headin
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.distributed.elastic.metrics.put_metric">
-<span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.metrics.</span></span><span class="sig-name descname"><span class="pre">put_metric</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">metric_name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">metric_value</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">metric_group</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'torchelastic'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/metrics/api.html#put_metric"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/elastic/metrics/api.py#L190"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.metrics.put_metric" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.metrics.</span></span><span class="sig-name descname"><span class="pre">put_metric</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">metric_name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">metric_value</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">metric_group</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'torchelastic'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/metrics/api.html#put_metric"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/elastic/metrics/api.py#L190"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.metrics.put_metric" title="Permalink to this definition">¶</a></dt>
 <dd><p>Publish a metric data point.</p>
 <p>Usage</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">put_metric</span><span class="p">(</span><span class="s2">&quot;metric_name&quot;</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
diff --git a/main/elastic/multiprocessing.html b/main/elastic/multiprocessing.html
index fda620f8d34..f740f94ead7 100644
--- a/main/elastic/multiprocessing.html
+++ b/main/elastic/multiprocessing.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -665,7 +665,7 @@
 <h2>Starting Multiple Workers<a class="headerlink" href="#starting-multiple-workers" title="Permalink to this heading">¶</a></h2>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.distributed.elastic.multiprocessing.start_processes">
-<span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.multiprocessing.</span></span><span class="sig-name descname"><span class="pre">start_processes</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">entrypoint</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">envs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">logs_specs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">log_line_prefixes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">start_method</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'spawn'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/multiprocessing.html#start_processes"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/elastic/multiprocessing/__init__.py#L100"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.multiprocessing.start_processes" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.multiprocessing.</span></span><span class="sig-name descname"><span class="pre">start_processes</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">entrypoint</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">envs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">logs_specs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">log_line_prefixes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">start_method</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'spawn'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/multiprocessing.html#start_processes"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/elastic/multiprocessing/__init__.py#L100"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.multiprocessing.start_processes" title="Permalink to this definition">¶</a></dt>
 <dd><p>Start <code class="docutils literal notranslate"><span class="pre">n</span></code> copies of <code class="docutils literal notranslate"><span class="pre">entrypoint</span></code> processes with the provided options.</p>
 <p><code class="docutils literal notranslate"><span class="pre">entrypoint</span></code> is either a <code class="docutils literal notranslate"><span class="pre">Callable</span></code> (function) or a <code class="docutils literal notranslate"><span class="pre">str</span></code> (binary).
 The number of copies is determined by the number of entries for <code class="docutils literal notranslate"><span class="pre">args</span></code> and
@@ -769,7 +769,7 @@ <h2>Starting Multiple Workers<a class="headerlink" href="#starting-multiple-work
 <h2>Process Context<a class="headerlink" href="#process-context" title="Permalink to this heading">¶</a></h2>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributed.elastic.multiprocessing.api.PContext">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.multiprocessing.api.</span></span><span class="sig-name descname"><span class="pre">PContext</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">entrypoint</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">envs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">logs_specs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">log_line_prefixes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/multiprocessing/api.html#PContext"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/elastic/multiprocessing/api.py#L426"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.multiprocessing.api.PContext" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.multiprocessing.api.</span></span><span class="sig-name descname"><span class="pre">PContext</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">entrypoint</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">envs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">logs_specs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">log_line_prefixes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/multiprocessing/api.html#PContext"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/elastic/multiprocessing/api.py#L426"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.multiprocessing.api.PContext" title="Permalink to this definition">¶</a></dt>
 <dd><p>The base class that standardizes operations over a set of processes that are launched via different mechanisms.</p>
 <p>The name <code class="docutils literal notranslate"><span class="pre">PContext</span></code> is intentional to disambiguate with <code class="docutils literal notranslate"><span class="pre">torch.multiprocessing.ProcessContext</span></code>.</p>
 <div class="admonition warning">
@@ -784,7 +784,7 @@ <h2>Process Context<a class="headerlink" href="#process-context" title="Permalin
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributed.elastic.multiprocessing.api.MultiprocessContext">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.multiprocessing.api.</span></span><span class="sig-name descname"><span class="pre">MultiprocessContext</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">entrypoint</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">envs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">start_method</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">logs_specs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">log_line_prefixes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/multiprocessing/api.html#MultiprocessContext"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/elastic/multiprocessing/api.py#L617"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.multiprocessing.api.MultiprocessContext" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.multiprocessing.api.</span></span><span class="sig-name descname"><span class="pre">MultiprocessContext</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">entrypoint</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">envs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">start_method</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">logs_specs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">log_line_prefixes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/multiprocessing/api.html#MultiprocessContext"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/elastic/multiprocessing/api.py#L617"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.multiprocessing.api.MultiprocessContext" title="Permalink to this definition">¶</a></dt>
 <dd><p><code class="docutils literal notranslate"><span class="pre">PContext</span></code> holding worker processes invoked as a function.</p>
 <dl class="field-list simple">
 </dl>
@@ -792,7 +792,7 @@ <h2>Process Context<a class="headerlink" href="#process-context" title="Permalin
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributed.elastic.multiprocessing.api.SubprocessContext">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.multiprocessing.api.</span></span><span class="sig-name descname"><span class="pre">SubprocessContext</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">entrypoint</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">envs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">logs_specs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">log_line_prefixes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/multiprocessing/api.html#SubprocessContext"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/elastic/multiprocessing/api.py#L799"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.multiprocessing.api.SubprocessContext" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.multiprocessing.api.</span></span><span class="sig-name descname"><span class="pre">SubprocessContext</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">entrypoint</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">envs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">logs_specs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">log_line_prefixes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/multiprocessing/api.html#SubprocessContext"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/elastic/multiprocessing/api.py#L799"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.multiprocessing.api.SubprocessContext" title="Permalink to this definition">¶</a></dt>
 <dd><p><code class="docutils literal notranslate"><span class="pre">PContext</span></code> holding worker processes invoked as a binary.</p>
 <dl class="field-list simple">
 </dl>
@@ -800,7 +800,7 @@ <h2>Process Context<a class="headerlink" href="#process-context" title="Permalin
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributed.elastic.multiprocessing.api.RunProcsResult">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.multiprocessing.api.</span></span><span class="sig-name descname"><span class="pre">RunProcsResult</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">return_values=&lt;factory&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">failures=&lt;factory&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stdouts=&lt;factory&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stderrs=&lt;factory&gt;</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/multiprocessing/api.html#RunProcsResult"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/elastic/multiprocessing/api.py#L403"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.multiprocessing.api.RunProcsResult" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.multiprocessing.api.</span></span><span class="sig-name descname"><span class="pre">RunProcsResult</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">return_values=&lt;factory&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">failures=&lt;factory&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stdouts=&lt;factory&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stderrs=&lt;factory&gt;</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/multiprocessing/api.html#RunProcsResult"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/elastic/multiprocessing/api.py#L403"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.multiprocessing.api.RunProcsResult" title="Permalink to this definition">¶</a></dt>
 <dd><p>Results of a completed run of processes started with <code class="docutils literal notranslate"><span class="pre">start_processes()</span></code>. Returned by <code class="docutils literal notranslate"><span class="pre">PContext</span></code>.</p>
 <p>Note the following:</p>
 <ol class="arabic simple">
@@ -815,7 +815,7 @@ <h2>Process Context<a class="headerlink" href="#process-context" title="Permalin
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributed.elastic.multiprocessing.api.DefaultLogsSpecs">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.multiprocessing.api.</span></span><span class="sig-name descname"><span class="pre">DefaultLogsSpecs</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">log_dir</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">redirects</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">Std.NONE</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tee</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">Std.NONE</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">local_ranks_filter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/multiprocessing/api.html#DefaultLogsSpecs"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/elastic/multiprocessing/api.py#L241"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.multiprocessing.api.DefaultLogsSpecs" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.multiprocessing.api.</span></span><span class="sig-name descname"><span class="pre">DefaultLogsSpecs</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">log_dir</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">redirects</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">Std.NONE</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tee</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">Std.NONE</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">local_ranks_filter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/multiprocessing/api.html#DefaultLogsSpecs"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/elastic/multiprocessing/api.py#L241"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.multiprocessing.api.DefaultLogsSpecs" title="Permalink to this definition">¶</a></dt>
 <dd><p>Default LogsSpecs implementation:</p>
 <ul class="simple">
 <li><p><cite>log_dir</cite> will be created if it doesn’t exist</p></li>
@@ -825,7 +825,7 @@ <h2>Process Context<a class="headerlink" href="#process-context" title="Permalin
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.elastic.multiprocessing.api.DefaultLogsSpecs.reify">
-<span class="sig-name descname"><span class="pre">reify</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">envs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/multiprocessing/api.html#DefaultLogsSpecs.reify"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/elastic/multiprocessing/api.py#L279"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.multiprocessing.api.DefaultLogsSpecs.reify" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">reify</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">envs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/multiprocessing/api.html#DefaultLogsSpecs.reify"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/elastic/multiprocessing/api.py#L279"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.multiprocessing.api.DefaultLogsSpecs.reify" title="Permalink to this definition">¶</a></dt>
 <dd><p>Uses following scheme to build log destination paths:</p>
 <ul class="simple">
 <li><p><cite>&lt;log_dir&gt;/&lt;rdzv_run_id&gt;/attempt_&lt;attempt&gt;/&lt;rank&gt;/stdout.log</cite></p></li>
@@ -843,7 +843,7 @@ <h2>Process Context<a class="headerlink" href="#process-context" title="Permalin
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributed.elastic.multiprocessing.api.LogsDest">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.multiprocessing.api.</span></span><span class="sig-name descname"><span class="pre">LogsDest</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">stdouts=&lt;factory&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stderrs=&lt;factory&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tee_stdouts=&lt;factory&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tee_stderrs=&lt;factory&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">error_files=&lt;factory&gt;</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/multiprocessing/api.html#LogsDest"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/elastic/multiprocessing/api.py#L181"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.multiprocessing.api.LogsDest" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.multiprocessing.api.</span></span><span class="sig-name descname"><span class="pre">LogsDest</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">stdouts=&lt;factory&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stderrs=&lt;factory&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tee_stdouts=&lt;factory&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tee_stderrs=&lt;factory&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">error_files=&lt;factory&gt;</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/multiprocessing/api.html#LogsDest"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/elastic/multiprocessing/api.py#L181"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.multiprocessing.api.LogsDest" title="Permalink to this definition">¶</a></dt>
 <dd><p>For each log type, holds mapping of local rank ids to file paths.</p>
 <dl class="field-list simple">
 </dl>
@@ -851,7 +851,7 @@ <h2>Process Context<a class="headerlink" href="#process-context" title="Permalin
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributed.elastic.multiprocessing.api.LogsSpecs">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.multiprocessing.api.</span></span><span class="sig-name descname"><span class="pre">LogsSpecs</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">log_dir</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">redirects</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">Std.NONE</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tee</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">Std.NONE</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">local_ranks_filter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/multiprocessing/api.html#LogsSpecs"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/elastic/multiprocessing/api.py#L194"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.multiprocessing.api.LogsSpecs" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.multiprocessing.api.</span></span><span class="sig-name descname"><span class="pre">LogsSpecs</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">log_dir</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">redirects</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">Std.NONE</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tee</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">Std.NONE</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">local_ranks_filter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/multiprocessing/api.html#LogsSpecs"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/elastic/multiprocessing/api.py#L194"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.multiprocessing.api.LogsSpecs" title="Permalink to this definition">¶</a></dt>
 <dd><p>Defines logs processing and redirection for each worker process.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -868,7 +868,7 @@ <h2>Process Context<a class="headerlink" href="#process-context" title="Permalin
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.elastic.multiprocessing.api.LogsSpecs.reify">
-<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">reify</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">envs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/multiprocessing/api.html#LogsSpecs.reify"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/elastic/multiprocessing/api.py#L223"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.multiprocessing.api.LogsSpecs.reify" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">reify</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">envs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/multiprocessing/api.html#LogsSpecs.reify"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/elastic/multiprocessing/api.py#L223"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.multiprocessing.api.LogsSpecs.reify" title="Permalink to this definition">¶</a></dt>
 <dd><p>Given the environment variables, builds destination of log files for each of the local ranks.</p>
 <p>Envs parameter contains env variables dict for each of the local ranks, where entries are defined in:
 <code class="xref py py-func docutils literal notranslate"><span class="pre">_start_workers()</span></code>.</p>
diff --git a/main/elastic/quickstart.html b/main/elastic/quickstart.html
index 8c37311734e..a29a5127659 100644
--- a/main/elastic/quickstart.html
+++ b/main/elastic/quickstart.html
@@ -300,7 +300,7 @@
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     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/elastic/rendezvous.html b/main/elastic/rendezvous.html
index e172d25c38f..84177c8849b 100644
--- a/main/elastic/rendezvous.html
+++ b/main/elastic/rendezvous.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -713,7 +713,7 @@
 <h2>Registry<a class="headerlink" href="#registry" title="Permalink to this heading">¶</a></h2>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributed.elastic.rendezvous.RendezvousParameters">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.rendezvous.</span></span><span class="sig-name descname"><span class="pre">RendezvousParameters</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">backend</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">endpoint</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">run_id</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_nodes</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_nodes</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">local_addr</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/rendezvous/api.html#RendezvousParameters"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/elastic/rendezvous/api.py#L234"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.rendezvous.RendezvousParameters" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.rendezvous.</span></span><span class="sig-name descname"><span class="pre">RendezvousParameters</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">backend</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">endpoint</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">run_id</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_nodes</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_nodes</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">local_addr</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/rendezvous/api.html#RendezvousParameters"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/elastic/rendezvous/api.py#L234"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.rendezvous.RendezvousParameters" title="Permalink to this definition">¶</a></dt>
 <dd><p>Hold the parameters to construct a <a class="reference internal" href="#torch.distributed.elastic.rendezvous.RendezvousHandler" title="torch.distributed.elastic.rendezvous.RendezvousHandler"><code class="xref py py-class docutils literal notranslate"><span class="pre">RendezvousHandler</span></code></a>.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -730,7 +730,7 @@ <h2>Registry<a class="headerlink" href="#registry" title="Permalink to this head
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.elastic.rendezvous.RendezvousParameters.get">
-<span class="sig-name descname"><span class="pre">get</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">key</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">default</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/rendezvous/api.html#RendezvousParameters.get"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/elastic/rendezvous/api.py#L285"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.rendezvous.RendezvousParameters.get" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">get</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">key</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">default</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/rendezvous/api.html#RendezvousParameters.get"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/elastic/rendezvous/api.py#L285"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.rendezvous.RendezvousParameters.get" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the value for <code class="docutils literal notranslate"><span class="pre">key</span></code> if <code class="docutils literal notranslate"><span class="pre">key</span></code> exists, else <code class="docutils literal notranslate"><span class="pre">default</span></code>.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -741,7 +741,7 @@ <h2>Registry<a class="headerlink" href="#registry" title="Permalink to this head
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.elastic.rendezvous.RendezvousParameters.get_as_bool">
-<span class="sig-name descname"><span class="pre">get_as_bool</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">key</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">default</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/rendezvous/api.html#RendezvousParameters.get_as_bool"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/elastic/rendezvous/api.py#L289"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.rendezvous.RendezvousParameters.get_as_bool" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">get_as_bool</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">key</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">default</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/rendezvous/api.html#RendezvousParameters.get_as_bool"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/elastic/rendezvous/api.py#L289"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.rendezvous.RendezvousParameters.get_as_bool" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the value for <code class="docutils literal notranslate"><span class="pre">key</span></code> as a <code class="docutils literal notranslate"><span class="pre">bool</span></code>.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -752,7 +752,7 @@ <h2>Registry<a class="headerlink" href="#registry" title="Permalink to this head
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.elastic.rendezvous.RendezvousParameters.get_as_int">
-<span class="sig-name descname"><span class="pre">get_as_int</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">key</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">default</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/rendezvous/api.html#RendezvousParameters.get_as_int"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/elastic/rendezvous/api.py#L308"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.rendezvous.RendezvousParameters.get_as_int" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">get_as_int</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">key</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">default</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/rendezvous/api.html#RendezvousParameters.get_as_int"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/elastic/rendezvous/api.py#L308"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.rendezvous.RendezvousParameters.get_as_int" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the value for <code class="docutils literal notranslate"><span class="pre">key</span></code> as an <code class="docutils literal notranslate"><span class="pre">int</span></code>.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -765,7 +765,7 @@ <h2>Registry<a class="headerlink" href="#registry" title="Permalink to this head
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributed.elastic.rendezvous.RendezvousHandlerRegistry">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.rendezvous.</span></span><span class="sig-name descname"><span class="pre">RendezvousHandlerRegistry</span></span><a class="reference internal" href="../_modules/torch/distributed/elastic/rendezvous/api.html#RendezvousHandlerRegistry"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/elastic/rendezvous/api.py#L325"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.rendezvous.RendezvousHandlerRegistry" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.rendezvous.</span></span><span class="sig-name descname"><span class="pre">RendezvousHandlerRegistry</span></span><a class="reference internal" href="../_modules/torch/distributed/elastic/rendezvous/api.html#RendezvousHandlerRegistry"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/elastic/rendezvous/api.py#L325"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.rendezvous.RendezvousHandlerRegistry" title="Permalink to this definition">¶</a></dt>
 <dd><p>Represent a registry of <a class="reference internal" href="#torch.distributed.elastic.rendezvous.RendezvousHandler" title="torch.distributed.elastic.rendezvous.RendezvousHandler"><code class="xref py py-class docutils literal notranslate"><span class="pre">RendezvousHandler</span></code></a> backends.</p>
 <dl class="field-list simple">
 </dl>
@@ -776,7 +776,7 @@ <h2>Registry<a class="headerlink" href="#registry" title="Permalink to this head
 <h2>Handler<a class="headerlink" href="#handler" title="Permalink to this heading">¶</a></h2>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributed.elastic.rendezvous.RendezvousHandler">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.rendezvous.</span></span><span class="sig-name descname"><span class="pre">RendezvousHandler</span></span><a class="reference internal" href="../_modules/torch/distributed/elastic/rendezvous/api.html#RendezvousHandler"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/elastic/rendezvous/api.py#L138"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.rendezvous.RendezvousHandler" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.rendezvous.</span></span><span class="sig-name descname"><span class="pre">RendezvousHandler</span></span><a class="reference internal" href="../_modules/torch/distributed/elastic/rendezvous/api.html#RendezvousHandler"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/elastic/rendezvous/api.py#L138"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.rendezvous.RendezvousHandler" title="Permalink to this definition">¶</a></dt>
 <dd><p>Main rendezvous interface.</p>
 <div class="admonition note">
 <p class="admonition-title">Note</p>
@@ -786,7 +786,7 @@ <h2>Handler<a class="headerlink" href="#handler" title="Permalink to this headin
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.elastic.rendezvous.RendezvousHandler.get_backend">
-<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">get_backend</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/rendezvous/api.html#RendezvousHandler.get_backend"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/elastic/rendezvous/api.py#L147"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.rendezvous.RendezvousHandler.get_backend" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">get_backend</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/rendezvous/api.html#RendezvousHandler.get_backend"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/elastic/rendezvous/api.py#L147"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.rendezvous.RendezvousHandler.get_backend" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the name of the rendezvous backend.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -797,7 +797,7 @@ <h2>Handler<a class="headerlink" href="#handler" title="Permalink to this headin
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.elastic.rendezvous.RendezvousHandler.get_run_id">
-<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">get_run_id</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/rendezvous/api.html#RendezvousHandler.get_run_id"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/elastic/rendezvous/api.py#L211"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.rendezvous.RendezvousHandler.get_run_id" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">get_run_id</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/rendezvous/api.html#RendezvousHandler.get_run_id"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/elastic/rendezvous/api.py#L211"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.rendezvous.RendezvousHandler.get_run_id" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the run id of the rendezvous.</p>
 <p>The run id is a user-defined id that uniquely identifies an instance of
 a distributed application. It typically maps to a job id and is used to
@@ -811,7 +811,7 @@ <h2>Handler<a class="headerlink" href="#handler" title="Permalink to this headin
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.elastic.rendezvous.RendezvousHandler.is_closed">
-<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">is_closed</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/rendezvous/api.html#RendezvousHandler.is_closed"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/elastic/rendezvous/api.py#L183"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.rendezvous.RendezvousHandler.is_closed" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">is_closed</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/rendezvous/api.html#RendezvousHandler.is_closed"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/elastic/rendezvous/api.py#L183"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.rendezvous.RendezvousHandler.is_closed" title="Permalink to this definition">¶</a></dt>
 <dd><p>Check whether the rendezvous has been closed.</p>
 <p>A closed rendezvous means all future attempts to re-rendezvous within
 same job will fail.</p>
@@ -829,7 +829,7 @@ <h2>Handler<a class="headerlink" href="#handler" title="Permalink to this headin
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.elastic.rendezvous.RendezvousHandler.next_rendezvous">
-<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">next_rendezvous</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/rendezvous/api.html#RendezvousHandler.next_rendezvous"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/elastic/rendezvous/api.py#L161"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.rendezvous.RendezvousHandler.next_rendezvous" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">next_rendezvous</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/rendezvous/api.html#RendezvousHandler.next_rendezvous"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/elastic/rendezvous/api.py#L161"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.rendezvous.RendezvousHandler.next_rendezvous" title="Permalink to this definition">¶</a></dt>
 <dd><p>Main entry-point into the rendezvous barrier.</p>
 <p>Blocks until the rendezvous is complete and the current process is
 included in the formed worker group, or a timeout occurs, or the
@@ -854,7 +854,7 @@ <h2>Handler<a class="headerlink" href="#handler" title="Permalink to this headin
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.elastic.rendezvous.RendezvousHandler.num_nodes_waiting">
-<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">num_nodes_waiting</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/rendezvous/api.html#RendezvousHandler.num_nodes_waiting"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/elastic/rendezvous/api.py#L201"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.rendezvous.RendezvousHandler.num_nodes_waiting" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">num_nodes_waiting</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/rendezvous/api.html#RendezvousHandler.num_nodes_waiting"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/elastic/rendezvous/api.py#L201"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.rendezvous.RendezvousHandler.num_nodes_waiting" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the number of nodes who arrived late at the rendezvous
 barrier, hence were not included in the current worker group.</p>
 <p>Callers should periodically call this method to check whether new
@@ -869,13 +869,13 @@ <h2>Handler<a class="headerlink" href="#handler" title="Permalink to this headin
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.elastic.rendezvous.RendezvousHandler.set_closed">
-<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">set_closed</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/rendezvous/api.html#RendezvousHandler.set_closed"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/elastic/rendezvous/api.py#L197"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.rendezvous.RendezvousHandler.set_closed" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">set_closed</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/rendezvous/api.html#RendezvousHandler.set_closed"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/elastic/rendezvous/api.py#L197"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.rendezvous.RendezvousHandler.set_closed" title="Permalink to this definition">¶</a></dt>
 <dd><p>Mark the rendezvous as closed.</p>
 </dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.elastic.rendezvous.RendezvousHandler.shutdown">
-<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">shutdown</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/rendezvous/api.html#RendezvousHandler.shutdown"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/elastic/rendezvous/api.py#L220"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.rendezvous.RendezvousHandler.shutdown" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">shutdown</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/rendezvous/api.html#RendezvousHandler.shutdown"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/elastic/rendezvous/api.py#L220"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.rendezvous.RendezvousHandler.shutdown" title="Permalink to this definition">¶</a></dt>
 <dd><p>Close all resources that were open for the rendezvous.</p>
 <p>Example:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">rdzv_handler</span> <span class="o">=</span> <span class="o">...</span>
@@ -908,7 +908,7 @@ <h2>Handler<a class="headerlink" href="#handler" title="Permalink to this headin
 <h2>Dataclasses<a class="headerlink" href="#dataclasses" title="Permalink to this heading">¶</a></h2>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributed.elastic.rendezvous.RendezvousInfo">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.rendezvous.</span></span><span class="sig-name descname"><span class="pre">RendezvousInfo</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">store</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">rank</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">world_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bootstrap_store_info</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/rendezvous/api.html#RendezvousInfo"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/elastic/rendezvous/api.py#L102"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.rendezvous.RendezvousInfo" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.rendezvous.</span></span><span class="sig-name descname"><span class="pre">RendezvousInfo</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">store</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">rank</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">world_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bootstrap_store_info</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/rendezvous/api.html#RendezvousInfo"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/elastic/rendezvous/api.py#L102"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.rendezvous.RendezvousInfo" title="Permalink to this definition">¶</a></dt>
 <dd><p>Holds the information about the rendezvous.</p>
 <dl class="field-list simple">
 </dl>
@@ -916,13 +916,13 @@ <h2>Dataclasses<a class="headerlink" href="#dataclasses" title="Permalink to thi
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributed.elastic.rendezvous.api.RendezvousStoreInfo">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.rendezvous.api.</span></span><span class="sig-name descname"><span class="pre">RendezvousStoreInfo</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">master_addr</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">master_port</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/rendezvous/api.html#RendezvousStoreInfo"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/elastic/rendezvous/api.py#L61"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.rendezvous.api.RendezvousStoreInfo" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.rendezvous.api.</span></span><span class="sig-name descname"><span class="pre">RendezvousStoreInfo</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">master_addr</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">master_port</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/rendezvous/api.html#RendezvousStoreInfo"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/elastic/rendezvous/api.py#L61"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.rendezvous.api.RendezvousStoreInfo" title="Permalink to this definition">¶</a></dt>
 <dd><p>Store address and port that can be used to bootstrap trainer distributed comms</p>
 <dl class="field-list simple">
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.elastic.rendezvous.api.RendezvousStoreInfo.build">
-<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">build</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">rank</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">store</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/rendezvous/api.html#RendezvousStoreInfo.build"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/elastic/rendezvous/api.py#L70"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.rendezvous.api.RendezvousStoreInfo.build" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">build</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">rank</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">store</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/rendezvous/api.html#RendezvousStoreInfo.build"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/elastic/rendezvous/api.py#L70"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.rendezvous.api.RendezvousStoreInfo.build" title="Permalink to this definition">¶</a></dt>
 <dd><p>Factory method, finds unused new port on rank0 host and addr/port info with all ranks.</p>
 <p>If master_addr/master_port is knowns (useful when sharing existing tcp store server) use the constructor.</p>
 <dl class="field-list simple">
@@ -947,37 +947,37 @@ <h2>Dataclasses<a class="headerlink" href="#dataclasses" title="Permalink to thi
 <h2>Exceptions<a class="headerlink" href="#exceptions" title="Permalink to this heading">¶</a></h2>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributed.elastic.rendezvous.api.RendezvousError">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.rendezvous.api.</span></span><span class="sig-name descname"><span class="pre">RendezvousError</span></span><a class="reference internal" href="../_modules/torch/distributed/elastic/rendezvous/api.html#RendezvousError"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/elastic/rendezvous/api.py#L34"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.rendezvous.api.RendezvousError" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.rendezvous.api.</span></span><span class="sig-name descname"><span class="pre">RendezvousError</span></span><a class="reference internal" href="../_modules/torch/distributed/elastic/rendezvous/api.html#RendezvousError"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/elastic/rendezvous/api.py#L34"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.rendezvous.api.RendezvousError" title="Permalink to this definition">¶</a></dt>
 <dd><p>Represents the base type for rendezvous errors.</p>
 </dd></dl>
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributed.elastic.rendezvous.api.RendezvousClosedError">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.rendezvous.api.</span></span><span class="sig-name descname"><span class="pre">RendezvousClosedError</span></span><a class="reference internal" href="../_modules/torch/distributed/elastic/rendezvous/api.html#RendezvousClosedError"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/elastic/rendezvous/api.py#L38"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.rendezvous.api.RendezvousClosedError" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.rendezvous.api.</span></span><span class="sig-name descname"><span class="pre">RendezvousClosedError</span></span><a class="reference internal" href="../_modules/torch/distributed/elastic/rendezvous/api.html#RendezvousClosedError"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/elastic/rendezvous/api.py#L38"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.rendezvous.api.RendezvousClosedError" title="Permalink to this definition">¶</a></dt>
 <dd><p>Raised when a rendezvous is closed.</p>
 </dd></dl>
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributed.elastic.rendezvous.api.RendezvousTimeoutError">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.rendezvous.api.</span></span><span class="sig-name descname"><span class="pre">RendezvousTimeoutError</span></span><a class="reference internal" href="../_modules/torch/distributed/elastic/rendezvous/api.html#RendezvousTimeoutError"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/elastic/rendezvous/api.py#L42"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.rendezvous.api.RendezvousTimeoutError" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.rendezvous.api.</span></span><span class="sig-name descname"><span class="pre">RendezvousTimeoutError</span></span><a class="reference internal" href="../_modules/torch/distributed/elastic/rendezvous/api.html#RendezvousTimeoutError"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/elastic/rendezvous/api.py#L42"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.rendezvous.api.RendezvousTimeoutError" title="Permalink to this definition">¶</a></dt>
 <dd><p>Raised when a rendezvous did not complete on time.</p>
 </dd></dl>
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributed.elastic.rendezvous.api.RendezvousConnectionError">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.rendezvous.api.</span></span><span class="sig-name descname"><span class="pre">RendezvousConnectionError</span></span><a class="reference internal" href="../_modules/torch/distributed/elastic/rendezvous/api.html#RendezvousConnectionError"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/elastic/rendezvous/api.py#L46"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.rendezvous.api.RendezvousConnectionError" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.rendezvous.api.</span></span><span class="sig-name descname"><span class="pre">RendezvousConnectionError</span></span><a class="reference internal" href="../_modules/torch/distributed/elastic/rendezvous/api.html#RendezvousConnectionError"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/elastic/rendezvous/api.py#L46"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.rendezvous.api.RendezvousConnectionError" title="Permalink to this definition">¶</a></dt>
 <dd><p>Raised when the connection to a rendezvous backend has failed.</p>
 </dd></dl>
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributed.elastic.rendezvous.api.RendezvousStateError">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.rendezvous.api.</span></span><span class="sig-name descname"><span class="pre">RendezvousStateError</span></span><a class="reference internal" href="../_modules/torch/distributed/elastic/rendezvous/api.html#RendezvousStateError"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/elastic/rendezvous/api.py#L50"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.rendezvous.api.RendezvousStateError" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.rendezvous.api.</span></span><span class="sig-name descname"><span class="pre">RendezvousStateError</span></span><a class="reference internal" href="../_modules/torch/distributed/elastic/rendezvous/api.html#RendezvousStateError"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/elastic/rendezvous/api.py#L50"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.rendezvous.api.RendezvousStateError" title="Permalink to this definition">¶</a></dt>
 <dd><p>Raised when the state of a rendezvous is corrupt.</p>
 </dd></dl>
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributed.elastic.rendezvous.api.RendezvousGracefulExitError">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.rendezvous.api.</span></span><span class="sig-name descname"><span class="pre">RendezvousGracefulExitError</span></span><a class="reference internal" href="../_modules/torch/distributed/elastic/rendezvous/api.html#RendezvousGracefulExitError"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/elastic/rendezvous/api.py#L54"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.rendezvous.api.RendezvousGracefulExitError" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.rendezvous.api.</span></span><span class="sig-name descname"><span class="pre">RendezvousGracefulExitError</span></span><a class="reference internal" href="../_modules/torch/distributed/elastic/rendezvous/api.html#RendezvousGracefulExitError"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/elastic/rendezvous/api.py#L54"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.rendezvous.api.RendezvousGracefulExitError" title="Permalink to this definition">¶</a></dt>
 <dd><p>Raised when node wasn’t not included in rendezvous and gracefully exits.</p>
 <p>Exception is a mechanism to exit the stack, however does not mean a failure.</p>
 </dd></dl>
@@ -989,7 +989,7 @@ <h2>Implementations<a class="headerlink" href="#implementations" title="Permalin
 <h3>Dynamic Rendezvous<a class="headerlink" href="#dynamic-rendezvous" title="Permalink to this heading">¶</a></h3>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.distributed.elastic.rendezvous.dynamic_rendezvous.create_handler">
-<span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.rendezvous.dynamic_rendezvous.</span></span><span class="sig-name descname"><span class="pre">create_handler</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">store</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">backend</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/rendezvous/dynamic_rendezvous.html#create_handler"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/elastic/rendezvous/dynamic_rendezvous.py#L1380"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.rendezvous.dynamic_rendezvous.create_handler" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.rendezvous.dynamic_rendezvous.</span></span><span class="sig-name descname"><span class="pre">create_handler</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">store</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">backend</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/rendezvous/dynamic_rendezvous.html#create_handler"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/elastic/rendezvous/dynamic_rendezvous.py#L1380"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.rendezvous.dynamic_rendezvous.create_handler" title="Permalink to this definition">¶</a></dt>
 <dd><p>Create a new <a class="reference internal" href="#torch.distributed.elastic.rendezvous.dynamic_rendezvous.DynamicRendezvousHandler" title="torch.distributed.elastic.rendezvous.dynamic_rendezvous.DynamicRendezvousHandler"><code class="xref py py-class docutils literal notranslate"><span class="pre">DynamicRendezvousHandler</span></code></a> from the specified parameters.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -1032,13 +1032,13 @@ <h3>Dynamic Rendezvous<a class="headerlink" href="#dynamic-rendezvous" title="Pe
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributed.elastic.rendezvous.dynamic_rendezvous.DynamicRendezvousHandler">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.rendezvous.dynamic_rendezvous.</span></span><span class="sig-name descname"><span class="pre">DynamicRendezvousHandler</span></span><a class="reference internal" href="../_modules/torch/distributed/elastic/rendezvous/dynamic_rendezvous.html#DynamicRendezvousHandler"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/elastic/rendezvous/dynamic_rendezvous.py#L995"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.rendezvous.dynamic_rendezvous.DynamicRendezvousHandler" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.rendezvous.dynamic_rendezvous.</span></span><span class="sig-name descname"><span class="pre">DynamicRendezvousHandler</span></span><a class="reference internal" href="../_modules/torch/distributed/elastic/rendezvous/dynamic_rendezvous.html#DynamicRendezvousHandler"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/elastic/rendezvous/dynamic_rendezvous.py#L995"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.rendezvous.dynamic_rendezvous.DynamicRendezvousHandler" title="Permalink to this definition">¶</a></dt>
 <dd><p>Represent a handler that sets up a rendezvous among a set of nodes.</p>
 <dl class="field-list simple">
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.elastic.rendezvous.dynamic_rendezvous.DynamicRendezvousHandler.from_backend">
-<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_backend</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">run_id</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">store</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">backend</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_nodes</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_nodes</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">local_addr</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">timeout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/rendezvous/dynamic_rendezvous.html#DynamicRendezvousHandler.from_backend"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/elastic/rendezvous/dynamic_rendezvous.py#L1010"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.rendezvous.dynamic_rendezvous.DynamicRendezvousHandler.from_backend" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_backend</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">run_id</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">store</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">backend</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_nodes</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_nodes</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">local_addr</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">timeout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/rendezvous/dynamic_rendezvous.html#DynamicRendezvousHandler.from_backend"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/elastic/rendezvous/dynamic_rendezvous.py#L1010"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.rendezvous.dynamic_rendezvous.DynamicRendezvousHandler.from_backend" title="Permalink to this definition">¶</a></dt>
 <dd><p>Create a new <a class="reference internal" href="#torch.distributed.elastic.rendezvous.dynamic_rendezvous.DynamicRendezvousHandler" title="torch.distributed.elastic.rendezvous.dynamic_rendezvous.DynamicRendezvousHandler"><code class="xref py py-class docutils literal notranslate"><span class="pre">DynamicRendezvousHandler</span></code></a>.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -1059,11 +1059,11 @@ <h3>Dynamic Rendezvous<a class="headerlink" href="#dynamic-rendezvous" title="Pe
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributed.elastic.rendezvous.dynamic_rendezvous.RendezvousBackend">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.rendezvous.dynamic_rendezvous.</span></span><span class="sig-name descname"><span class="pre">RendezvousBackend</span></span><a class="reference internal" href="../_modules/torch/distributed/elastic/rendezvous/dynamic_rendezvous.html#RendezvousBackend"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/elastic/rendezvous/dynamic_rendezvous.py#L61"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.rendezvous.dynamic_rendezvous.RendezvousBackend" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.rendezvous.dynamic_rendezvous.</span></span><span class="sig-name descname"><span class="pre">RendezvousBackend</span></span><a class="reference internal" href="../_modules/torch/distributed/elastic/rendezvous/dynamic_rendezvous.html#RendezvousBackend"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/elastic/rendezvous/dynamic_rendezvous.py#L61"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.rendezvous.dynamic_rendezvous.RendezvousBackend" title="Permalink to this definition">¶</a></dt>
 <dd><p>Represent a backend that holds the rendezvous state.</p>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.elastic.rendezvous.dynamic_rendezvous.RendezvousBackend.get_state">
-<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">get_state</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/rendezvous/dynamic_rendezvous.html#RendezvousBackend.get_state"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/elastic/rendezvous/dynamic_rendezvous.py#L69"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.rendezvous.dynamic_rendezvous.RendezvousBackend.get_state" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">get_state</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/rendezvous/dynamic_rendezvous.html#RendezvousBackend.get_state"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/elastic/rendezvous/dynamic_rendezvous.py#L69"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.rendezvous.dynamic_rendezvous.RendezvousBackend.get_state" title="Permalink to this definition">¶</a></dt>
 <dd><p>Get the rendezvous state.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Returns</dt>
@@ -1090,7 +1090,7 @@ <h3>Dynamic Rendezvous<a class="headerlink" href="#dynamic-rendezvous" title="Pe
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.elastic.rendezvous.dynamic_rendezvous.RendezvousBackend.set_state">
-<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">set_state</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">token</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/rendezvous/dynamic_rendezvous.html#RendezvousBackend.set_state"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/elastic/rendezvous/dynamic_rendezvous.py#L84"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.rendezvous.dynamic_rendezvous.RendezvousBackend.set_state" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">set_state</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">token</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/rendezvous/dynamic_rendezvous.html#RendezvousBackend.set_state"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/elastic/rendezvous/dynamic_rendezvous.py#L84"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.rendezvous.dynamic_rendezvous.RendezvousBackend.set_state" title="Permalink to this definition">¶</a></dt>
 <dd><p>Set the rendezvous state.</p>
 <p>The new rendezvous state is set conditionally:</p>
 <blockquote>
@@ -1135,7 +1135,7 @@ <h3>Dynamic Rendezvous<a class="headerlink" href="#dynamic-rendezvous" title="Pe
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributed.elastic.rendezvous.dynamic_rendezvous.RendezvousTimeout">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.rendezvous.dynamic_rendezvous.</span></span><span class="sig-name descname"><span class="pre">RendezvousTimeout</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">join</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">last_call</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">close</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">heartbeat</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/rendezvous/dynamic_rendezvous.html#RendezvousTimeout"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/elastic/rendezvous/dynamic_rendezvous.py#L122"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.rendezvous.dynamic_rendezvous.RendezvousTimeout" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.rendezvous.dynamic_rendezvous.</span></span><span class="sig-name descname"><span class="pre">RendezvousTimeout</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">join</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">last_call</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">close</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">heartbeat</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/rendezvous/dynamic_rendezvous.html#RendezvousTimeout"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/elastic/rendezvous/dynamic_rendezvous.py#L122"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.rendezvous.dynamic_rendezvous.RendezvousTimeout" title="Permalink to this definition">¶</a></dt>
 <dd><p>Hold the timeout configuration of a rendezvous.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -1181,7 +1181,7 @@ <h3>Dynamic Rendezvous<a class="headerlink" href="#dynamic-rendezvous" title="Pe
 <h4>C10d Backend<a class="headerlink" href="#c10d-backend" title="Permalink to this heading">¶</a></h4>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.distributed.elastic.rendezvous.c10d_rendezvous_backend.create_backend">
-<span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.rendezvous.c10d_rendezvous_backend.</span></span><span class="sig-name descname"><span class="pre">create_backend</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/rendezvous/c10d_rendezvous_backend.html#create_backend"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/elastic/rendezvous/c10d_rendezvous_backend.py#L214"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.rendezvous.c10d_rendezvous_backend.create_backend" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.rendezvous.c10d_rendezvous_backend.</span></span><span class="sig-name descname"><span class="pre">create_backend</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/rendezvous/c10d_rendezvous_backend.html#create_backend"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/elastic/rendezvous/c10d_rendezvous_backend.py#L214"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.rendezvous.c10d_rendezvous_backend.create_backend" title="Permalink to this definition">¶</a></dt>
 <dd><p>Create a new <a class="reference internal" href="#torch.distributed.elastic.rendezvous.c10d_rendezvous_backend.C10dRendezvousBackend" title="torch.distributed.elastic.rendezvous.c10d_rendezvous_backend.C10dRendezvousBackend"><code class="xref py py-class docutils literal notranslate"><span class="pre">C10dRendezvousBackend</span></code></a> from the specified parameters.</p>
 <table class="docutils colwidths-auto align-default">
 <thead>
@@ -1231,7 +1231,7 @@ <h4>C10d Backend<a class="headerlink" href="#c10d-backend" title="Permalink to t
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributed.elastic.rendezvous.c10d_rendezvous_backend.C10dRendezvousBackend">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.rendezvous.c10d_rendezvous_backend.</span></span><span class="sig-name descname"><span class="pre">C10dRendezvousBackend</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">store</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">run_id</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/rendezvous/c10d_rendezvous_backend.html#C10dRendezvousBackend"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/elastic/rendezvous/c10d_rendezvous_backend.py#L35"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.rendezvous.c10d_rendezvous_backend.C10dRendezvousBackend" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.rendezvous.c10d_rendezvous_backend.</span></span><span class="sig-name descname"><span class="pre">C10dRendezvousBackend</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">store</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">run_id</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/rendezvous/c10d_rendezvous_backend.html#C10dRendezvousBackend"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/elastic/rendezvous/c10d_rendezvous_backend.py#L35"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.rendezvous.c10d_rendezvous_backend.C10dRendezvousBackend" title="Permalink to this definition">¶</a></dt>
 <dd><p>Represents a C10d-backed rendezvous backend.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -1244,7 +1244,7 @@ <h4>C10d Backend<a class="headerlink" href="#c10d-backend" title="Permalink to t
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.elastic.rendezvous.c10d_rendezvous_backend.C10dRendezvousBackend.get_state">
-<span class="sig-name descname"><span class="pre">get_state</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/rendezvous/c10d_rendezvous_backend.html#C10dRendezvousBackend.get_state"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/elastic/rendezvous/c10d_rendezvous_backend.py#L73"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.rendezvous.c10d_rendezvous_backend.C10dRendezvousBackend.get_state" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">get_state</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/rendezvous/c10d_rendezvous_backend.html#C10dRendezvousBackend.get_state"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/elastic/rendezvous/c10d_rendezvous_backend.py#L73"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.rendezvous.c10d_rendezvous_backend.C10dRendezvousBackend.get_state" title="Permalink to this definition">¶</a></dt>
 <dd><p>See base class.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -1261,7 +1261,7 @@ <h4>C10d Backend<a class="headerlink" href="#c10d-backend" title="Permalink to t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.elastic.rendezvous.c10d_rendezvous_backend.C10dRendezvousBackend.set_state">
-<span class="sig-name descname"><span class="pre">set_state</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">token</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/rendezvous/c10d_rendezvous_backend.html#C10dRendezvousBackend.set_state"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/elastic/rendezvous/c10d_rendezvous_backend.py#L79"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.rendezvous.c10d_rendezvous_backend.C10dRendezvousBackend.set_state" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">set_state</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">token</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/rendezvous/c10d_rendezvous_backend.html#C10dRendezvousBackend.set_state"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/elastic/rendezvous/c10d_rendezvous_backend.py#L79"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.rendezvous.c10d_rendezvous_backend.C10dRendezvousBackend.set_state" title="Permalink to this definition">¶</a></dt>
 <dd><p>See base class.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -1277,7 +1277,7 @@ <h4>C10d Backend<a class="headerlink" href="#c10d-backend" title="Permalink to t
 <h4>Etcd Backend<a class="headerlink" href="#etcd-backend" title="Permalink to this heading">¶</a></h4>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.distributed.elastic.rendezvous.etcd_rendezvous_backend.create_backend">
-<span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.rendezvous.etcd_rendezvous_backend.</span></span><span class="sig-name descname"><span class="pre">create_backend</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/rendezvous/etcd_rendezvous_backend.html#create_backend"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/elastic/rendezvous/etcd_rendezvous_backend.py#L185"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.rendezvous.etcd_rendezvous_backend.create_backend" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.rendezvous.etcd_rendezvous_backend.</span></span><span class="sig-name descname"><span class="pre">create_backend</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">params</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/rendezvous/etcd_rendezvous_backend.html#create_backend"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/elastic/rendezvous/etcd_rendezvous_backend.py#L185"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.rendezvous.etcd_rendezvous_backend.create_backend" title="Permalink to this definition">¶</a></dt>
 <dd><p>Create a new <a class="reference internal" href="#torch.distributed.elastic.rendezvous.etcd_rendezvous_backend.EtcdRendezvousBackend" title="torch.distributed.elastic.rendezvous.etcd_rendezvous_backend.EtcdRendezvousBackend"><code class="xref py py-class docutils literal notranslate"><span class="pre">EtcdRendezvousBackend</span></code></a> from the specified parameters.</p>
 <table class="docutils colwidths-auto align-default">
 <thead>
@@ -1317,7 +1317,7 @@ <h4>Etcd Backend<a class="headerlink" href="#etcd-backend" title="Permalink to t
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributed.elastic.rendezvous.etcd_rendezvous_backend.EtcdRendezvousBackend">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.rendezvous.etcd_rendezvous_backend.</span></span><span class="sig-name descname"><span class="pre">EtcdRendezvousBackend</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">client</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">run_id</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">key_prefix</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ttl</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/rendezvous/etcd_rendezvous_backend.html#EtcdRendezvousBackend"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/elastic/rendezvous/etcd_rendezvous_backend.py#L28"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.rendezvous.etcd_rendezvous_backend.EtcdRendezvousBackend" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.rendezvous.etcd_rendezvous_backend.</span></span><span class="sig-name descname"><span class="pre">EtcdRendezvousBackend</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">client</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">run_id</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">key_prefix</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ttl</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/rendezvous/etcd_rendezvous_backend.html#EtcdRendezvousBackend"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/elastic/rendezvous/etcd_rendezvous_backend.py#L28"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.rendezvous.etcd_rendezvous_backend.EtcdRendezvousBackend" title="Permalink to this definition">¶</a></dt>
 <dd><p>Represents an etcd-based rendezvous backend.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -1331,7 +1331,7 @@ <h4>Etcd Backend<a class="headerlink" href="#etcd-backend" title="Permalink to t
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.elastic.rendezvous.etcd_rendezvous_backend.EtcdRendezvousBackend.get_state">
-<span class="sig-name descname"><span class="pre">get_state</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/rendezvous/etcd_rendezvous_backend.html#EtcdRendezvousBackend.get_state"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/elastic/rendezvous/etcd_rendezvous_backend.py#L75"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.rendezvous.etcd_rendezvous_backend.EtcdRendezvousBackend.get_state" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">get_state</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/rendezvous/etcd_rendezvous_backend.html#EtcdRendezvousBackend.get_state"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/elastic/rendezvous/etcd_rendezvous_backend.py#L75"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.rendezvous.etcd_rendezvous_backend.EtcdRendezvousBackend.get_state" title="Permalink to this definition">¶</a></dt>
 <dd><p>See base class.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -1348,7 +1348,7 @@ <h4>Etcd Backend<a class="headerlink" href="#etcd-backend" title="Permalink to t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.elastic.rendezvous.etcd_rendezvous_backend.EtcdRendezvousBackend.set_state">
-<span class="sig-name descname"><span class="pre">set_state</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">token</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/rendezvous/etcd_rendezvous_backend.html#EtcdRendezvousBackend.set_state"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/elastic/rendezvous/etcd_rendezvous_backend.py#L88"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.rendezvous.etcd_rendezvous_backend.EtcdRendezvousBackend.set_state" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">set_state</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">token</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/rendezvous/etcd_rendezvous_backend.html#EtcdRendezvousBackend.set_state"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/elastic/rendezvous/etcd_rendezvous_backend.py#L88"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.rendezvous.etcd_rendezvous_backend.EtcdRendezvousBackend.set_state" title="Permalink to this definition">¶</a></dt>
 <dd><p>See base class.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -1371,7 +1371,7 @@ <h3>Etcd Rendezvous (Legacy)<a class="headerlink" href="#etcd-rendezvous-legacy"
 </div>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributed.elastic.rendezvous.etcd_rendezvous.EtcdRendezvousHandler">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.rendezvous.etcd_rendezvous.</span></span><span class="sig-name descname"><span class="pre">EtcdRendezvousHandler</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">rdzv_impl</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">local_addr</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/rendezvous/etcd_rendezvous.html#EtcdRendezvousHandler"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/elastic/rendezvous/etcd_rendezvous.py#L92"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.rendezvous.etcd_rendezvous.EtcdRendezvousHandler" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.rendezvous.etcd_rendezvous.</span></span><span class="sig-name descname"><span class="pre">EtcdRendezvousHandler</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">rdzv_impl</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">local_addr</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/rendezvous/etcd_rendezvous.html#EtcdRendezvousHandler"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/elastic/rendezvous/etcd_rendezvous.py#L92"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.rendezvous.etcd_rendezvous.EtcdRendezvousHandler" title="Permalink to this definition">¶</a></dt>
 <dd><p>Implements a
 <a class="reference internal" href="#torch.distributed.elastic.rendezvous.RendezvousHandler" title="torch.distributed.elastic.rendezvous.RendezvousHandler"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.distributed.elastic.rendezvous.RendezvousHandler</span></code></a> interface
 backed by
@@ -1452,14 +1452,14 @@ <h3>Etcd Store<a class="headerlink" href="#etcd-store" title="Permalink to this
 <code class="docutils literal notranslate"><span class="pre">next_rendezvous()</span></code> when etcd is used as the rendezvous backend.</p>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributed.elastic.rendezvous.etcd_store.EtcdStore">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.rendezvous.etcd_store.</span></span><span class="sig-name descname"><span class="pre">EtcdStore</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">etcd_client</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">etcd_store_prefix</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">timeout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/rendezvous/etcd_store.html#EtcdStore"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/elastic/rendezvous/etcd_store.py#L31"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.rendezvous.etcd_store.EtcdStore" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.rendezvous.etcd_store.</span></span><span class="sig-name descname"><span class="pre">EtcdStore</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">etcd_client</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">etcd_store_prefix</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">timeout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/rendezvous/etcd_store.html#EtcdStore"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/elastic/rendezvous/etcd_store.py#L31"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.rendezvous.etcd_store.EtcdStore" title="Permalink to this definition">¶</a></dt>
 <dd><p>Implement a c10 Store interface by piggybacking on the rendezvous etcd instance.</p>
 <p>This is the store object returned by <code class="docutils literal notranslate"><span class="pre">EtcdRendezvous</span></code>.</p>
 <dl class="field-list simple">
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.elastic.rendezvous.etcd_store.EtcdStore.add">
-<span class="sig-name descname"><span class="pre">add</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">key</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/rendezvous/etcd_store.html#EtcdStore.add"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/elastic/rendezvous/etcd_store.py#L86"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.rendezvous.etcd_store.EtcdStore.add" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">add</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">key</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/rendezvous/etcd_store.html#EtcdStore.add"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/elastic/rendezvous/etcd_store.py#L86"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.rendezvous.etcd_store.EtcdStore.add" title="Permalink to this definition">¶</a></dt>
 <dd><p>Atomically increment a value by an integer amount.</p>
 <p>The integer is represented as a string using base 10. If key is not present,
 a default value of <code class="docutils literal notranslate"><span class="pre">0</span></code> will be assumed.</p>
@@ -1475,7 +1475,7 @@ <h3>Etcd Store<a class="headerlink" href="#etcd-store" title="Permalink to this
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.elastic.rendezvous.etcd_store.EtcdStore.check">
-<span class="sig-name descname"><span class="pre">check</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">keys</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/rendezvous/etcd_store.html#EtcdStore.check"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/elastic/rendezvous/etcd_store.py#L137"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.rendezvous.etcd_store.EtcdStore.check" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">check</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">keys</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/rendezvous/etcd_store.html#EtcdStore.check"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/elastic/rendezvous/etcd_store.py#L137"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.rendezvous.etcd_store.EtcdStore.check" title="Permalink to this definition">¶</a></dt>
 <dd><p>Check if all of the keys are immediately present (without waiting).</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -1486,7 +1486,7 @@ <h3>Etcd Store<a class="headerlink" href="#etcd-store" title="Permalink to this
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.elastic.rendezvous.etcd_store.EtcdStore.get">
-<span class="sig-name descname"><span class="pre">get</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">key</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/rendezvous/etcd_store.html#EtcdStore.get"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/elastic/rendezvous/etcd_store.py#L64"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.rendezvous.etcd_store.EtcdStore.get" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">get</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">key</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/rendezvous/etcd_store.html#EtcdStore.get"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/elastic/rendezvous/etcd_store.py#L64"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.rendezvous.etcd_store.EtcdStore.get" title="Permalink to this definition">¶</a></dt>
 <dd><p>Get a value by key, possibly doing a blocking wait.</p>
 <p>If key is not immediately present, will do a blocking wait
 for at most <code class="docutils literal notranslate"><span class="pre">timeout</span></code> duration or until the key is published.</p>
@@ -1505,14 +1505,14 @@ <h3>Etcd Store<a class="headerlink" href="#etcd-store" title="Permalink to this
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.elastic.rendezvous.etcd_store.EtcdStore.set">
-<span class="sig-name descname"><span class="pre">set</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">key</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/rendezvous/etcd_store.html#EtcdStore.set"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/elastic/rendezvous/etcd_store.py#L56"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.rendezvous.etcd_store.EtcdStore.set" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">set</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">key</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/rendezvous/etcd_store.html#EtcdStore.set"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/elastic/rendezvous/etcd_store.py#L56"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.rendezvous.etcd_store.EtcdStore.set" title="Permalink to this definition">¶</a></dt>
 <dd><p>Write a key/value pair into <code class="docutils literal notranslate"><span class="pre">EtcdStore</span></code>.</p>
 <p>Both key and value may be either Python <code class="docutils literal notranslate"><span class="pre">str</span></code> or <code class="docutils literal notranslate"><span class="pre">bytes</span></code>.</p>
 </dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.elastic.rendezvous.etcd_store.EtcdStore.wait">
-<span class="sig-name descname"><span class="pre">wait</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">keys</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">override_timeout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/rendezvous/etcd_store.html#EtcdStore.wait"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/elastic/rendezvous/etcd_store.py#L124"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.rendezvous.etcd_store.EtcdStore.wait" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">wait</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">keys</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">override_timeout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/rendezvous/etcd_store.html#EtcdStore.wait"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/elastic/rendezvous/etcd_store.py#L124"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.rendezvous.etcd_store.EtcdStore.wait" title="Permalink to this definition">¶</a></dt>
 <dd><p>Wait until all of the keys are published, or until timeout.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Raises</dt>
@@ -1538,7 +1538,7 @@ <h3>Etcd Server<a class="headerlink" href="#etcd-server" title="Permalink to thi
 </div>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributed.elastic.rendezvous.etcd_server.EtcdServer">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.rendezvous.etcd_server.</span></span><span class="sig-name descname"><span class="pre">EtcdServer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">data_dir</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/rendezvous/etcd_server.html#EtcdServer"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/elastic/rendezvous/etcd_server.py#L78"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.rendezvous.etcd_server.EtcdServer" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.rendezvous.etcd_server.</span></span><span class="sig-name descname"><span class="pre">EtcdServer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">data_dir</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/rendezvous/etcd_server.html#EtcdServer"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/elastic/rendezvous/etcd_server.py#L78"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.rendezvous.etcd_server.EtcdServer" title="Permalink to this definition">¶</a></dt>
 <dd><div class="admonition note">
 <p class="admonition-title">Note</p>
 <p>tested on etcd server v3.4.3.</p>
diff --git a/main/elastic/run.html b/main/elastic/run.html
index 18e5c5c0dde..6d4add9fa88 100644
--- a/main/elastic/run.html
+++ b/main/elastic/run.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/elastic/subprocess_handler.html b/main/elastic/subprocess_handler.html
index 78c575a6e87..3a3097faa99 100644
--- a/main/elastic/subprocess_handler.html
+++ b/main/elastic/subprocess_handler.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h2>Retrieve SubprocessHandler<a class="headerlink" href="#retrieve-subprocesshandler" title="Permalink to this heading">¶</a></h2>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.distributed.elastic.multiprocessing.subprocess_handler.handlers.get_subprocess_handler">
-<span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.multiprocessing.subprocess_handler.handlers.</span></span><span class="sig-name descname"><span class="pre">get_subprocess_handler</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">entrypoint</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">env</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stdout</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stderr</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">local_rank_id</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/multiprocessing/subprocess_handler/handlers.html#get_subprocess_handler"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/elastic/multiprocessing/subprocess_handler/handlers.py#L15"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.multiprocessing.subprocess_handler.handlers.get_subprocess_handler" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.multiprocessing.subprocess_handler.handlers.</span></span><span class="sig-name descname"><span class="pre">get_subprocess_handler</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">entrypoint</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">env</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stdout</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stderr</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">local_rank_id</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/multiprocessing/subprocess_handler/handlers.html#get_subprocess_handler"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/elastic/multiprocessing/subprocess_handler/handlers.py#L15"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.multiprocessing.subprocess_handler.handlers.get_subprocess_handler" title="Permalink to this definition">¶</a></dt>
 <dd><dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
 <dd class="field-odd"><p><a class="reference internal" href="#torch.distributed.elastic.multiprocessing.subprocess_handler.subprocess_handler.SubprocessHandler" title="torch.distributed.elastic.multiprocessing.subprocess_handler.subprocess_handler.SubprocessHandler"><em>SubprocessHandler</em></a></p>
@@ -627,7 +627,7 @@ <h2>Retrieve SubprocessHandler<a class="headerlink" href="#retrieve-subprocessha
 <h2>SubprocessHandler<a class="headerlink" href="#subprocesshandler" title="Permalink to this heading">¶</a></h2>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributed.elastic.multiprocessing.subprocess_handler.subprocess_handler.SubprocessHandler">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.multiprocessing.subprocess_handler.subprocess_handler.</span></span><span class="sig-name descname"><span class="pre">SubprocessHandler</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">entrypoint</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">env</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stdout</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stderr</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">local_rank_id</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/multiprocessing/subprocess_handler/subprocess_handler.html#SubprocessHandler"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/elastic/multiprocessing/subprocess_handler/subprocess_handler.py#L28"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.multiprocessing.subprocess_handler.subprocess_handler.SubprocessHandler" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.multiprocessing.subprocess_handler.subprocess_handler.</span></span><span class="sig-name descname"><span class="pre">SubprocessHandler</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">entrypoint</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">env</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stdout</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stderr</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">local_rank_id</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/multiprocessing/subprocess_handler/subprocess_handler.html#SubprocessHandler"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/elastic/multiprocessing/subprocess_handler/subprocess_handler.py#L28"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.multiprocessing.subprocess_handler.subprocess_handler.SubprocessHandler" title="Permalink to this definition">¶</a></dt>
 <dd><p>Convenience wrapper around python’s <code class="docutils literal notranslate"><span class="pre">subprocess.Popen</span></code>. Keeps track of
 meta-objects associated to the process (e.g. stdout and stderr redirect fds).</p>
 <dl class="field-list simple">
diff --git a/main/elastic/timer.html b/main/elastic/timer.html
index 6b36fa1b61a..4118025759e 100644
--- a/main/elastic/timer.html
+++ b/main/elastic/timer.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -645,7 +645,7 @@
 <h2>Client Methods<a class="headerlink" href="#client-methods" title="Permalink to this heading">¶</a></h2>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.distributed.elastic.timer.configure">
-<span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.timer.</span></span><span class="sig-name descname"><span class="pre">configure</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">timer_client</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/timer/api.html#configure"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/elastic/timer/api.py#L240"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.timer.configure" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.timer.</span></span><span class="sig-name descname"><span class="pre">configure</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">timer_client</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/timer/api.html#configure"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/elastic/timer/api.py#L240"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.timer.configure" title="Permalink to this definition">¶</a></dt>
 <dd><p>Configures a timer client. Must be called before using <code class="docutils literal notranslate"><span class="pre">expires</span></code>.</p>
 <dl class="field-list simple">
 </dl>
@@ -653,7 +653,7 @@ <h2>Client Methods<a class="headerlink" href="#client-methods" title="Permalink
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.distributed.elastic.timer.expires">
-<span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.timer.</span></span><span class="sig-name descname"><span class="pre">expires</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">after</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scope</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">client</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/timer/api.html#expires"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/elastic/timer/api.py#L249"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.timer.expires" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.timer.</span></span><span class="sig-name descname"><span class="pre">expires</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">after</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scope</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">client</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/timer/api.html#expires"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/elastic/timer/api.py#L249"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.timer.expires" title="Permalink to this definition">¶</a></dt>
 <dd><p>Acquires a countdown timer that expires in <code class="docutils literal notranslate"><span class="pre">after</span></code> seconds from now,
 unless the code-block that it wraps is finished within the timeframe.
 When the timer expires, this worker is eligible to be reaped. The
@@ -687,7 +687,7 @@ <h2>Server/Client Implementations<a class="headerlink" href="#server-client-impl
 a <code class="docutils literal notranslate"><span class="pre">multiprocess.Queue</span></code>.</p>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributed.elastic.timer.LocalTimerServer">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.timer.</span></span><span class="sig-name descname"><span class="pre">LocalTimerServer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mp_queue</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_interval</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">60</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">daemon</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/timer/local_timer.html#LocalTimerServer"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/elastic/timer/local_timer.py#L78"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.timer.LocalTimerServer" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.timer.</span></span><span class="sig-name descname"><span class="pre">LocalTimerServer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mp_queue</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_interval</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">60</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">daemon</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/timer/local_timer.html#LocalTimerServer"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/elastic/timer/local_timer.py#L78"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.timer.LocalTimerServer" title="Permalink to this definition">¶</a></dt>
 <dd><p>Server that works with <code class="docutils literal notranslate"><span class="pre">LocalTimerClient</span></code>. Clients are expected to be
 subprocesses to the parent process that is running this server. Each host
 in the job is expected to start its own timer server locally and each
@@ -699,7 +699,7 @@ <h2>Server/Client Implementations<a class="headerlink" href="#server-client-impl
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributed.elastic.timer.LocalTimerClient">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.timer.</span></span><span class="sig-name descname"><span class="pre">LocalTimerClient</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mp_queue</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/timer/local_timer.html#LocalTimerClient"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/elastic/timer/local_timer.py#L23"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.timer.LocalTimerClient" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.timer.</span></span><span class="sig-name descname"><span class="pre">LocalTimerClient</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mp_queue</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/timer/local_timer.html#LocalTimerClient"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/elastic/timer/local_timer.py#L23"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.timer.LocalTimerClient" title="Permalink to this definition">¶</a></dt>
 <dd><p>Client side of <code class="docutils literal notranslate"><span class="pre">LocalTimerServer</span></code>. This client is meant to be used
 on the same host that the <code class="docutils literal notranslate"><span class="pre">LocalTimerServer</span></code> is running on and uses
 pid to uniquely identify a worker. This is particularly useful in situations
@@ -711,7 +711,7 @@ <h2>Server/Client Implementations<a class="headerlink" href="#server-client-impl
 based on a named pipe.</p>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributed.elastic.timer.FileTimerServer">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.timer.</span></span><span class="sig-name descname"><span class="pre">FileTimerServer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">file_path</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">run_id</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_interval</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">daemon</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">log_event</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/timer/file_based_local_timer.html#FileTimerServer"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/elastic/timer/file_based_local_timer.py#L172"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.timer.FileTimerServer" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.timer.</span></span><span class="sig-name descname"><span class="pre">FileTimerServer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">file_path</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">run_id</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_interval</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">daemon</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">log_event</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/timer/file_based_local_timer.html#FileTimerServer"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/elastic/timer/file_based_local_timer.py#L172"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.timer.FileTimerServer" title="Permalink to this definition">¶</a></dt>
 <dd><p>Server that works with <code class="docutils literal notranslate"><span class="pre">FileTimerClient</span></code>. Clients are expected to be
 running on the same host as the process that is running this server.
 Each host in the job is expected to start its own timer server locally
@@ -733,7 +733,7 @@ <h2>Server/Client Implementations<a class="headerlink" href="#server-client-impl
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributed.elastic.timer.FileTimerClient">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.timer.</span></span><span class="sig-name descname"><span class="pre">FileTimerClient</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">file_path</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">signal</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">Signals.SIGKILL</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/timer/file_based_local_timer.html#FileTimerClient"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/elastic/timer/file_based_local_timer.py#L100"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.timer.FileTimerClient" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.timer.</span></span><span class="sig-name descname"><span class="pre">FileTimerClient</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">file_path</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">signal</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">Signals.SIGKILL</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/timer/file_based_local_timer.html#FileTimerClient"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/elastic/timer/file_based_local_timer.py#L100"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.timer.FileTimerClient" title="Permalink to this definition">¶</a></dt>
 <dd><p>Client side of <code class="docutils literal notranslate"><span class="pre">FileTimerServer</span></code>. This client is meant to be used
 on the same host that the <code class="docutils literal notranslate"><span class="pre">FileTimerServer</span></code> is running on and uses
 pid to uniquely identify a worker.
@@ -763,7 +763,7 @@ <h2>Writing a custom timer server/client<a class="headerlink" href="#writing-a-c
 the server and client.</p>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributed.elastic.timer.TimerRequest">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.timer.</span></span><span class="sig-name descname"><span class="pre">TimerRequest</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">worker_id</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scope_id</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">expiration_time</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/timer/api.html#TimerRequest"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/elastic/timer/api.py#L28"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.timer.TimerRequest" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.timer.</span></span><span class="sig-name descname"><span class="pre">TimerRequest</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">worker_id</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scope_id</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">expiration_time</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/timer/api.html#TimerRequest"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/elastic/timer/api.py#L28"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.timer.TimerRequest" title="Permalink to this definition">¶</a></dt>
 <dd><p>Data object representing a countdown timer acquisition and release
 that is used between the <code class="docutils literal notranslate"><span class="pre">TimerClient</span></code> and <code class="docutils literal notranslate"><span class="pre">TimerServer</span></code>.
 A negative <code class="docutils literal notranslate"><span class="pre">expiration_time</span></code> should be interpreted as a “release”
@@ -780,7 +780,7 @@ <h2>Writing a custom timer server/client<a class="headerlink" href="#writing-a-c
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributed.elastic.timer.TimerServer">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.timer.</span></span><span class="sig-name descname"><span class="pre">TimerServer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">request_queue</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_interval</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">daemon</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/timer/api.html#TimerServer"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/elastic/timer/api.py#L113"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.timer.TimerServer" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.timer.</span></span><span class="sig-name descname"><span class="pre">TimerServer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">request_queue</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_interval</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">daemon</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/timer/api.html#TimerServer"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/elastic/timer/api.py#L113"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.timer.TimerServer" title="Permalink to this definition">¶</a></dt>
 <dd><p>Entity that monitors active timers and expires them
 in a timely fashion. This server is responsible for
 reaping workers that have expired timers.</p>
@@ -788,7 +788,7 @@ <h2>Writing a custom timer server/client<a class="headerlink" href="#writing-a-c
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.elastic.timer.TimerServer.clear_timers">
-<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">clear_timers</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">worker_ids</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/timer/api.html#TimerServer.clear_timers"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/elastic/timer/api.py#L145"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.timer.TimerServer.clear_timers" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">clear_timers</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">worker_ids</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/timer/api.html#TimerServer.clear_timers"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/elastic/timer/api.py#L145"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.timer.TimerServer.clear_timers" title="Permalink to this definition">¶</a></dt>
 <dd><p>Clears all timers for the given <code class="docutils literal notranslate"><span class="pre">worker_ids</span></code>.</p>
 <dl class="field-list simple">
 </dl>
@@ -796,7 +796,7 @@ <h2>Writing a custom timer server/client<a class="headerlink" href="#writing-a-c
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.elastic.timer.TimerServer.get_expired_timers">
-<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">get_expired_timers</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deadline</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/timer/api.html#TimerServer.get_expired_timers"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/elastic/timer/api.py#L151"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.timer.TimerServer.get_expired_timers" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">get_expired_timers</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deadline</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/timer/api.html#TimerServer.get_expired_timers"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/elastic/timer/api.py#L151"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.timer.TimerServer.get_expired_timers" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns all expired timers for each worker_id. An expired timer
 is a timer for which the expiration_time is less than or equal to
 the provided deadline.</p>
@@ -809,7 +809,7 @@ <h2>Writing a custom timer server/client<a class="headerlink" href="#writing-a-c
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.elastic.timer.TimerServer.register_timers">
-<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">register_timers</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">timer_requests</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/timer/api.html#TimerServer.register_timers"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/elastic/timer/api.py#L136"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.timer.TimerServer.register_timers" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">register_timers</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">timer_requests</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/timer/api.html#TimerServer.register_timers"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/elastic/timer/api.py#L136"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.timer.TimerServer.register_timers" title="Permalink to this definition">¶</a></dt>
 <dd><p>Processes the incoming timer requests and registers them with the server.
 The timer request can either be a acquire-timer or release-timer request.
 Timer requests with a negative expiration_time should be interpreted
@@ -822,12 +822,12 @@ <h2>Writing a custom timer server/client<a class="headerlink" href="#writing-a-c
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributed.elastic.timer.TimerClient">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.timer.</span></span><span class="sig-name descname"><span class="pre">TimerClient</span></span><a class="reference internal" href="../_modules/torch/distributed/elastic/timer/api.html#TimerClient"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/elastic/timer/api.py#L57"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.timer.TimerClient" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.timer.</span></span><span class="sig-name descname"><span class="pre">TimerClient</span></span><a class="reference internal" href="../_modules/torch/distributed/elastic/timer/api.html#TimerClient"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/elastic/timer/api.py#L57"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.timer.TimerClient" title="Permalink to this definition">¶</a></dt>
 <dd><p>Client library to acquire and release countdown timers by communicating
 with the TimerServer.</p>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.elastic.timer.TimerClient.acquire">
-<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">acquire</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">scope_id</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">expiration_time</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/timer/api.html#TimerClient.acquire"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/elastic/timer/api.py#L63"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.timer.TimerClient.acquire" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">acquire</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">scope_id</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">expiration_time</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/timer/api.html#TimerClient.acquire"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/elastic/timer/api.py#L63"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.timer.TimerClient.acquire" title="Permalink to this definition">¶</a></dt>
 <dd><p>Acquires a timer for the worker that holds this client object
 given the scope_id and expiration_time. Typically registers
 the timer with the TimerServer.</p>
@@ -837,7 +837,7 @@ <h2>Writing a custom timer server/client<a class="headerlink" href="#writing-a-c
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.elastic.timer.TimerClient.release">
-<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">release</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">scope_id</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/timer/api.html#TimerClient.release"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/elastic/timer/api.py#L71"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.timer.TimerClient.release" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">release</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">scope_id</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/timer/api.html#TimerClient.release"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/elastic/timer/api.py#L71"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.timer.TimerClient.release" title="Permalink to this definition">¶</a></dt>
 <dd><p>Releases the timer for the <code class="docutils literal notranslate"><span class="pre">scope_id</span></code> on the worker this
 client represents. After this method is
 called, the countdown timer on the scope is no longer in effect.</p>
@@ -852,7 +852,7 @@ <h2>Writing a custom timer server/client<a class="headerlink" href="#writing-a-c
 <span id="debug-info-logging"></span><h2>Debug info logging<a class="headerlink" href="#module-torch.distributed.elastic.timer.debug_info_logging" title="Permalink to this heading">¶</a></h2>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.distributed.elastic.timer.debug_info_logging.log_debug_info_for_expired_timers">
-<span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.timer.debug_info_logging.</span></span><span class="sig-name descname"><span class="pre">log_debug_info_for_expired_timers</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">run_id</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">expired_timers</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/timer/debug_info_logging.html#log_debug_info_for_expired_timers"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/elastic/timer/debug_info_logging.py#L19"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.timer.debug_info_logging.log_debug_info_for_expired_timers" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributed.elastic.timer.debug_info_logging.</span></span><span class="sig-name descname"><span class="pre">log_debug_info_for_expired_timers</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">run_id</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">expired_timers</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/distributed/elastic/timer/debug_info_logging.html#log_debug_info_for_expired_timers"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/elastic/timer/debug_info_logging.py#L19"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.elastic.timer.debug_info_logging.log_debug_info_for_expired_timers" title="Permalink to this definition">¶</a></dt>
 <dd><dl class="field-list simple">
 </dl>
 </dd></dl>
diff --git a/main/elastic/train_script.html b/main/elastic/train_script.html
index fd802ceffd6..7b545be4c25 100644
--- a/main/elastic/train_script.html
+++ b/main/elastic/train_script.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/export.html b/main/export.html
index 8e471cac2a1..6ebe060f32f 100644
--- a/main/export.html
+++ b/main/export.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
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     <div class="searchbox" id="googleSearchBox">
@@ -1593,7 +1593,7 @@ <h2>Read More<a class="headerlink" href="#read-more" title="Permalink to this he
 <span id="api-reference"></span><h2>API Reference<a class="headerlink" href="#module-torch.export" title="Permalink to this heading">¶</a></h2>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.export.export">
-<span class="sig-prename descclassname"><span class="pre">torch.export.</span></span><span class="sig-name descname"><span class="pre">export</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mod</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kwargs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dynamic_shapes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">strict</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">preserve_module_call_signature</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">()</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/export.html#export"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/export/__init__.py#L263"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.export.export" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.export.</span></span><span class="sig-name descname"><span class="pre">export</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mod</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kwargs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dynamic_shapes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">strict</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">preserve_module_call_signature</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">()</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/export.html#export"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/export/__init__.py#L263"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.export.export" title="Permalink to this definition">¶</a></dt>
 <dd><p><a class="reference internal" href="#torch.export.export" title="torch.export.export"><code class="xref py py-func docutils literal notranslate"><span class="pre">export()</span></code></a> takes any nn.Module along with example inputs, and produces a traced graph representing
 only the Tensor computation of the function in an Ahead-of-Time (AOT) fashion,
 which can subsequently be executed with different inputs or serialized.  The
@@ -1677,7 +1677,7 @@ <h2>Read More<a class="headerlink" href="#read-more" title="Permalink to this he
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.export.save">
-<span class="sig-prename descclassname"><span class="pre">torch.export.</span></span><span class="sig-name descname"><span class="pre">save</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">ep</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">f</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">extra_files</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opset_version</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pickle_protocol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/export.html#save"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/export/__init__.py#L382"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.export.save" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.export.</span></span><span class="sig-name descname"><span class="pre">save</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">ep</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">f</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">extra_files</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opset_version</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pickle_protocol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/export.html#save"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/export/__init__.py#L382"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.export.save" title="Permalink to this definition">¶</a></dt>
 <dd><div class="admonition warning">
 <p class="admonition-title">Warning</p>
 <p>Under active development, saved files may not be usable in newer versions
@@ -1724,7 +1724,7 @@ <h2>Read More<a class="headerlink" href="#read-more" title="Permalink to this he
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.export.load">
-<span class="sig-prename descclassname"><span class="pre">torch.export.</span></span><span class="sig-name descname"><span class="pre">load</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">f</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">extra_files</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">expected_opset_version</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/export.html#load"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/export/__init__.py#L466"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.export.load" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.export.</span></span><span class="sig-name descname"><span class="pre">load</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">f</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">extra_files</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">expected_opset_version</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/export.html#load"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/export/__init__.py#L466"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.export.load" title="Permalink to this definition">¶</a></dt>
 <dd><div class="admonition warning">
 <p class="admonition-title">Warning</p>
 <p>Under active development, saved files may not be usable in newer versions
@@ -1775,7 +1775,7 @@ <h2>Read More<a class="headerlink" href="#read-more" title="Permalink to this he
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.export.register_dataclass">
-<span class="sig-prename descclassname"><span class="pre">torch.export.</span></span><span class="sig-name descname"><span class="pre">register_dataclass</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">cls</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">serialized_type_name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/export.html#register_dataclass"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/export/__init__.py#L579"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.export.register_dataclass" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.export.</span></span><span class="sig-name descname"><span class="pre">register_dataclass</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">cls</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">serialized_type_name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/export.html#register_dataclass"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/export/__init__.py#L579"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.export.register_dataclass" title="Permalink to this definition">¶</a></dt>
 <dd><p>Registers a dataclass as a valid input/output type for <a class="reference internal" href="#torch.export.export" title="torch.export.export"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.export.export()</span></code></a>.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -1816,7 +1816,7 @@ <h2>Read More<a class="headerlink" href="#read-more" title="Permalink to this he
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.export.dynamic_shapes.Dim">
-<span class="sig-prename descclassname"><span class="pre">torch.export.dynamic_shapes.</span></span><span class="sig-name descname"><span class="pre">Dim</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/export/dynamic_shapes.html#Dim"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/export/dynamic_shapes.py#L209"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.export.dynamic_shapes.Dim" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.export.dynamic_shapes.</span></span><span class="sig-name descname"><span class="pre">Dim</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/export/dynamic_shapes.html#Dim"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/export/dynamic_shapes.py#L209"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.export.dynamic_shapes.Dim" title="Permalink to this definition">¶</a></dt>
 <dd><p><a class="reference internal" href="#torch.export.dynamic_shapes.Dim" title="torch.export.dynamic_shapes.Dim"><code class="xref py py-func docutils literal notranslate"><span class="pre">Dim()</span></code></a> constructs a type analogous to a named symbolic integer with a range.
 It can be used to describe multiple possible values of a dynamic tensor dimension.
 Note that different dynamic dimensions of the same tensor, or of different tensors,
@@ -1837,7 +1837,7 @@ <h2>Read More<a class="headerlink" href="#read-more" title="Permalink to this he
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.export.exported_program.default_decompositions">
-<span class="sig-prename descclassname"><span class="pre">torch.export.exported_program.</span></span><span class="sig-name descname"><span class="pre">default_decompositions</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/export/exported_program.html#default_decompositions"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/export/exported_program.py#L335"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.export.exported_program.default_decompositions" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.export.exported_program.</span></span><span class="sig-name descname"><span class="pre">default_decompositions</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/export/exported_program.html#default_decompositions"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/export/exported_program.py#L335"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.export.exported_program.default_decompositions" title="Permalink to this definition">¶</a></dt>
 <dd><p>This is the default decomposition table which contains decomposition of
 all ATEN operators to core aten opset. Use this API together with
 <code class="xref py py-func docutils literal notranslate"><span class="pre">run_decompositions()</span></code></p>
@@ -1850,7 +1850,7 @@ <h2>Read More<a class="headerlink" href="#read-more" title="Permalink to this he
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.export.dims">
-<span class="sig-prename descclassname"><span class="pre">torch.export.</span></span><span class="sig-name descname"><span class="pre">dims</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">names</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/export/dynamic_shapes.html#dims"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/export/dynamic_shapes.py#L243"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.export.dims" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.export.</span></span><span class="sig-name descname"><span class="pre">dims</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">names</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/export/dynamic_shapes.html#dims"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/export/dynamic_shapes.py#L243"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.export.dims" title="Permalink to this definition">¶</a></dt>
 <dd><p>Util to create multiple <code class="xref py py-func docutils literal notranslate"><span class="pre">Dim()</span></code> types.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Returns</dt>
@@ -1864,7 +1864,7 @@ <h2>Read More<a class="headerlink" href="#read-more" title="Permalink to this he
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.export.dynamic_shapes.ShapesCollection">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.export.dynamic_shapes.</span></span><span class="sig-name descname"><span class="pre">ShapesCollection</span></span><a class="reference internal" href="_modules/torch/export/dynamic_shapes.html#ShapesCollection"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/export/dynamic_shapes.py#L601"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.export.dynamic_shapes.ShapesCollection" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.export.dynamic_shapes.</span></span><span class="sig-name descname"><span class="pre">ShapesCollection</span></span><a class="reference internal" href="_modules/torch/export/dynamic_shapes.html#ShapesCollection"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/export/dynamic_shapes.py#L601"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.export.dynamic_shapes.ShapesCollection" title="Permalink to this definition">¶</a></dt>
 <dd><p>Builder for dynamic_shapes.
 Used to assign dynamic shape specifications to tensors that appear in inputs.</p>
 <p>This is useful particularly when <code class="xref py py-func docutils literal notranslate"><span class="pre">args()</span></code> is a nested input structure, and it’s
@@ -1885,7 +1885,7 @@ <h2>Read More<a class="headerlink" href="#read-more" title="Permalink to this he
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.export.dynamic_shapes.ShapesCollection.dynamic_shapes">
-<span class="sig-name descname"><span class="pre">dynamic_shapes</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">m</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kwargs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/export/dynamic_shapes.html#ShapesCollection.dynamic_shapes"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/export/dynamic_shapes.py#L651"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.export.dynamic_shapes.ShapesCollection.dynamic_shapes" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">dynamic_shapes</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">m</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kwargs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/export/dynamic_shapes.html#ShapesCollection.dynamic_shapes"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/export/dynamic_shapes.py#L651"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.export.dynamic_shapes.ShapesCollection.dynamic_shapes" title="Permalink to this definition">¶</a></dt>
 <dd><p>Generates the <a class="reference internal" href="#torch.export.dynamic_shapes.ShapesCollection.dynamic_shapes" title="torch.export.dynamic_shapes.ShapesCollection.dynamic_shapes"><code class="xref py py-func docutils literal notranslate"><span class="pre">dynamic_shapes()</span></code></a> pytree structure according to <code class="xref py py-func docutils literal notranslate"><span class="pre">args()</span></code> and <code class="xref py py-func docutils literal notranslate"><span class="pre">kwargs()</span></code>.</p>
 </dd></dl>
 
@@ -1893,7 +1893,7 @@ <h2>Read More<a class="headerlink" href="#read-more" title="Permalink to this he
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.export.dynamic_shapes.refine_dynamic_shapes_from_suggested_fixes">
-<span class="sig-prename descclassname"><span class="pre">torch.export.dynamic_shapes.</span></span><span class="sig-name descname"><span class="pre">refine_dynamic_shapes_from_suggested_fixes</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">msg</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dynamic_shapes</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/export/dynamic_shapes.html#refine_dynamic_shapes_from_suggested_fixes"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/export/dynamic_shapes.py#L1003"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.export.dynamic_shapes.refine_dynamic_shapes_from_suggested_fixes" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.export.dynamic_shapes.</span></span><span class="sig-name descname"><span class="pre">refine_dynamic_shapes_from_suggested_fixes</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">msg</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dynamic_shapes</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/export/dynamic_shapes.html#refine_dynamic_shapes_from_suggested_fixes"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/export/dynamic_shapes.py#L1003"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.export.dynamic_shapes.refine_dynamic_shapes_from_suggested_fixes" title="Permalink to this definition">¶</a></dt>
 <dd><p>When exporting with <code class="xref py py-func docutils literal notranslate"><span class="pre">dynamic_shapes()</span></code>, export may fail with a ConstraintViolation error if the specification
 doesn’t match the constraints inferred from tracing the model. The error message may provide suggested fixes -
 changes that can be made to <code class="xref py py-func docutils literal notranslate"><span class="pre">dynamic_shapes()</span></code> to export successfully.</p>
@@ -1932,7 +1932,7 @@ <h2>Read More<a class="headerlink" href="#read-more" title="Permalink to this he
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.export.ExportedProgram">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.export.</span></span><span class="sig-name descname"><span class="pre">ExportedProgram</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">root</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">graph</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">graph_signature</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">state_dict</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">range_constraints</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">module_call_graph</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">example_inputs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">constants</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verifiers</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/export/exported_program.html#ExportedProgram"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/export/exported_program.py#L863"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.export.ExportedProgram" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.export.</span></span><span class="sig-name descname"><span class="pre">ExportedProgram</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">root</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">graph</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">graph_signature</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">state_dict</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">range_constraints</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">module_call_graph</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">example_inputs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">constants</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verifiers</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/export/exported_program.html#ExportedProgram"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/export/exported_program.py#L863"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.export.ExportedProgram" title="Permalink to this definition">¶</a></dt>
 <dd><p>Package of a program from <a class="reference internal" href="#torch.export.export" title="torch.export.export"><code class="xref py py-func docutils literal notranslate"><span class="pre">export()</span></code></a>. It contains
 an <a class="reference internal" href="fx.html#torch.fx.Graph" title="torch.fx.Graph"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.fx.Graph</span></code></a> that represents Tensor computation, a state_dict containing
 tensor values of all lifted parameters and buffers, and various metadata.</p>
@@ -1947,7 +1947,7 @@ <h2>Read More<a class="headerlink" href="#read-more" title="Permalink to this he
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.export.ExportedProgram.module">
-<span class="sig-name descname"><span class="pre">module</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/export/exported_program.html#ExportedProgram.module"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/export/exported_program.py#L1260"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.export.ExportedProgram.module" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">module</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/export/exported_program.html#ExportedProgram.module"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/export/exported_program.py#L1260"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.export.ExportedProgram.module" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns a self contained GraphModule with all the parameters/buffers inlined.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -1958,7 +1958,7 @@ <h2>Read More<a class="headerlink" href="#read-more" title="Permalink to this he
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.export.ExportedProgram.buffers">
-<span class="sig-name descname"><span class="pre">buffers</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/export/exported_program.html#ExportedProgram.buffers"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/export/exported_program.py#L975"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.export.ExportedProgram.buffers" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">buffers</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/export/exported_program.html#ExportedProgram.buffers"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/export/exported_program.py#L975"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.export.ExportedProgram.buffers" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns an iterator over original module buffers.</p>
 <div class="admonition warning">
 <p class="admonition-title">Warning</p>
@@ -1973,7 +1973,7 @@ <h2>Read More<a class="headerlink" href="#read-more" title="Permalink to this he
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.export.ExportedProgram.named_buffers">
-<span class="sig-name descname"><span class="pre">named_buffers</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/export/exported_program.html#ExportedProgram.named_buffers"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/export/exported_program.py#L983"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.export.ExportedProgram.named_buffers" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">named_buffers</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/export/exported_program.html#ExportedProgram.named_buffers"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/export/exported_program.py#L983"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.export.ExportedProgram.named_buffers" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns an iterator over original module buffers, yielding
 both the name of the buffer as well as the buffer itself.</p>
 <div class="admonition warning">
@@ -1989,7 +1989,7 @@ <h2>Read More<a class="headerlink" href="#read-more" title="Permalink to this he
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.export.ExportedProgram.parameters">
-<span class="sig-name descname"><span class="pre">parameters</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/export/exported_program.html#ExportedProgram.parameters"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/export/exported_program.py#L958"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.export.ExportedProgram.parameters" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">parameters</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/export/exported_program.html#ExportedProgram.parameters"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/export/exported_program.py#L958"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.export.ExportedProgram.parameters" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns an iterator over original module’s parameters.</p>
 <div class="admonition warning">
 <p class="admonition-title">Warning</p>
@@ -2004,7 +2004,7 @@ <h2>Read More<a class="headerlink" href="#read-more" title="Permalink to this he
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.export.ExportedProgram.named_parameters">
-<span class="sig-name descname"><span class="pre">named_parameters</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/export/exported_program.html#ExportedProgram.named_parameters"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/export/exported_program.py#L966"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.export.ExportedProgram.named_parameters" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">named_parameters</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/export/exported_program.html#ExportedProgram.named_parameters"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/export/exported_program.py#L966"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.export.ExportedProgram.named_parameters" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns an iterator over original module parameters, yielding
 both the name of the parameter as well as the parameter itself.</p>
 <div class="admonition warning">
@@ -2020,7 +2020,7 @@ <h2>Read More<a class="headerlink" href="#read-more" title="Permalink to this he
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.export.ExportedProgram.run_decompositions">
-<span class="sig-name descname"><span class="pre">run_decompositions</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">decomp_table</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">decompose_custom_triton_ops</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/export/exported_program.html#ExportedProgram.run_decompositions"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/export/exported_program.py#L1288"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.export.ExportedProgram.run_decompositions" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">run_decompositions</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">decomp_table</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">decompose_custom_triton_ops</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/export/exported_program.html#ExportedProgram.run_decompositions"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/export/exported_program.py#L1288"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.export.ExportedProgram.run_decompositions" title="Permalink to this definition">¶</a></dt>
 <dd><p>Run a set of decompositions on the exported program and returns a new
 exported program. By default we will run the Core ATen decompositions to
 get operators in the
@@ -2055,14 +2055,14 @@ <h2>Read More<a class="headerlink" href="#read-more" title="Permalink to this he
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.export.ExportBackwardSignature">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.export.</span></span><span class="sig-name descname"><span class="pre">ExportBackwardSignature</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">gradients_to_parameters</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.13)"><span class="pre">dict</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">gradients_to_user_inputs</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.13)"><span class="pre">dict</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">loss_output</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/export/graph_signature.html#ExportBackwardSignature"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/export/graph_signature.py#L146"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.export.ExportBackwardSignature" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.export.</span></span><span class="sig-name descname"><span class="pre">ExportBackwardSignature</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">gradients_to_parameters</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.13)"><span class="pre">dict</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">gradients_to_user_inputs</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.13)"><span class="pre">dict</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">loss_output</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/export/graph_signature.html#ExportBackwardSignature"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/export/graph_signature.py#L146"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.export.ExportBackwardSignature" title="Permalink to this definition">¶</a></dt>
 <dd><dl class="field-list simple">
 </dl>
 </dd></dl>
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.export.ExportGraphSignature">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.export.</span></span><span class="sig-name descname"><span class="pre">ExportGraphSignature</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_specs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_specs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/export/graph_signature.html#ExportGraphSignature"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/export/graph_signature.py#L153"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.export.ExportGraphSignature" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.export.</span></span><span class="sig-name descname"><span class="pre">ExportGraphSignature</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_specs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_specs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/export/graph_signature.html#ExportGraphSignature"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/export/graph_signature.py#L153"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.export.ExportGraphSignature" title="Permalink to this definition">¶</a></dt>
 <dd><p><a class="reference internal" href="#torch.export.ExportGraphSignature" title="torch.export.ExportGraphSignature"><code class="xref py py-class docutils literal notranslate"><span class="pre">ExportGraphSignature</span></code></a> models the input/output signature of Export Graph,
 which is a fx.Graph with stronger invariants gurantees.</p>
 <p>Export Graph is functional and does not access “states” like parameters
@@ -2135,21 +2135,21 @@ <h2>Read More<a class="headerlink" href="#read-more" title="Permalink to this he
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.export.ModuleCallSignature">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.export.</span></span><span class="sig-name descname"><span class="pre">ModuleCallSignature</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">inputs</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.13)"><span class="pre">list</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Union" title="(in Python v3.13)"><span class="pre">Union</span></a><span class="p"><span class="pre">[</span></span><span class="pre">torch.export.graph_signature.TensorArgument</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="#torch.export.graph_signature.SymIntArgument" title="torch.export.graph_signature.SymIntArgument"><span class="pre">torch.export.graph_signature.SymIntArgument</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="#torch.export.graph_signature.SymFloatArgument" title="torch.export.graph_signature.SymFloatArgument"><span class="pre">torch.export.graph_signature.SymFloatArgument</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="#torch.export.graph_signature.SymBoolArgument" title="torch.export.graph_signature.SymBoolArgument"><span class="pre">torch.export.graph_signature.SymBoolArgument</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">torch.export.graph_signature.ConstantArgument</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="#torch.export.graph_signature.CustomObjArgument" title="torch.export.graph_signature.CustomObjArgument"><span class="pre">torch.export.graph_signature.CustomObjArgument</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">torch.export.graph_signature.TokenArgument</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">outputs</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.13)"><span class="pre">list</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Union" title="(in Python v3.13)"><span class="pre">Union</span></a><span class="p"><span class="pre">[</span></span><span class="pre">torch.export.graph_signature.TensorArgument</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="#torch.export.graph_signature.SymIntArgument" title="torch.export.graph_signature.SymIntArgument"><span class="pre">torch.export.graph_signature.SymIntArgument</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="#torch.export.graph_signature.SymFloatArgument" title="torch.export.graph_signature.SymFloatArgument"><span class="pre">torch.export.graph_signature.SymFloatArgument</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="#torch.export.graph_signature.SymBoolArgument" title="torch.export.graph_signature.SymBoolArgument"><span class="pre">torch.export.graph_signature.SymBoolArgument</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">torch.export.graph_signature.ConstantArgument</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="#torch.export.graph_signature.CustomObjArgument" title="torch.export.graph_signature.CustomObjArgument"><span class="pre">torch.export.graph_signature.CustomObjArgument</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">torch.export.graph_signature.TokenArgument</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">in_spec</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.utils._pytree.TreeSpec</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out_spec</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.utils._pytree.TreeSpec</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">forward_arg_names</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.13)"><span class="pre">list</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/export/exported_program.html#ModuleCallSignature"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/export/exported_program.py#L90"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.export.ModuleCallSignature" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.export.</span></span><span class="sig-name descname"><span class="pre">ModuleCallSignature</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">inputs</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.13)"><span class="pre">list</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Union" title="(in Python v3.13)"><span class="pre">Union</span></a><span class="p"><span class="pre">[</span></span><span class="pre">torch.export.graph_signature.TensorArgument</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="#torch.export.graph_signature.SymIntArgument" title="torch.export.graph_signature.SymIntArgument"><span class="pre">torch.export.graph_signature.SymIntArgument</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="#torch.export.graph_signature.SymFloatArgument" title="torch.export.graph_signature.SymFloatArgument"><span class="pre">torch.export.graph_signature.SymFloatArgument</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="#torch.export.graph_signature.SymBoolArgument" title="torch.export.graph_signature.SymBoolArgument"><span class="pre">torch.export.graph_signature.SymBoolArgument</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">torch.export.graph_signature.ConstantArgument</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="#torch.export.graph_signature.CustomObjArgument" title="torch.export.graph_signature.CustomObjArgument"><span class="pre">torch.export.graph_signature.CustomObjArgument</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">torch.export.graph_signature.TokenArgument</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">outputs</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.13)"><span class="pre">list</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Union" title="(in Python v3.13)"><span class="pre">Union</span></a><span class="p"><span class="pre">[</span></span><span class="pre">torch.export.graph_signature.TensorArgument</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="#torch.export.graph_signature.SymIntArgument" title="torch.export.graph_signature.SymIntArgument"><span class="pre">torch.export.graph_signature.SymIntArgument</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="#torch.export.graph_signature.SymFloatArgument" title="torch.export.graph_signature.SymFloatArgument"><span class="pre">torch.export.graph_signature.SymFloatArgument</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="#torch.export.graph_signature.SymBoolArgument" title="torch.export.graph_signature.SymBoolArgument"><span class="pre">torch.export.graph_signature.SymBoolArgument</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">torch.export.graph_signature.ConstantArgument</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="#torch.export.graph_signature.CustomObjArgument" title="torch.export.graph_signature.CustomObjArgument"><span class="pre">torch.export.graph_signature.CustomObjArgument</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">torch.export.graph_signature.TokenArgument</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">in_spec</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.utils._pytree.TreeSpec</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out_spec</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.utils._pytree.TreeSpec</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">forward_arg_names</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.13)"><span class="pre">list</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/export/exported_program.html#ModuleCallSignature"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/export/exported_program.py#L90"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.export.ModuleCallSignature" title="Permalink to this definition">¶</a></dt>
 <dd><dl class="field-list simple">
 </dl>
 </dd></dl>
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.export.ModuleCallEntry">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.export.</span></span><span class="sig-name descname"><span class="pre">ModuleCallEntry</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">fqn</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">signature</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="#torch.export.ModuleCallSignature" title="torch.export.exported_program.ModuleCallSignature"><span class="pre">torch.export.exported_program.ModuleCallSignature</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/export/exported_program.html#ModuleCallEntry"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/export/exported_program.py#L107"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.export.ModuleCallEntry" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.export.</span></span><span class="sig-name descname"><span class="pre">ModuleCallEntry</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">fqn</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">signature</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="#torch.export.ModuleCallSignature" title="torch.export.exported_program.ModuleCallSignature"><span class="pre">torch.export.exported_program.ModuleCallSignature</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/export/exported_program.html#ModuleCallEntry"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/export/exported_program.py#L107"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.export.ModuleCallEntry" title="Permalink to this definition">¶</a></dt>
 <dd><dl class="field-list simple">
 </dl>
 </dd></dl>
 
 <span class="target" id="module-torch.export.decomp_utils"></span><dl class="py class">
 <dt class="sig sig-object py" id="torch.export.decomp_utils.CustomDecompTable">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.export.decomp_utils.</span></span><span class="sig-name descname"><span class="pre">CustomDecompTable</span></span><a class="reference internal" href="_modules/torch/export/decomp_utils.html#CustomDecompTable"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/export/decomp_utils.py#L16"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.export.decomp_utils.CustomDecompTable" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.export.decomp_utils.</span></span><span class="sig-name descname"><span class="pre">CustomDecompTable</span></span><a class="reference internal" href="_modules/torch/export/decomp_utils.html#CustomDecompTable"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/export/decomp_utils.py#L16"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.export.decomp_utils.CustomDecompTable" title="Permalink to this definition">¶</a></dt>
 <dd><p>This is a custom dictionary that is specifically used for handling decomp_table in export.
 The reason we need this is because in the new world, you can only <em>delete</em> an op from decomp
 table to preserve it. This is problematic for custom ops because we don’t know when the custom
@@ -2167,7 +2167,7 @@ <h2>Read More<a class="headerlink" href="#read-more" title="Permalink to this he
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.export.decomp_utils.CustomDecompTable.copy">
-<span class="sig-name descname"><span class="pre">copy</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/export/decomp_utils.html#CustomDecompTable.copy"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/export/decomp_utils.py#L88"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.export.decomp_utils.CustomDecompTable.copy" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">copy</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/export/decomp_utils.html#CustomDecompTable.copy"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/export/decomp_utils.py#L88"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.export.decomp_utils.CustomDecompTable.copy" title="Permalink to this definition">¶</a></dt>
 <dd><dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
 <dd class="field-odd"><p><a class="reference internal" href="#torch.export.decomp_utils.CustomDecompTable" title="torch.export.decomp_utils.CustomDecompTable"><em>CustomDecompTable</em></a></p>
@@ -2177,17 +2177,17 @@ <h2>Read More<a class="headerlink" href="#read-more" title="Permalink to this he
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.export.decomp_utils.CustomDecompTable.items">
-<span class="sig-name descname"><span class="pre">items</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/export/decomp_utils.html#CustomDecompTable.items"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/export/decomp_utils.py#L125"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.export.decomp_utils.CustomDecompTable.items" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">items</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/export/decomp_utils.html#CustomDecompTable.items"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/export/decomp_utils.py#L125"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.export.decomp_utils.CustomDecompTable.items" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.export.decomp_utils.CustomDecompTable.keys">
-<span class="sig-name descname"><span class="pre">keys</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/export/decomp_utils.html#CustomDecompTable.keys"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/export/decomp_utils.py#L58"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.export.decomp_utils.CustomDecompTable.keys" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">keys</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/export/decomp_utils.html#CustomDecompTable.keys"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/export/decomp_utils.py#L58"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.export.decomp_utils.CustomDecompTable.keys" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.export.decomp_utils.CustomDecompTable.materialize">
-<span class="sig-name descname"><span class="pre">materialize</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/export/decomp_utils.html#CustomDecompTable.materialize"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/export/decomp_utils.py#L129"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.export.decomp_utils.CustomDecompTable.materialize" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">materialize</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/export/decomp_utils.html#CustomDecompTable.materialize"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/export/decomp_utils.py#L129"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.export.decomp_utils.CustomDecompTable.materialize" title="Permalink to this definition">¶</a></dt>
 <dd><dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
 <dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.13)">dict</a>[torch._ops.OperatorBase, <a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Callable" title="(in Python v3.13)"><em>Callable</em></a>]</p>
@@ -2197,66 +2197,66 @@ <h2>Read More<a class="headerlink" href="#read-more" title="Permalink to this he
 
 <dl class="py method">
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-<span class="sig-name descname"><span class="pre">pop</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/export/decomp_utils.html#CustomDecompTable.pop"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/export/decomp_utils.py#L95"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.export.decomp_utils.CustomDecompTable.pop" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">pop</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/export/decomp_utils.html#CustomDecompTable.pop"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/export/decomp_utils.py#L95"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.export.decomp_utils.CustomDecompTable.pop" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.export.decomp_utils.CustomDecompTable.update">
-<span class="sig-name descname"><span class="pre">update</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">other_dict</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/export/decomp_utils.html#CustomDecompTable.update"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/export/decomp_utils.py#L65"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.export.decomp_utils.CustomDecompTable.update" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">update</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">other_dict</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/export/decomp_utils.html#CustomDecompTable.update"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/export/decomp_utils.py#L65"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.export.decomp_utils.CustomDecompTable.update" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 </dd></dl>
 
 <span class="target" id="module-torch.export.exported_program"></span><span class="target" id="module-torch.export.graph_signature"></span><dl class="py class">
 <dt class="sig sig-object py" id="torch.export.graph_signature.InputKind">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.export.graph_signature.</span></span><span class="sig-name descname"><span class="pre">InputKind</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/export/graph_signature.html#InputKind"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/export/graph_signature.py#L80"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.export.graph_signature.InputKind" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.export.graph_signature.</span></span><span class="sig-name descname"><span class="pre">InputKind</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/export/graph_signature.html#InputKind"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/export/graph_signature.py#L80"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.export.graph_signature.InputKind" title="Permalink to this definition">¶</a></dt>
 <dd><p>An enumeration.</p>
 </dd></dl>
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.export.graph_signature.InputSpec">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.export.graph_signature.</span></span><span class="sig-name descname"><span class="pre">InputSpec</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">kind</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="#torch.export.graph_signature.InputKind" title="torch.export.graph_signature.InputKind"><span class="pre">torch.export.graph_signature.InputKind</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">arg</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.export.graph_signature.TensorArgument</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="#torch.export.graph_signature.SymIntArgument" title="torch.export.graph_signature.SymIntArgument"><span class="pre">torch.export.graph_signature.SymIntArgument</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="#torch.export.graph_signature.SymFloatArgument" title="torch.export.graph_signature.SymFloatArgument"><span class="pre">torch.export.graph_signature.SymFloatArgument</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="#torch.export.graph_signature.SymBoolArgument" title="torch.export.graph_signature.SymBoolArgument"><span class="pre">torch.export.graph_signature.SymBoolArgument</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">torch.export.graph_signature.ConstantArgument</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="#torch.export.graph_signature.CustomObjArgument" title="torch.export.graph_signature.CustomObjArgument"><span class="pre">torch.export.graph_signature.CustomObjArgument</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">torch.export.graph_signature.TokenArgument</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">persistent</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/export/graph_signature.html#InputSpec"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/export/graph_signature.py#L89"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.export.graph_signature.InputSpec" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.export.graph_signature.</span></span><span class="sig-name descname"><span class="pre">InputSpec</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">kind</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="#torch.export.graph_signature.InputKind" title="torch.export.graph_signature.InputKind"><span class="pre">torch.export.graph_signature.InputKind</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">arg</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.export.graph_signature.TensorArgument</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="#torch.export.graph_signature.SymIntArgument" title="torch.export.graph_signature.SymIntArgument"><span class="pre">torch.export.graph_signature.SymIntArgument</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="#torch.export.graph_signature.SymFloatArgument" title="torch.export.graph_signature.SymFloatArgument"><span class="pre">torch.export.graph_signature.SymFloatArgument</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="#torch.export.graph_signature.SymBoolArgument" title="torch.export.graph_signature.SymBoolArgument"><span class="pre">torch.export.graph_signature.SymBoolArgument</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">torch.export.graph_signature.ConstantArgument</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="#torch.export.graph_signature.CustomObjArgument" title="torch.export.graph_signature.CustomObjArgument"><span class="pre">torch.export.graph_signature.CustomObjArgument</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">torch.export.graph_signature.TokenArgument</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">persistent</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/export/graph_signature.html#InputSpec"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/export/graph_signature.py#L89"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.export.graph_signature.InputSpec" title="Permalink to this definition">¶</a></dt>
 <dd><dl class="field-list simple">
 </dl>
 </dd></dl>
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.export.graph_signature.OutputKind">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.export.graph_signature.</span></span><span class="sig-name descname"><span class="pre">OutputKind</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/export/graph_signature.html#OutputKind"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/export/graph_signature.py#L115"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.export.graph_signature.OutputKind" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.export.graph_signature.</span></span><span class="sig-name descname"><span class="pre">OutputKind</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/export/graph_signature.html#OutputKind"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/export/graph_signature.py#L115"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.export.graph_signature.OutputKind" title="Permalink to this definition">¶</a></dt>
 <dd><p>An enumeration.</p>
 </dd></dl>
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.export.graph_signature.OutputSpec">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.export.graph_signature.</span></span><span class="sig-name descname"><span class="pre">OutputSpec</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">kind</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="#torch.export.graph_signature.OutputKind" title="torch.export.graph_signature.OutputKind"><span class="pre">torch.export.graph_signature.OutputKind</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">arg</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.export.graph_signature.TensorArgument</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="#torch.export.graph_signature.SymIntArgument" title="torch.export.graph_signature.SymIntArgument"><span class="pre">torch.export.graph_signature.SymIntArgument</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="#torch.export.graph_signature.SymFloatArgument" title="torch.export.graph_signature.SymFloatArgument"><span class="pre">torch.export.graph_signature.SymFloatArgument</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="#torch.export.graph_signature.SymBoolArgument" title="torch.export.graph_signature.SymBoolArgument"><span class="pre">torch.export.graph_signature.SymBoolArgument</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">torch.export.graph_signature.ConstantArgument</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="#torch.export.graph_signature.CustomObjArgument" title="torch.export.graph_signature.CustomObjArgument"><span class="pre">torch.export.graph_signature.CustomObjArgument</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">torch.export.graph_signature.TokenArgument</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a><span class="p"><span class="pre">]</span></span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/export/graph_signature.html#OutputSpec"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/export/graph_signature.py#L125"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.export.graph_signature.OutputSpec" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.export.graph_signature.</span></span><span class="sig-name descname"><span class="pre">OutputSpec</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">kind</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="#torch.export.graph_signature.OutputKind" title="torch.export.graph_signature.OutputKind"><span class="pre">torch.export.graph_signature.OutputKind</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">arg</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.export.graph_signature.TensorArgument</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="#torch.export.graph_signature.SymIntArgument" title="torch.export.graph_signature.SymIntArgument"><span class="pre">torch.export.graph_signature.SymIntArgument</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="#torch.export.graph_signature.SymFloatArgument" title="torch.export.graph_signature.SymFloatArgument"><span class="pre">torch.export.graph_signature.SymFloatArgument</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="#torch.export.graph_signature.SymBoolArgument" title="torch.export.graph_signature.SymBoolArgument"><span class="pre">torch.export.graph_signature.SymBoolArgument</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">torch.export.graph_signature.ConstantArgument</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="#torch.export.graph_signature.CustomObjArgument" title="torch.export.graph_signature.CustomObjArgument"><span class="pre">torch.export.graph_signature.CustomObjArgument</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">torch.export.graph_signature.TokenArgument</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a><span class="p"><span class="pre">]</span></span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/export/graph_signature.html#OutputSpec"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/export/graph_signature.py#L125"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.export.graph_signature.OutputSpec" title="Permalink to this definition">¶</a></dt>
 <dd><dl class="field-list simple">
 </dl>
 </dd></dl>
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.export.graph_signature.SymIntArgument">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.export.graph_signature.</span></span><span class="sig-name descname"><span class="pre">SymIntArgument</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/export/graph_signature.html#SymIntArgument"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/export/graph_signature.py#L41"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.export.graph_signature.SymIntArgument" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.export.graph_signature.</span></span><span class="sig-name descname"><span class="pre">SymIntArgument</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/export/graph_signature.html#SymIntArgument"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/export/graph_signature.py#L41"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.export.graph_signature.SymIntArgument" title="Permalink to this definition">¶</a></dt>
 <dd><dl class="field-list simple">
 </dl>
 </dd></dl>
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.export.graph_signature.SymBoolArgument">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.export.graph_signature.</span></span><span class="sig-name descname"><span class="pre">SymBoolArgument</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/export/graph_signature.html#SymBoolArgument"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/export/graph_signature.py#L51"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.export.graph_signature.SymBoolArgument" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.export.graph_signature.</span></span><span class="sig-name descname"><span class="pre">SymBoolArgument</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/export/graph_signature.html#SymBoolArgument"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/export/graph_signature.py#L51"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.export.graph_signature.SymBoolArgument" title="Permalink to this definition">¶</a></dt>
 <dd><dl class="field-list simple">
 </dl>
 </dd></dl>
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.export.graph_signature.SymFloatArgument">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.export.graph_signature.</span></span><span class="sig-name descname"><span class="pre">SymFloatArgument</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/export/graph_signature.html#SymFloatArgument"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/export/graph_signature.py#L46"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.export.graph_signature.SymFloatArgument" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.export.graph_signature.</span></span><span class="sig-name descname"><span class="pre">SymFloatArgument</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/export/graph_signature.html#SymFloatArgument"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/export/graph_signature.py#L46"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.export.graph_signature.SymFloatArgument" title="Permalink to this definition">¶</a></dt>
 <dd><dl class="field-list simple">
 </dl>
 </dd></dl>
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.export.graph_signature.ExportGraphSignature">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.export.graph_signature.</span></span><span class="sig-name descname"><span class="pre">ExportGraphSignature</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_specs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_specs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/export/graph_signature.html#ExportGraphSignature"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/export/graph_signature.py#L153"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.export.graph_signature.ExportGraphSignature" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.export.graph_signature.</span></span><span class="sig-name descname"><span class="pre">ExportGraphSignature</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_specs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_specs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/export/graph_signature.html#ExportGraphSignature"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/export/graph_signature.py#L153"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.export.graph_signature.ExportGraphSignature" title="Permalink to this definition">¶</a></dt>
 <dd><p><a class="reference internal" href="#torch.export.graph_signature.ExportGraphSignature" title="torch.export.graph_signature.ExportGraphSignature"><code class="xref py py-class docutils literal notranslate"><span class="pre">ExportGraphSignature</span></code></a> models the input/output signature of Export Graph,
 which is a fx.Graph with stronger invariants gurantees.</p>
 <p>Export Graph is functional and does not access “states” like parameters
@@ -2327,7 +2327,7 @@ <h2>Read More<a class="headerlink" href="#read-more" title="Permalink to this he
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.export.graph_signature.ExportGraphSignature.replace_all_uses">
-<span class="sig-name descname"><span class="pre">replace_all_uses</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">old</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">new</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/export/graph_signature.html#ExportGraphSignature.replace_all_uses"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/export/graph_signature.py#L458"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.export.graph_signature.ExportGraphSignature.replace_all_uses" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">replace_all_uses</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">old</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">new</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/export/graph_signature.html#ExportGraphSignature.replace_all_uses"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/export/graph_signature.py#L458"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.export.graph_signature.ExportGraphSignature.replace_all_uses" title="Permalink to this definition">¶</a></dt>
 <dd><p>Replace all uses of the old name with new name in the signature.</p>
 <dl class="field-list simple">
 </dl>
@@ -2335,25 +2335,25 @@ <h2>Read More<a class="headerlink" href="#read-more" title="Permalink to this he
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.export.graph_signature.ExportGraphSignature.get_replace_hook">
-<span class="sig-name descname"><span class="pre">get_replace_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">replace_inputs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/export/graph_signature.html#ExportGraphSignature.get_replace_hook"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/export/graph_signature.py#L481"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.export.graph_signature.ExportGraphSignature.get_replace_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">get_replace_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">replace_inputs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/export/graph_signature.html#ExportGraphSignature.get_replace_hook"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/export/graph_signature.py#L481"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.export.graph_signature.ExportGraphSignature.get_replace_hook" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 </dd></dl>
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.export.graph_signature.CustomObjArgument">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.export.graph_signature.</span></span><span class="sig-name descname"><span class="pre">CustomObjArgument</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">class_fqn</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">fake_val</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch._library.fake_class_registry.FakeScriptObject</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/export/graph_signature.html#CustomObjArgument"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/export/graph_signature.py#L56"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.export.graph_signature.CustomObjArgument" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.export.graph_signature.</span></span><span class="sig-name descname"><span class="pre">CustomObjArgument</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">class_fqn</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">fake_val</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">torch._library.fake_class_registry.FakeScriptObject</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/export/graph_signature.html#CustomObjArgument"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/export/graph_signature.py#L56"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.export.graph_signature.CustomObjArgument" title="Permalink to this definition">¶</a></dt>
 <dd><dl class="field-list simple">
 </dl>
 </dd></dl>
 
 <span class="target" id="module-torch.export.dynamic_shapes"></span><span class="target" id="module-torch.export.unflatten"></span><dl class="py class">
 <dt class="sig sig-object py" id="torch.export.unflatten.FlatArgsAdapter">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.export.unflatten.</span></span><span class="sig-name descname"><span class="pre">FlatArgsAdapter</span></span><a class="reference internal" href="_modules/torch/export/unflatten.html#FlatArgsAdapter"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/export/unflatten.py#L266"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.export.unflatten.FlatArgsAdapter" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.export.unflatten.</span></span><span class="sig-name descname"><span class="pre">FlatArgsAdapter</span></span><a class="reference internal" href="_modules/torch/export/unflatten.html#FlatArgsAdapter"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/export/unflatten.py#L266"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.export.unflatten.FlatArgsAdapter" title="Permalink to this definition">¶</a></dt>
 <dd><p>Adapts input arguments with <code class="docutils literal notranslate"><span class="pre">input_spec</span></code> to align <code class="docutils literal notranslate"><span class="pre">target_spec</span></code>.</p>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.export.unflatten.FlatArgsAdapter.adapt">
-<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">adapt</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">target_spec</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_spec</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_args</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/export/unflatten.html#FlatArgsAdapter.adapt"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/export/unflatten.py#L271"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.export.unflatten.FlatArgsAdapter.adapt" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">adapt</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">target_spec</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_spec</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_args</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/export/unflatten.html#FlatArgsAdapter.adapt"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/export/unflatten.py#L271"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.export.unflatten.FlatArgsAdapter.adapt" title="Permalink to this definition">¶</a></dt>
 <dd><p>NOTE: This adapter may mutate given <code class="docutils literal notranslate"><span class="pre">input_args_with_path</span></code>.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -2366,7 +2366,7 @@ <h2>Read More<a class="headerlink" href="#read-more" title="Permalink to this he
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.export.unflatten.InterpreterModule">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.export.unflatten.</span></span><span class="sig-name descname"><span class="pre">InterpreterModule</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">graph</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ty</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/export/unflatten.html#InterpreterModule"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/export/unflatten.py#L130"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.export.unflatten.InterpreterModule" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.export.unflatten.</span></span><span class="sig-name descname"><span class="pre">InterpreterModule</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">graph</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ty</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/export/unflatten.html#InterpreterModule"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/export/unflatten.py#L130"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.export.unflatten.InterpreterModule" title="Permalink to this definition">¶</a></dt>
 <dd><p>A module that uses torch.fx.Interpreter to execute instead of the usual
 codegen that GraphModule uses. This provides better stack trace information
 and makes it easier to debug execution.</p>
@@ -2376,7 +2376,7 @@ <h2>Read More<a class="headerlink" href="#read-more" title="Permalink to this he
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.export.unflatten.InterpreterModuleDispatcher">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.export.unflatten.</span></span><span class="sig-name descname"><span class="pre">InterpreterModuleDispatcher</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">attrs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">call_modules</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/export/unflatten.html#InterpreterModuleDispatcher"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/export/unflatten.py#L218"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.export.unflatten.InterpreterModuleDispatcher" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.export.unflatten.</span></span><span class="sig-name descname"><span class="pre">InterpreterModuleDispatcher</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">attrs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">call_modules</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/export/unflatten.html#InterpreterModuleDispatcher"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/export/unflatten.py#L218"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.export.unflatten.InterpreterModuleDispatcher" title="Permalink to this definition">¶</a></dt>
 <dd><p>A module that carries a sequence of InterpreterModules corresponding to
 a sequence of calls of that module. Each call to the module dispatches
 to the next InterpreterModule, and wraps back around after the last.</p>
@@ -2386,7 +2386,7 @@ <h2>Read More<a class="headerlink" href="#read-more" title="Permalink to this he
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.export.unflatten.unflatten">
-<span class="sig-prename descclassname"><span class="pre">torch.export.unflatten.</span></span><span class="sig-name descname"><span class="pre">unflatten</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">flat_args_adapter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/export/unflatten.html#unflatten"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/export/unflatten.py#L682"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.export.unflatten.unflatten" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.export.unflatten.</span></span><span class="sig-name descname"><span class="pre">unflatten</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">flat_args_adapter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/export/unflatten.html#unflatten"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/export/unflatten.py#L682"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.export.unflatten.unflatten" title="Permalink to this definition">¶</a></dt>
 <dd><p>Unflatten an ExportedProgram, producing a module with the same module
 hierarchy as the original eager module. This can be useful if you are trying
 to use <a class="reference internal" href="#module-torch.export" title="torch.export"><code class="xref py py-mod docutils literal notranslate"><span class="pre">torch.export</span></code></a> with another system that expects a module
@@ -2418,7 +2418,7 @@ <h2>Read More<a class="headerlink" href="#read-more" title="Permalink to this he
 
 <span class="target" id="module-torch.export.custom_obj"></span><span class="target" id="module-torch.export.experimental"></span><span class="target" id="module-torch.export.passes"></span><dl class="py function">
 <dt class="sig sig-object py" id="torch.export.passes.move_to_device_pass">
-<span class="sig-prename descclassname"><span class="pre">torch.export.passes.</span></span><span class="sig-name descname"><span class="pre">move_to_device_pass</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">ep</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">location</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/export/passes.html#move_to_device_pass"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/export/passes/__init__.py#L11"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.export.passes.move_to_device_pass" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.export.passes.</span></span><span class="sig-name descname"><span class="pre">move_to_device_pass</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">ep</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">location</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/export/passes.html#move_to_device_pass"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/export/passes/__init__.py#L11"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.export.passes.move_to_device_pass" title="Permalink to this definition">¶</a></dt>
 <dd><p>Move the exported program to the given device.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/export.ir_spec.html b/main/export.ir_spec.html
index 4fc88a8ecd4..2e71a9cf840 100644
--- a/main/export.ir_spec.html
+++ b/main/export.ir_spec.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/export.programming_model.html b/main/export.programming_model.html
index 7cb3c2465ec..7100123ef16 100644
--- a/main/export.programming_model.html
+++ b/main/export.programming_model.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/fft.html b/main/fft.html
index fd765c57c83..3cbd9e247bd 100644
--- a/main/fft.html
+++ b/main/fft.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/fsdp.html b/main/fsdp.html
index 3702e56faea..d3379744846 100644
--- a/main/fsdp.html
+++ b/main/fsdp.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -610,7 +610,7 @@
 <span id="fullyshardeddataparallel"></span><h1>FullyShardedDataParallel<a class="headerlink" href="#module-torch.distributed.fsdp" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributed.fsdp.FullyShardedDataParallel">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.fsdp.</span></span><span class="sig-name descname"><span class="pre">FullyShardedDataParallel</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">process_group</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sharding_strategy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cpu_offload</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">auto_wrap_policy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">backward_prefetch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">BackwardPrefetch.BACKWARD_PRE</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mixed_precision</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ignored_modules</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">param_init_fn</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device_id</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sync_module_states</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">forward_prefetch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">limit_all_gathers</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_orig_params</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ignored_states</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device_mesh</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/fsdp/fully_sharded_data_parallel.html#FullyShardedDataParallel"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/fsdp/fully_sharded_data_parallel.py#L117"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.fsdp.FullyShardedDataParallel" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.fsdp.</span></span><span class="sig-name descname"><span class="pre">FullyShardedDataParallel</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">process_group</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sharding_strategy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cpu_offload</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">auto_wrap_policy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">backward_prefetch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">BackwardPrefetch.BACKWARD_PRE</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mixed_precision</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ignored_modules</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">param_init_fn</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device_id</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sync_module_states</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">forward_prefetch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">limit_all_gathers</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_orig_params</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ignored_states</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device_mesh</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/fsdp/fully_sharded_data_parallel.html#FullyShardedDataParallel"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/fsdp/fully_sharded_data_parallel.py#L117"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.fsdp.FullyShardedDataParallel" title="Permalink to this definition">¶</a></dt>
 <dd><p>A wrapper for sharding module parameters across data parallel workers.</p>
 <p>This is inspired by <a class="reference external" href="https://arxiv.org/abs/2004.13336">Xu et al.</a> as well as the ZeRO Stage 3 from <a class="reference external" href="https://www.deepspeed.ai/">DeepSpeed</a>.
 FullyShardedDataParallel is commonly shortened to FSDP.</p>
@@ -868,7 +868,7 @@
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.fsdp.FullyShardedDataParallel.apply">
-<span class="sig-name descname"><span class="pre">apply</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">fn</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/fsdp/fully_sharded_data_parallel.html#FullyShardedDataParallel.apply"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/fsdp/fully_sharded_data_parallel.py#L575"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.fsdp.FullyShardedDataParallel.apply" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">apply</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">fn</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/fsdp/fully_sharded_data_parallel.html#FullyShardedDataParallel.apply"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/fsdp/fully_sharded_data_parallel.py#L575"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.fsdp.FullyShardedDataParallel.apply" title="Permalink to this definition">¶</a></dt>
 <dd><p>Apply <code class="docutils literal notranslate"><span class="pre">fn</span></code> recursively to every submodule (as returned by <code class="docutils literal notranslate"><span class="pre">.children()</span></code>) as well as self.</p>
 <p>Typical use includes initializing the parameters of a model (see also <a class="reference internal" href="nn.init.html#nn-init-doc"><span class="std std-ref">torch.nn.init</span></a>).</p>
 <p>Compared to <code class="docutils literal notranslate"><span class="pre">torch.nn.Module.apply</span></code>, this version additionally gathers
@@ -889,7 +889,7 @@
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.fsdp.FullyShardedDataParallel.check_is_root">
-<span class="sig-name descname"><span class="pre">check_is_root</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/fsdp/fully_sharded_data_parallel.html#FullyShardedDataParallel.check_is_root"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/fsdp/fully_sharded_data_parallel.py#L548"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.fsdp.FullyShardedDataParallel.check_is_root" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">check_is_root</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/fsdp/fully_sharded_data_parallel.html#FullyShardedDataParallel.check_is_root"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/fsdp/fully_sharded_data_parallel.py#L548"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.fsdp.FullyShardedDataParallel.check_is_root" title="Permalink to this definition">¶</a></dt>
 <dd><p>Check if this instance is a root FSDP module.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -900,7 +900,7 @@
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.fsdp.FullyShardedDataParallel.clip_grad_norm_">
-<span class="sig-name descname"><span class="pre">clip_grad_norm_</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">max_norm</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">norm_type</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2.0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/fsdp/fully_sharded_data_parallel.html#FullyShardedDataParallel.clip_grad_norm_"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/fsdp/fully_sharded_data_parallel.py#L1066"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.fsdp.FullyShardedDataParallel.clip_grad_norm_" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">clip_grad_norm_</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">max_norm</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">norm_type</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2.0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/fsdp/fully_sharded_data_parallel.html#FullyShardedDataParallel.clip_grad_norm_"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/fsdp/fully_sharded_data_parallel.py#L1066"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.fsdp.FullyShardedDataParallel.clip_grad_norm_" title="Permalink to this definition">¶</a></dt>
 <dd><p>Clip the gradient norm of all parameters.</p>
 <p>The norm is computed over all parameters’ gradients as viewed as a single vector, and the
 gradients are modified in-place.</p>
@@ -941,7 +941,7 @@
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.fsdp.FullyShardedDataParallel.flatten_sharded_optim_state_dict">
-<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">flatten_sharded_optim_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sharded_optim_state_dict</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">optim</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/fsdp/fully_sharded_data_parallel.html#FullyShardedDataParallel.flatten_sharded_optim_state_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/fsdp/fully_sharded_data_parallel.py#L1553"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.fsdp.FullyShardedDataParallel.flatten_sharded_optim_state_dict" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">flatten_sharded_optim_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sharded_optim_state_dict</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">optim</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/fsdp/fully_sharded_data_parallel.html#FullyShardedDataParallel.flatten_sharded_optim_state_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/fsdp/fully_sharded_data_parallel.py#L1553"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.fsdp.FullyShardedDataParallel.flatten_sharded_optim_state_dict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Flatten a sharded optimizer state-dict.</p>
 <p>The API is similar to <a class="reference internal" href="#torch.distributed.fsdp.FullyShardedDataParallel.shard_full_optim_state_dict" title="torch.distributed.fsdp.FullyShardedDataParallel.shard_full_optim_state_dict"><code class="xref py py-meth docutils literal notranslate"><span class="pre">shard_full_optim_state_dict()</span></code></a>. The only
 difference is that the input <code class="docutils literal notranslate"><span class="pre">sharded_optim_state_dict</span></code> should be
@@ -969,7 +969,7 @@
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.fsdp.FullyShardedDataParallel.forward">
-<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/fsdp/fully_sharded_data_parallel.html#FullyShardedDataParallel.forward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/fsdp/fully_sharded_data_parallel.py#L832"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.fsdp.FullyShardedDataParallel.forward" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/fsdp/fully_sharded_data_parallel.html#FullyShardedDataParallel.forward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/fsdp/fully_sharded_data_parallel.py#L832"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.fsdp.FullyShardedDataParallel.forward" title="Permalink to this definition">¶</a></dt>
 <dd><p>Run the forward pass for the wrapped module, inserting FSDP-specific pre- and post-forward sharding logic.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -980,7 +980,7 @@
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.fsdp.FullyShardedDataParallel.fsdp_modules">
-<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">fsdp_modules</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">root_only</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/fsdp/fully_sharded_data_parallel.html#FullyShardedDataParallel.fsdp_modules"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/fsdp/fully_sharded_data_parallel.py#L552"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.fsdp.FullyShardedDataParallel.fsdp_modules" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">fsdp_modules</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">root_only</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/fsdp/fully_sharded_data_parallel.html#FullyShardedDataParallel.fsdp_modules"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/fsdp/fully_sharded_data_parallel.py#L552"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.fsdp.FullyShardedDataParallel.fsdp_modules" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return all nested FSDP instances.</p>
 <p>This possibly includes <code class="docutils literal notranslate"><span class="pre">module</span></code> itself and only includes FSDP root modules if <code class="docutils literal notranslate"><span class="pre">root_only=True</span></code>.</p>
 <dl class="field-list simple">
@@ -1004,7 +1004,7 @@
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.fsdp.FullyShardedDataParallel.full_optim_state_dict">
-<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">full_optim_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">optim</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">optim_input</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">rank0_only</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/fsdp/fully_sharded_data_parallel.html#FullyShardedDataParallel.full_optim_state_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/fsdp/fully_sharded_data_parallel.py#L1364"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.fsdp.FullyShardedDataParallel.full_optim_state_dict" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">full_optim_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">optim</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">optim_input</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">rank0_only</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/fsdp/fully_sharded_data_parallel.html#FullyShardedDataParallel.full_optim_state_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/fsdp/fully_sharded_data_parallel.py#L1364"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.fsdp.FullyShardedDataParallel.full_optim_state_dict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the full optimizer state-dict.</p>
 <p>Consolidates the full optimizer state on rank 0 and returns it
 as a <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.13)"><code class="xref py py-class docutils literal notranslate"><span class="pre">dict</span></code></a> following the convention of
@@ -1057,7 +1057,7 @@
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.fsdp.FullyShardedDataParallel.get_state_dict_type">
-<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">get_state_dict_type</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/fsdp/fully_sharded_data_parallel.html#FullyShardedDataParallel.get_state_dict_type"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/fsdp/fully_sharded_data_parallel.py#L751"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.fsdp.FullyShardedDataParallel.get_state_dict_type" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">get_state_dict_type</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/fsdp/fully_sharded_data_parallel.html#FullyShardedDataParallel.get_state_dict_type"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/fsdp/fully_sharded_data_parallel.py#L751"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.fsdp.FullyShardedDataParallel.get_state_dict_type" title="Permalink to this definition">¶</a></dt>
 <dd><p>Get the state_dict_type and the corresponding configurations for the FSDP modules rooted at <code class="docutils literal notranslate"><span class="pre">module</span></code>.</p>
 <p>The target module does not have to be an FSDP module.</p>
 <dl class="field-list simple">
@@ -1085,7 +1085,7 @@
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.fsdp.FullyShardedDataParallel.named_buffers">
-<span class="sig-name descname"><span class="pre">named_buffers</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/fsdp/fully_sharded_data_parallel.html#FullyShardedDataParallel.named_buffers"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/fsdp/fully_sharded_data_parallel.py#L971"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.fsdp.FullyShardedDataParallel.named_buffers" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">named_buffers</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/fsdp/fully_sharded_data_parallel.html#FullyShardedDataParallel.named_buffers"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/fsdp/fully_sharded_data_parallel.py#L971"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.fsdp.FullyShardedDataParallel.named_buffers" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return an iterator over module buffers, yielding both the name of the buffer and the buffer itself.</p>
 <p>Intercepts buffer names and removes all occurrences of the FSDP-specific flattened buffer prefix
 when inside the <a class="reference internal" href="#torch.distributed.fsdp.FullyShardedDataParallel.summon_full_params" title="torch.distributed.fsdp.FullyShardedDataParallel.summon_full_params"><code class="xref py py-meth docutils literal notranslate"><span class="pre">summon_full_params()</span></code></a> context manager.</p>
@@ -1098,7 +1098,7 @@
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.fsdp.FullyShardedDataParallel.named_parameters">
-<span class="sig-name descname"><span class="pre">named_parameters</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/fsdp/fully_sharded_data_parallel.html#FullyShardedDataParallel.named_parameters"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/fsdp/fully_sharded_data_parallel.py#L989"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.fsdp.FullyShardedDataParallel.named_parameters" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">named_parameters</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/fsdp/fully_sharded_data_parallel.html#FullyShardedDataParallel.named_parameters"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/fsdp/fully_sharded_data_parallel.py#L989"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.fsdp.FullyShardedDataParallel.named_parameters" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return an iterator over module parameters, yielding both the name of the parameter and the parameter itself.</p>
 <p>Intercepts parameter names and removes all occurrences of the FSDP-specific flattened parameter prefix
 when inside the <a class="reference internal" href="#torch.distributed.fsdp.FullyShardedDataParallel.summon_full_params" title="torch.distributed.fsdp.FullyShardedDataParallel.summon_full_params"><code class="xref py py-meth docutils literal notranslate"><span class="pre">summon_full_params()</span></code></a> context manager.</p>
@@ -1111,7 +1111,7 @@
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.fsdp.FullyShardedDataParallel.no_sync">
-<span class="sig-name descname"><span class="pre">no_sync</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/fsdp/fully_sharded_data_parallel.html#FullyShardedDataParallel.no_sync"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/fsdp/fully_sharded_data_parallel.py#L1027"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.fsdp.FullyShardedDataParallel.no_sync" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">no_sync</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/fsdp/fully_sharded_data_parallel.html#FullyShardedDataParallel.no_sync"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/fsdp/fully_sharded_data_parallel.py#L1027"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.fsdp.FullyShardedDataParallel.no_sync" title="Permalink to this definition">¶</a></dt>
 <dd><p>Disable gradient synchronizations across FSDP instances.</p>
 <p>Within this context, gradients will be accumulated in module
 variables, which will later be synchronized in the first
@@ -1139,7 +1139,7 @@
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.fsdp.FullyShardedDataParallel.optim_state_dict">
-<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">optim_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">optim</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">optim_state_dict</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/fsdp/fully_sharded_data_parallel.html#FullyShardedDataParallel.optim_state_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/fsdp/fully_sharded_data_parallel.py#L1801"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.fsdp.FullyShardedDataParallel.optim_state_dict" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">optim_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">optim</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">optim_state_dict</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/fsdp/fully_sharded_data_parallel.html#FullyShardedDataParallel.optim_state_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/fsdp/fully_sharded_data_parallel.py#L1801"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.fsdp.FullyShardedDataParallel.optim_state_dict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Transform the state-dict of an optimizer corresponding to a sharded model.</p>
 <p>The given state-dict can be transformed to one of three types:
 1) full optimizer state_dict, 2) sharded optimizer state_dict, 3) local optimizer state_dict.</p>
@@ -1213,7 +1213,7 @@
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.fsdp.FullyShardedDataParallel.optim_state_dict_to_load">
-<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">optim_state_dict_to_load</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">optim</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">optim_state_dict</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">is_named_optimizer</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">load_directly</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/fsdp/fully_sharded_data_parallel.html#FullyShardedDataParallel.optim_state_dict_to_load"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/fsdp/fully_sharded_data_parallel.py#L1897"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.fsdp.FullyShardedDataParallel.optim_state_dict_to_load" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">optim_state_dict_to_load</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">optim</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">optim_state_dict</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">is_named_optimizer</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">load_directly</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/fsdp/fully_sharded_data_parallel.html#FullyShardedDataParallel.optim_state_dict_to_load"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/fsdp/fully_sharded_data_parallel.py#L1897"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.fsdp.FullyShardedDataParallel.optim_state_dict_to_load" title="Permalink to this definition">¶</a></dt>
 <dd><p>Convert an optimizer state-dict so that it can be loaded into the optimizer associated with the FSDP model.</p>
 <p>Given a <code class="docutils literal notranslate"><span class="pre">optim_state_dict</span></code> that is transformed through
 <a class="reference internal" href="#torch.distributed.fsdp.FullyShardedDataParallel.optim_state_dict" title="torch.distributed.fsdp.FullyShardedDataParallel.optim_state_dict"><code class="xref py py-meth docutils literal notranslate"><span class="pre">optim_state_dict()</span></code></a>, it gets converted to the flattened optimizer
@@ -1284,7 +1284,7 @@
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.fsdp.FullyShardedDataParallel.register_comm_hook">
-<span class="sig-name descname"><span class="pre">register_comm_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/fsdp/fully_sharded_data_parallel.html#FullyShardedDataParallel.register_comm_hook"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/fsdp/fully_sharded_data_parallel.py#L1987"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.fsdp.FullyShardedDataParallel.register_comm_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_comm_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/fsdp/fully_sharded_data_parallel.html#FullyShardedDataParallel.register_comm_hook"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/fsdp/fully_sharded_data_parallel.py#L1987"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.fsdp.FullyShardedDataParallel.register_comm_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register a communication hook.</p>
 <p>This is an enhancement that provides a flexible hook to users where they can specify how FSDP aggregates
 gradients across multiple workers.
@@ -1330,7 +1330,7 @@
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.fsdp.FullyShardedDataParallel.rekey_optim_state_dict">
-<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">rekey_optim_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">optim_state_dict</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">optim_state_key_type</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">optim_input</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">optim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/fsdp/fully_sharded_data_parallel.html#FullyShardedDataParallel.rekey_optim_state_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/fsdp/fully_sharded_data_parallel.py#L1676"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.fsdp.FullyShardedDataParallel.rekey_optim_state_dict" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">rekey_optim_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">optim_state_dict</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">optim_state_key_type</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">optim_input</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">optim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/fsdp/fully_sharded_data_parallel.html#FullyShardedDataParallel.rekey_optim_state_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/fsdp/fully_sharded_data_parallel.py#L1676"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.fsdp.FullyShardedDataParallel.rekey_optim_state_dict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Re-keys the optimizer state dict <code class="docutils literal notranslate"><span class="pre">optim_state_dict</span></code> to use the key type <code class="docutils literal notranslate"><span class="pre">optim_state_key_type</span></code>.</p>
 <p>This can be used to achieve compatibility between optimizer state dicts from models with FSDP
 instances and ones without.</p>
@@ -1367,7 +1367,7 @@
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.fsdp.FullyShardedDataParallel.scatter_full_optim_state_dict">
-<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">scatter_full_optim_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">full_optim_state_dict</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">optim_input</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">optim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/fsdp/fully_sharded_data_parallel.html#FullyShardedDataParallel.scatter_full_optim_state_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/fsdp/fully_sharded_data_parallel.py#L1592"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.fsdp.FullyShardedDataParallel.scatter_full_optim_state_dict" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">scatter_full_optim_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">full_optim_state_dict</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">optim_input</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">optim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/fsdp/fully_sharded_data_parallel.html#FullyShardedDataParallel.scatter_full_optim_state_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/fsdp/fully_sharded_data_parallel.py#L1592"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.fsdp.FullyShardedDataParallel.scatter_full_optim_state_dict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Scatter the full optimizer state dict from rank 0 to all other ranks.</p>
 <p>Returns the sharded optimizer state dict on each rank.
 The return value is the same as <a class="reference internal" href="#torch.distributed.fsdp.FullyShardedDataParallel.shard_full_optim_state_dict" title="torch.distributed.fsdp.FullyShardedDataParallel.shard_full_optim_state_dict"><code class="xref py py-meth docutils literal notranslate"><span class="pre">shard_full_optim_state_dict()</span></code></a>, and on rank
@@ -1432,7 +1432,7 @@
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.fsdp.FullyShardedDataParallel.set_state_dict_type">
-<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">set_state_dict_type</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">state_dict_type</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">state_dict_config</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">optim_state_dict_config</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/fsdp/fully_sharded_data_parallel.html#FullyShardedDataParallel.set_state_dict_type"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/fsdp/fully_sharded_data_parallel.py#L632"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.fsdp.FullyShardedDataParallel.set_state_dict_type" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">set_state_dict_type</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">state_dict_type</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">state_dict_config</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">optim_state_dict_config</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/fsdp/fully_sharded_data_parallel.html#FullyShardedDataParallel.set_state_dict_type"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/fsdp/fully_sharded_data_parallel.py#L632"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.fsdp.FullyShardedDataParallel.set_state_dict_type" title="Permalink to this definition">¶</a></dt>
 <dd><p>Set the <code class="docutils literal notranslate"><span class="pre">state_dict_type</span></code> of all the descendant FSDP modules of the target module.</p>
 <p>Also takes (optional) configuration for the model’s and optimizer’s state dict.
 The target module does not have to be a FSDP module. If the target
@@ -1486,7 +1486,7 @@
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.fsdp.FullyShardedDataParallel.shard_full_optim_state_dict">
-<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">shard_full_optim_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">full_optim_state_dict</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">optim_input</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">optim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/fsdp/fully_sharded_data_parallel.html#FullyShardedDataParallel.shard_full_optim_state_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/fsdp/fully_sharded_data_parallel.py#L1474"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.fsdp.FullyShardedDataParallel.shard_full_optim_state_dict" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">shard_full_optim_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">full_optim_state_dict</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">optim_input</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">optim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/fsdp/fully_sharded_data_parallel.html#FullyShardedDataParallel.shard_full_optim_state_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/fsdp/fully_sharded_data_parallel.py#L1474"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.fsdp.FullyShardedDataParallel.shard_full_optim_state_dict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Shard a full optimizer state-dict.</p>
 <p>Remaps the state in <code class="docutils literal notranslate"><span class="pre">full_optim_state_dict</span></code> to flattened parameters instead of unflattened
 parameters and restricts to only this rank’s part of the optimizer state.
@@ -1549,7 +1549,7 @@
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.fsdp.FullyShardedDataParallel.sharded_optim_state_dict">
-<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">sharded_optim_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">optim</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/fsdp/fully_sharded_data_parallel.html#FullyShardedDataParallel.sharded_optim_state_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/fsdp/fully_sharded_data_parallel.py#L1440"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.fsdp.FullyShardedDataParallel.sharded_optim_state_dict" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">sharded_optim_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">optim</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/fsdp/fully_sharded_data_parallel.html#FullyShardedDataParallel.sharded_optim_state_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/fsdp/fully_sharded_data_parallel.py#L1440"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.fsdp.FullyShardedDataParallel.sharded_optim_state_dict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the optimizer state-dict in its sharded form.</p>
 <p>The API is similar to <a class="reference internal" href="#torch.distributed.fsdp.FullyShardedDataParallel.full_optim_state_dict" title="torch.distributed.fsdp.FullyShardedDataParallel.full_optim_state_dict"><code class="xref py py-meth docutils literal notranslate"><span class="pre">full_optim_state_dict()</span></code></a> but this API chunks
 all non-zero-dimension states to <code class="xref py py-class docutils literal notranslate"><span class="pre">ShardedTensor</span></code> to save memory.
@@ -1570,7 +1570,7 @@
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.fsdp.FullyShardedDataParallel.state_dict_type">
-<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">state_dict_type</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">state_dict_type</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">state_dict_config</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">optim_state_dict_config</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/fsdp/fully_sharded_data_parallel.html#FullyShardedDataParallel.state_dict_type"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/fsdp/fully_sharded_data_parallel.py#L787"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.fsdp.FullyShardedDataParallel.state_dict_type" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">state_dict_type</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">state_dict_type</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">state_dict_config</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">optim_state_dict_config</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/fsdp/fully_sharded_data_parallel.html#FullyShardedDataParallel.state_dict_type"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/fsdp/fully_sharded_data_parallel.py#L787"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.fsdp.FullyShardedDataParallel.state_dict_type" title="Permalink to this definition">¶</a></dt>
 <dd><p>Set the <code class="docutils literal notranslate"><span class="pre">state_dict_type</span></code> of all the descendant FSDP modules of the target module.</p>
 <p>This context manager has the same functions as <a class="reference internal" href="#torch.distributed.fsdp.FullyShardedDataParallel.set_state_dict_type" title="torch.distributed.fsdp.FullyShardedDataParallel.set_state_dict_type"><code class="xref py py-meth docutils literal notranslate"><span class="pre">set_state_dict_type()</span></code></a>. Read the document of
 <a class="reference internal" href="#torch.distributed.fsdp.FullyShardedDataParallel.set_state_dict_type" title="torch.distributed.fsdp.FullyShardedDataParallel.set_state_dict_type"><code class="xref py py-meth docutils literal notranslate"><span class="pre">set_state_dict_type()</span></code></a> for the detail.</p>
@@ -1602,7 +1602,7 @@
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.fsdp.FullyShardedDataParallel.summon_full_params">
-<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">summon_full_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">recurse</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">writeback</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">rank0_only</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">offload_to_cpu</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">with_grads</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/fsdp/fully_sharded_data_parallel.html#FullyShardedDataParallel.summon_full_params"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/fsdp/fully_sharded_data_parallel.py#L859"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.fsdp.FullyShardedDataParallel.summon_full_params" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">summon_full_params</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">recurse</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">writeback</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">rank0_only</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">offload_to_cpu</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">with_grads</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/fsdp/fully_sharded_data_parallel.html#FullyShardedDataParallel.summon_full_params"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/fsdp/fully_sharded_data_parallel.py#L859"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.fsdp.FullyShardedDataParallel.summon_full_params" title="Permalink to this definition">¶</a></dt>
 <dd><p>Expose full params for FSDP instances with this context manager.</p>
 <p>Can be useful <em>after</em> forward/backward for a model to get
 the params for additional processing or checking. It can take a non-FSDP
@@ -1692,7 +1692,7 @@
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributed.fsdp.BackwardPrefetch">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.fsdp.</span></span><span class="sig-name descname"><span class="pre">BackwardPrefetch</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/fsdp/api.html#BackwardPrefetch"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/fsdp/api.py#L73"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.fsdp.BackwardPrefetch" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.fsdp.</span></span><span class="sig-name descname"><span class="pre">BackwardPrefetch</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/fsdp/api.html#BackwardPrefetch"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/fsdp/api.py#L73"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.fsdp.BackwardPrefetch" title="Permalink to this definition">¶</a></dt>
 <dd><p>This configures explicit backward prefetching, which improves throughput by
 enabling communication and computation overlap in the backward pass at the
 cost of slightly increased memory usage.</p>
@@ -1724,7 +1724,7 @@
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributed.fsdp.ShardingStrategy">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.fsdp.</span></span><span class="sig-name descname"><span class="pre">ShardingStrategy</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/fsdp/api.html#ShardingStrategy"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/fsdp/api.py#L33"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.fsdp.ShardingStrategy" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.fsdp.</span></span><span class="sig-name descname"><span class="pre">ShardingStrategy</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/fsdp/api.html#ShardingStrategy"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/fsdp/api.py#L33"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.fsdp.ShardingStrategy" title="Permalink to this definition">¶</a></dt>
 <dd><p>This specifies the sharding strategy to be used for distributed training by
 <a class="reference internal" href="#torch.distributed.fsdp.FullyShardedDataParallel" title="torch.distributed.fsdp.FullyShardedDataParallel"><code class="xref py py-class docutils literal notranslate"><span class="pre">FullyShardedDataParallel</span></code></a>.</p>
 <ul class="simple">
@@ -1759,7 +1759,7 @@
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributed.fsdp.MixedPrecision">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.fsdp.</span></span><span class="sig-name descname"><span class="pre">MixedPrecision</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">param_dtype=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduce_dtype=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">buffer_dtype=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">keep_low_precision_grads=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cast_forward_inputs=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cast_root_forward_inputs=True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_module_classes_to_ignore=(&lt;class</span> <span class="pre">'torch.nn.modules.batchnorm._BatchNorm'&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">)</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/fsdp/api.html#MixedPrecision"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/fsdp/api.py#L112"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.fsdp.MixedPrecision" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.fsdp.</span></span><span class="sig-name descname"><span class="pre">MixedPrecision</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">param_dtype=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduce_dtype=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">buffer_dtype=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">keep_low_precision_grads=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cast_forward_inputs=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cast_root_forward_inputs=True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_module_classes_to_ignore=(&lt;class</span> <span class="pre">'torch.nn.modules.batchnorm._BatchNorm'&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">)</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/fsdp/api.html#MixedPrecision"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/fsdp/api.py#L112"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.fsdp.MixedPrecision" title="Permalink to this definition">¶</a></dt>
 <dd><p>This configures FSDP-native mixed precision training.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Variables</dt>
@@ -1883,7 +1883,7 @@
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributed.fsdp.CPUOffload">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.fsdp.</span></span><span class="sig-name descname"><span class="pre">CPUOffload</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">offload_params</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/fsdp/api.html#CPUOffload"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/fsdp/api.py#L230"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.fsdp.CPUOffload" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.fsdp.</span></span><span class="sig-name descname"><span class="pre">CPUOffload</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">offload_params</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/fsdp/api.html#CPUOffload"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/fsdp/api.py#L230"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.fsdp.CPUOffload" title="Permalink to this definition">¶</a></dt>
 <dd><p>This configures CPU offloading.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Variables</dt>
@@ -1897,7 +1897,7 @@
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributed.fsdp.StateDictConfig">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.fsdp.</span></span><span class="sig-name descname"><span class="pre">StateDictConfig</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">offload_to_cpu</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/fsdp/api.html#StateDictConfig"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/fsdp/api.py#L276"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.fsdp.StateDictConfig" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.fsdp.</span></span><span class="sig-name descname"><span class="pre">StateDictConfig</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">offload_to_cpu</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/fsdp/api.html#StateDictConfig"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/fsdp/api.py#L276"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.fsdp.StateDictConfig" title="Permalink to this definition">¶</a></dt>
 <dd><p><code class="docutils literal notranslate"><span class="pre">StateDictConfig</span></code> is the base class for all <code class="docutils literal notranslate"><span class="pre">state_dict</span></code> configuration
 classes. Users should instantiate a child class (e.g.
 <code class="docutils literal notranslate"><span class="pre">FullStateDictConfig</span></code>) in order to configure settings for the
@@ -1913,7 +1913,7 @@
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributed.fsdp.FullStateDictConfig">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.fsdp.</span></span><span class="sig-name descname"><span class="pre">FullStateDictConfig</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">offload_to_cpu</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">rank0_only</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/fsdp/api.html#FullStateDictConfig"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/fsdp/api.py#L293"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.fsdp.FullStateDictConfig" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.fsdp.</span></span><span class="sig-name descname"><span class="pre">FullStateDictConfig</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">offload_to_cpu</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">rank0_only</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/fsdp/api.html#FullStateDictConfig"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/fsdp/api.py#L293"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.fsdp.FullStateDictConfig" title="Permalink to this definition">¶</a></dt>
 <dd><p><code class="docutils literal notranslate"><span class="pre">FullStateDictConfig</span></code> is a config class meant to be used with
 <code class="docutils literal notranslate"><span class="pre">StateDictType.FULL_STATE_DICT</span></code>. We recommend enabling both
 <code class="docutils literal notranslate"><span class="pre">offload_to_cpu=True</span></code> and <code class="docutils literal notranslate"><span class="pre">rank0_only=True</span></code> when saving full state
@@ -1949,7 +1949,7 @@
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributed.fsdp.ShardedStateDictConfig">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.fsdp.</span></span><span class="sig-name descname"><span class="pre">ShardedStateDictConfig</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">offload_to_cpu</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_use_dtensor</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/fsdp/api.html#ShardedStateDictConfig"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/fsdp/api.py#L335"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.fsdp.ShardedStateDictConfig" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.fsdp.</span></span><span class="sig-name descname"><span class="pre">ShardedStateDictConfig</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">offload_to_cpu</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_use_dtensor</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/fsdp/api.html#ShardedStateDictConfig"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/fsdp/api.py#L335"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.fsdp.ShardedStateDictConfig" title="Permalink to this definition">¶</a></dt>
 <dd><p><code class="docutils literal notranslate"><span class="pre">ShardedStateDictConfig</span></code> is a config class meant to be used with
 <code class="docutils literal notranslate"><span class="pre">StateDictType.SHARDED_STATE_DICT</span></code>.</p>
 <dl class="field-list simple">
@@ -1969,14 +1969,14 @@
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributed.fsdp.LocalStateDictConfig">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.fsdp.</span></span><span class="sig-name descname"><span class="pre">LocalStateDictConfig</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">offload_to_cpu</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/fsdp/api.html#LocalStateDictConfig"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/fsdp/api.py#L330"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.fsdp.LocalStateDictConfig" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.fsdp.</span></span><span class="sig-name descname"><span class="pre">LocalStateDictConfig</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">offload_to_cpu</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/fsdp/api.html#LocalStateDictConfig"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/fsdp/api.py#L330"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.fsdp.LocalStateDictConfig" title="Permalink to this definition">¶</a></dt>
 <dd><dl class="field-list simple">
 </dl>
 </dd></dl>
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributed.fsdp.OptimStateDictConfig">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.fsdp.</span></span><span class="sig-name descname"><span class="pre">OptimStateDictConfig</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">offload_to_cpu</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/fsdp/api.html#OptimStateDictConfig"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/fsdp/api.py#L354"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.fsdp.OptimStateDictConfig" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.fsdp.</span></span><span class="sig-name descname"><span class="pre">OptimStateDictConfig</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">offload_to_cpu</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/fsdp/api.html#OptimStateDictConfig"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/fsdp/api.py#L354"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.fsdp.OptimStateDictConfig" title="Permalink to this definition">¶</a></dt>
 <dd><p><code class="docutils literal notranslate"><span class="pre">OptimStateDictConfig</span></code> is the base class for all <code class="docutils literal notranslate"><span class="pre">optim_state_dict</span></code>
 configuration classes.  Users should instantiate a child class (e.g.
 <code class="docutils literal notranslate"><span class="pre">FullOptimStateDictConfig</span></code>) in order to configure settings for the
@@ -1993,7 +1993,7 @@
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributed.fsdp.FullOptimStateDictConfig">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.fsdp.</span></span><span class="sig-name descname"><span class="pre">FullOptimStateDictConfig</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">offload_to_cpu</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">rank0_only</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/fsdp/api.html#FullOptimStateDictConfig"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/fsdp/api.py#L372"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.fsdp.FullOptimStateDictConfig" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.fsdp.</span></span><span class="sig-name descname"><span class="pre">FullOptimStateDictConfig</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">offload_to_cpu</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">rank0_only</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/fsdp/api.html#FullOptimStateDictConfig"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/fsdp/api.py#L372"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.fsdp.FullOptimStateDictConfig" title="Permalink to this definition">¶</a></dt>
 <dd><dl class="field-list simple">
 <dt class="field-odd">Variables</dt>
 <dd class="field-odd"><p><strong>rank0_only</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><em>bool</em></a>) – If <code class="docutils literal notranslate"><span class="pre">True</span></code>, then only rank 0 saves the full state
@@ -2005,7 +2005,7 @@
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributed.fsdp.ShardedOptimStateDictConfig">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.fsdp.</span></span><span class="sig-name descname"><span class="pre">ShardedOptimStateDictConfig</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">offload_to_cpu</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_use_dtensor</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/fsdp/api.html#ShardedOptimStateDictConfig"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/fsdp/api.py#L389"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.fsdp.ShardedOptimStateDictConfig" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.fsdp.</span></span><span class="sig-name descname"><span class="pre">ShardedOptimStateDictConfig</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">offload_to_cpu</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_use_dtensor</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/fsdp/api.html#ShardedOptimStateDictConfig"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/fsdp/api.py#L389"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.fsdp.ShardedOptimStateDictConfig" title="Permalink to this definition">¶</a></dt>
 <dd><p><code class="docutils literal notranslate"><span class="pre">ShardedOptimStateDictConfig</span></code> is a config class meant to be used with
 <code class="docutils literal notranslate"><span class="pre">StateDictType.SHARDED_STATE_DICT</span></code>.</p>
 <dl class="field-list simple">
@@ -2025,14 +2025,14 @@
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributed.fsdp.LocalOptimStateDictConfig">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.fsdp.</span></span><span class="sig-name descname"><span class="pre">LocalOptimStateDictConfig</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">offload_to_cpu</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/fsdp/api.html#LocalOptimStateDictConfig"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/fsdp/api.py#L384"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.fsdp.LocalOptimStateDictConfig" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.fsdp.</span></span><span class="sig-name descname"><span class="pre">LocalOptimStateDictConfig</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">offload_to_cpu</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/fsdp/api.html#LocalOptimStateDictConfig"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/fsdp/api.py#L384"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.fsdp.LocalOptimStateDictConfig" title="Permalink to this definition">¶</a></dt>
 <dd><dl class="field-list simple">
 </dl>
 </dd></dl>
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributed.fsdp.StateDictSettings">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.fsdp.</span></span><span class="sig-name descname"><span class="pre">StateDictSettings</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state_dict_type</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.distributed.fsdp.api.StateDictType</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">state_dict_config</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="#torch.distributed.fsdp.StateDictConfig" title="torch.distributed.fsdp.api.StateDictConfig"><span class="pre">torch.distributed.fsdp.api.StateDictConfig</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">optim_state_dict_config</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="#torch.distributed.fsdp.OptimStateDictConfig" title="torch.distributed.fsdp.api.OptimStateDictConfig"><span class="pre">torch.distributed.fsdp.api.OptimStateDictConfig</span></a></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/fsdp/api.html#StateDictSettings"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/fsdp/api.py#L408"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.fsdp.StateDictSettings" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.fsdp.</span></span><span class="sig-name descname"><span class="pre">StateDictSettings</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state_dict_type</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.distributed.fsdp.api.StateDictType</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">state_dict_config</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="#torch.distributed.fsdp.StateDictConfig" title="torch.distributed.fsdp.api.StateDictConfig"><span class="pre">torch.distributed.fsdp.api.StateDictConfig</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">optim_state_dict_config</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="#torch.distributed.fsdp.OptimStateDictConfig" title="torch.distributed.fsdp.api.OptimStateDictConfig"><span class="pre">torch.distributed.fsdp.api.OptimStateDictConfig</span></a></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/fsdp/api.html#StateDictSettings"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/fsdp/api.py#L408"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.fsdp.StateDictSettings" title="Permalink to this definition">¶</a></dt>
 <dd><dl class="field-list simple">
 </dl>
 </dd></dl>
diff --git a/main/func.api.html b/main/func.api.html
index 6dbce562321..40b14af6de5 100644
--- a/main/func.api.html
+++ b/main/func.api.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/func.batch_norm.html b/main/func.batch_norm.html
index 3238a1c0699..8b08805115a 100644
--- a/main/func.batch_norm.html
+++ b/main/func.batch_norm.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/func.html b/main/func.html
index 4970177eb90..f864dda286d 100644
--- a/main/func.html
+++ b/main/func.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/func.migrating.html b/main/func.migrating.html
index c26240d9346..307aa1cea47 100644
--- a/main/func.migrating.html
+++ b/main/func.migrating.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/func.ux_limitations.html b/main/func.ux_limitations.html
index e72fe44df29..0f36a031a8a 100644
--- a/main/func.ux_limitations.html
+++ b/main/func.ux_limitations.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/func.whirlwind_tour.html b/main/func.whirlwind_tour.html
index 8ef4e6c8fc1..6dbca86bf81 100644
--- a/main/func.whirlwind_tour.html
+++ b/main/func.whirlwind_tour.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/future_mod.html b/main/future_mod.html
index 7260731ae67..22175c9c84f 100644
--- a/main/future_mod.html
+++ b/main/future_mod.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -610,7 +610,7 @@
 <span id="torch-future"></span><h1>torch.__future__<a class="headerlink" href="#module-torch.__future__" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.__future__.set_overwrite_module_params_on_conversion">
-<span class="sig-prename descclassname"><span class="pre">torch.__future__.</span></span><span class="sig-name descname"><span class="pre">set_overwrite_module_params_on_conversion</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/__future__.html#set_overwrite_module_params_on_conversion"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/__future__.py#L5"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.__future__.set_overwrite_module_params_on_conversion" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.__future__.</span></span><span class="sig-name descname"><span class="pre">set_overwrite_module_params_on_conversion</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/__future__.html#set_overwrite_module_params_on_conversion"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/__future__.py#L5"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.__future__.set_overwrite_module_params_on_conversion" title="Permalink to this definition">¶</a></dt>
 <dd><p>Sets whether to assign new tensors to the parameters instead of changing the
 existing parameters in-place when converting an <code class="docutils literal notranslate"><span class="pre">nn.Module</span></code>.</p>
 <p>When enabled, the following methods will assign new parameters to the module:</p>
@@ -629,7 +629,7 @@
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.__future__.get_overwrite_module_params_on_conversion">
-<span class="sig-prename descclassname"><span class="pre">torch.__future__.</span></span><span class="sig-name descname"><span class="pre">get_overwrite_module_params_on_conversion</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/__future__.html#get_overwrite_module_params_on_conversion"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/__future__.py#L25"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.__future__.get_overwrite_module_params_on_conversion" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.__future__.</span></span><span class="sig-name descname"><span class="pre">get_overwrite_module_params_on_conversion</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/__future__.html#get_overwrite_module_params_on_conversion"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/__future__.py#L25"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.__future__.get_overwrite_module_params_on_conversion" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns whether to assign new tensors to the parameters instead of changing the
 existing parameters in-place when converting an <a class="reference internal" href="generated/torch.nn.Module.html#torch.nn.Module" title="torch.nn.Module"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.nn.Module</span></code></a>. Defaults to <code class="docutils literal notranslate"><span class="pre">False</span></code>.</p>
 <p>See <a class="reference internal" href="#torch.__future__.set_overwrite_module_params_on_conversion" title="torch.__future__.set_overwrite_module_params_on_conversion"><code class="xref py py-func docutils literal notranslate"><span class="pre">set_overwrite_module_params_on_conversion()</span></code></a> for more information.</p>
@@ -642,7 +642,7 @@
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.__future__.set_swap_module_params_on_conversion">
-<span class="sig-prename descclassname"><span class="pre">torch.__future__.</span></span><span class="sig-name descname"><span class="pre">set_swap_module_params_on_conversion</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/__future__.html#set_swap_module_params_on_conversion"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/__future__.py#L35"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.__future__.set_swap_module_params_on_conversion" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.__future__.</span></span><span class="sig-name descname"><span class="pre">set_swap_module_params_on_conversion</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/__future__.html#set_swap_module_params_on_conversion"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/__future__.py#L35"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.__future__.set_swap_module_params_on_conversion" title="Permalink to this definition">¶</a></dt>
 <dd><p>Sets whether to use <a class="reference internal" href="generated/torch.utils.swap_tensors.html#torch.utils.swap_tensors" title="torch.utils.swap_tensors"><code class="xref py py-func docutils literal notranslate"><span class="pre">swap_tensors()</span></code></a> instead of setting <code class="docutils literal notranslate"><span class="pre">.data</span></code> to
 change the existing parameters in-place when converting an <code class="docutils literal notranslate"><span class="pre">nn.Module</span></code> and instead
 of <code class="docutils literal notranslate"><span class="pre">param.copy_(state_dict[key])</span></code> when loading a state dict into an <code class="docutils literal notranslate"><span class="pre">nn.Module</span></code>.</p>
@@ -675,7 +675,7 @@
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.__future__.get_swap_module_params_on_conversion">
-<span class="sig-prename descclassname"><span class="pre">torch.__future__.</span></span><span class="sig-name descname"><span class="pre">get_swap_module_params_on_conversion</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/__future__.html#get_swap_module_params_on_conversion"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/__future__.py#L68"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.__future__.get_swap_module_params_on_conversion" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.__future__.</span></span><span class="sig-name descname"><span class="pre">get_swap_module_params_on_conversion</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/__future__.html#get_swap_module_params_on_conversion"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/__future__.py#L68"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.__future__.get_swap_module_params_on_conversion" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns whether to use <a class="reference internal" href="generated/torch.utils.swap_tensors.html#torch.utils.swap_tensors" title="torch.utils.swap_tensors"><code class="xref py py-func docutils literal notranslate"><span class="pre">swap_tensors()</span></code></a> instead of setting .data to
 change the existing parameters in-place when converting an <code class="docutils literal notranslate"><span class="pre">nn.Module</span></code>. Defaults to <code class="docutils literal notranslate"><span class="pre">False</span></code>.</p>
 <p>See <a class="reference internal" href="#torch.__future__.set_swap_module_params_on_conversion" title="torch.__future__.set_swap_module_params_on_conversion"><code class="xref py py-func docutils literal notranslate"><span class="pre">set_swap_module_params_on_conversion()</span></code></a> for more information.</p>
diff --git a/main/futures.html b/main/futures.html
index c10b21db71a..1c95a2e431e 100644
--- a/main/futures.html
+++ b/main/futures.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -627,7 +627,7 @@
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.futures.Future.add_done_callback">
-<span class="sig-name descname"><span class="pre">add_done_callback</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">callback</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/futures.html#Future.add_done_callback"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/futures/__init__.py#L168"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.futures.Future.add_done_callback" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">add_done_callback</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">callback</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/futures.html#Future.add_done_callback"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/futures/__init__.py#L168"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.futures.Future.add_done_callback" title="Permalink to this definition">¶</a></dt>
 <dd><p>Append the given callback function to this <code class="docutils literal notranslate"><span class="pre">Future</span></code>, which will be run
 when the <code class="docutils literal notranslate"><span class="pre">Future</span></code> is completed.  Multiple callbacks can be added to
 the same <code class="docutils literal notranslate"><span class="pre">Future</span></code>, but the order in which they will be executed cannot
@@ -675,7 +675,7 @@
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.futures.Future.done">
-<span class="sig-name descname"><span class="pre">done</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/futures.html#Future.done"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/futures/__init__.py#L49"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.futures.Future.done" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">done</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/futures.html#Future.done"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/futures/__init__.py#L49"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.futures.Future.done" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return <code class="docutils literal notranslate"><span class="pre">True</span></code> if this <code class="docutils literal notranslate"><span class="pre">Future</span></code> is done. A <code class="docutils literal notranslate"><span class="pre">Future</span></code> is done if it
 has a result or an exception.</p>
 <p>If the value contains tensors that reside on GPUs, <code class="docutils literal notranslate"><span class="pre">Future.done()</span></code>
@@ -692,7 +692,7 @@
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.futures.Future.set_exception">
-<span class="sig-name descname"><span class="pre">set_exception</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">result</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/futures.html#Future.set_exception"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/futures/__init__.py#L251"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.futures.Future.set_exception" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">set_exception</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">result</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/futures.html#Future.set_exception"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/futures/__init__.py#L251"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.futures.Future.set_exception" title="Permalink to this definition">¶</a></dt>
 <dd><p>Set an exception for this <code class="docutils literal notranslate"><span class="pre">Future</span></code>, which will mark this <code class="docutils literal notranslate"><span class="pre">Future</span></code> as
 completed with an error and trigger all attached callbacks. Note that
 when calling wait()/value() on this <code class="docutils literal notranslate"><span class="pre">Future</span></code>, the exception set here
@@ -717,7 +717,7 @@
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.futures.Future.set_result">
-<span class="sig-name descname"><span class="pre">set_result</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">result</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/futures.html#Future.set_result"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/futures/__init__.py#L212"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.futures.Future.set_result" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">set_result</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">result</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/futures.html#Future.set_result"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/futures/__init__.py#L212"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.futures.Future.set_result" title="Permalink to this definition">¶</a></dt>
 <dd><p>Set the result for this <code class="docutils literal notranslate"><span class="pre">Future</span></code>, which will mark this <code class="docutils literal notranslate"><span class="pre">Future</span></code> as
 completed and trigger all attached callbacks. Note that a <code class="docutils literal notranslate"><span class="pre">Future</span></code>
 cannot be marked completed twice.</p>
@@ -759,7 +759,7 @@
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.futures.Future.then">
-<span class="sig-name descname"><span class="pre">then</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">callback</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/futures.html#Future.then"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/futures/__init__.py#L104"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.futures.Future.then" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">then</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">callback</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/futures.html#Future.then"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/futures/__init__.py#L104"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.futures.Future.then" title="Permalink to this definition">¶</a></dt>
 <dd><p>Append the given callback function to this <code class="docutils literal notranslate"><span class="pre">Future</span></code>, which will be run
 when the <code class="docutils literal notranslate"><span class="pre">Future</span></code> is completed.  Multiple callbacks can be added to
 the same <code class="docutils literal notranslate"><span class="pre">Future</span></code>, but the order in which they will be executed cannot
@@ -830,7 +830,7 @@
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.futures.Future.value">
-<span class="sig-name descname"><span class="pre">value</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/futures.html#Future.value"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/futures/__init__.py#L83"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.futures.Future.value" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">value</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/futures.html#Future.value"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/futures/__init__.py#L83"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.futures.Future.value" title="Permalink to this definition">¶</a></dt>
 <dd><p>Obtain the value of an already-completed future.</p>
 <p>This method should only be called after a call to <a class="reference internal" href="#torch.futures.Future.wait" title="torch.futures.Future.wait"><code class="xref py py-meth docutils literal notranslate"><span class="pre">wait()</span></code></a> has
 completed, or inside a callback function passed to <a class="reference internal" href="#torch.futures.Future.then" title="torch.futures.Future.then"><code class="xref py py-meth docutils literal notranslate"><span class="pre">then()</span></code></a>. In
@@ -854,7 +854,7 @@
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.futures.Future.wait">
-<span class="sig-name descname"><span class="pre">wait</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/futures.html#Future.wait"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/futures/__init__.py#L62"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.futures.Future.wait" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">wait</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/futures.html#Future.wait"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/futures/__init__.py#L62"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.futures.Future.wait" title="Permalink to this definition">¶</a></dt>
 <dd><p>Block until the value of this <code class="docutils literal notranslate"><span class="pre">Future</span></code> is ready.</p>
 <p>If the value contains tensors that reside on GPUs, then an additional
 synchronization is performed with the kernels (executing on the device)
@@ -881,7 +881,7 @@
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.futures.collect_all">
-<span class="sig-prename descclassname"><span class="pre">torch.futures.</span></span><span class="sig-name descname"><span class="pre">collect_all</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">futures</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/futures.html#collect_all"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/futures/__init__.py#L281"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.futures.collect_all" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.futures.</span></span><span class="sig-name descname"><span class="pre">collect_all</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">futures</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/futures.html#collect_all"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/futures/__init__.py#L281"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.futures.collect_all" title="Permalink to this definition">¶</a></dt>
 <dd><p>Collects the provided <a class="reference internal" href="#torch.futures.Future" title="torch.futures.Future"><code class="xref py py-class docutils literal notranslate"><span class="pre">Future</span></code></a> objects into a single
 combined <a class="reference internal" href="#torch.futures.Future" title="torch.futures.Future"><code class="xref py py-class docutils literal notranslate"><span class="pre">Future</span></code></a> that is completed when all of the
 sub-futures are completed.</p>
@@ -916,7 +916,7 @@
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.futures.wait_all">
-<span class="sig-prename descclassname"><span class="pre">torch.futures.</span></span><span class="sig-name descname"><span class="pre">wait_all</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">futures</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/futures.html#wait_all"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/futures/__init__.py#L313"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.futures.wait_all" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.futures.</span></span><span class="sig-name descname"><span class="pre">wait_all</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">futures</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/futures.html#wait_all"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/futures/__init__.py#L313"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.futures.wait_all" title="Permalink to this definition">¶</a></dt>
 <dd><p>Waits for all provided futures to be complete, and returns
 the list of completed values. If any of the futures encounters an error,
 the method will exit early and report the error not waiting for other
diff --git a/main/fx.experimental.html b/main/fx.experimental.html
index 54c64936c8e..08edcf07c80 100644
--- a/main/fx.experimental.html
+++ b/main/fx.experimental.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/fx.html b/main/fx.html
index dbbfcdcfaf4..40f2134b858 100644
--- a/main/fx.html
+++ b/main/fx.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -1737,7 +1737,7 @@ <h3>Miscellanea<a class="headerlink" href="#miscellanea" title="Permalink to thi
 <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to this heading">¶</a></h2>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.fx.symbolic_trace">
-<span class="sig-prename descclassname"><span class="pre">torch.fx.</span></span><span class="sig-name descname"><span class="pre">symbolic_trace</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">root</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">concrete_args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/_symbolic_trace.html#symbolic_trace"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/_symbolic_trace.py#L1241"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.symbolic_trace" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.fx.</span></span><span class="sig-name descname"><span class="pre">symbolic_trace</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">root</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">concrete_args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/_symbolic_trace.html#symbolic_trace"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/fx/_symbolic_trace.py#L1241"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.symbolic_trace" title="Permalink to this definition">¶</a></dt>
 <dd><p>Symbolic tracing API</p>
 <p>Given an <code class="docutils literal notranslate"><span class="pre">nn.Module</span></code> or function instance <code class="docutils literal notranslate"><span class="pre">root</span></code>, this function will return a <code class="docutils literal notranslate"><span class="pre">GraphModule</span></code>
 constructed by recording operations seen while tracing through <code class="docutils literal notranslate"><span class="pre">root</span></code>.</p>
@@ -1796,7 +1796,7 @@ <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.fx.wrap">
-<span class="sig-prename descclassname"><span class="pre">torch.fx.</span></span><span class="sig-name descname"><span class="pre">wrap</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">fn_or_name</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/_symbolic_trace.html#wrap"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/_symbolic_trace.py#L1177"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.wrap" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.fx.</span></span><span class="sig-name descname"><span class="pre">wrap</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">fn_or_name</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/_symbolic_trace.html#wrap"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/fx/_symbolic_trace.py#L1177"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.wrap" title="Permalink to this definition">¶</a></dt>
 <dd><p>This function can be called at module-level scope to register fn_or_name as a “leaf function”.
 A “leaf function” will be preserved as a CallFunction node in the FX trace instead of being
 traced through:</p>
@@ -1838,7 +1838,7 @@ <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.fx.GraphModule">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.fx.</span></span><span class="sig-name descname"><span class="pre">GraphModule</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/graph_module.html#GraphModule"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/graph_module.py#L403"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.GraphModule" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.fx.</span></span><span class="sig-name descname"><span class="pre">GraphModule</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/graph_module.html#GraphModule"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/fx/graph_module.py#L403"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.GraphModule" title="Permalink to this definition">¶</a></dt>
 <dd><p>GraphModule is an nn.Module generated from an fx.Graph. Graphmodule has a
 <code class="docutils literal notranslate"><span class="pre">graph</span></code> attribute, as well as <code class="docutils literal notranslate"><span class="pre">code</span></code> and <code class="docutils literal notranslate"><span class="pre">forward</span></code> attributes generated
 from that <code class="docutils literal notranslate"><span class="pre">graph</span></code>.</p>
@@ -1855,7 +1855,7 @@ <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.GraphModule.__init__">
-<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">root</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">graph</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">class_name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'GraphModule'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/graph_module.html#GraphModule.__init__"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/graph_module.py#L437"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.GraphModule.__init__" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">root</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">graph</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">class_name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'GraphModule'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/graph_module.html#GraphModule.__init__"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/fx/graph_module.py#L437"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.GraphModule.__init__" title="Permalink to this definition">¶</a></dt>
 <dd><p>Construct a GraphModule.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -1882,7 +1882,7 @@ <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.GraphModule.add_submodule">
-<span class="sig-name descname"><span class="pre">add_submodule</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">target</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">m</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/graph_module.html#GraphModule.add_submodule"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/graph_module.py#L645"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.GraphModule.add_submodule" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">add_submodule</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">target</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">m</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/graph_module.html#GraphModule.add_submodule"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/fx/graph_module.py#L645"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.GraphModule.add_submodule" title="Permalink to this definition">¶</a></dt>
 <dd><p>Adds the given submodule to <code class="docutils literal notranslate"><span class="pre">self</span></code>.</p>
 <p>This installs empty Modules where none exist yet if they are
 subpaths of <code class="docutils literal notranslate"><span class="pre">target</span></code>.</p>
@@ -1925,7 +1925,7 @@ <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.GraphModule.delete_all_unused_submodules">
-<span class="sig-name descname"><span class="pre">delete_all_unused_submodules</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/graph_module.html#GraphModule.delete_all_unused_submodules"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/graph_module.py#L727"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.GraphModule.delete_all_unused_submodules" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">delete_all_unused_submodules</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/graph_module.html#GraphModule.delete_all_unused_submodules"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/fx/graph_module.py#L727"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.GraphModule.delete_all_unused_submodules" title="Permalink to this definition">¶</a></dt>
 <dd><p>Deletes all unused submodules from <code class="docutils literal notranslate"><span class="pre">self</span></code>.</p>
 <p>A Module is considered “used” if any one of the following is
 true:
@@ -1945,7 +1945,7 @@ <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.GraphModule.delete_submodule">
-<span class="sig-name descname"><span class="pre">delete_submodule</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">target</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/graph_module.html#GraphModule.delete_submodule"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/graph_module.py#L685"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.GraphModule.delete_submodule" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">delete_submodule</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">target</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/graph_module.html#GraphModule.delete_submodule"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/fx/graph_module.py#L685"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.GraphModule.delete_submodule" title="Permalink to this definition">¶</a></dt>
 <dd><p>Deletes the given submodule from <code class="docutils literal notranslate"><span class="pre">self</span></code>.</p>
 <p>The module will not be deleted if <code class="docutils literal notranslate"><span class="pre">target</span></code> is not a valid
 target.</p>
@@ -1982,7 +1982,7 @@ <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.GraphModule.print_readable">
-<span class="sig-name descname"><span class="pre">print_readable</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">print_output</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">include_stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">include_device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">colored</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/graph_module.html#GraphModule.print_readable"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/graph_module.py#L914"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.GraphModule.print_readable" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">print_readable</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">print_output</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">include_stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">include_device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">colored</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/graph_module.html#GraphModule.print_readable"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/fx/graph_module.py#L914"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.GraphModule.print_readable" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the Python code generated for current GraphModule and its children GraphModules</p>
 <div class="admonition warning">
 <p class="admonition-title">Warning</p>
@@ -1992,7 +1992,7 @@ <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.GraphModule.recompile">
-<span class="sig-name descname"><span class="pre">recompile</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/graph_module.html#GraphModule.recompile"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/graph_module.py#L794"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.GraphModule.recompile" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">recompile</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/graph_module.html#GraphModule.recompile"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/fx/graph_module.py#L794"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.GraphModule.recompile" title="Permalink to this definition">¶</a></dt>
 <dd><p>Recompile this GraphModule from its <code class="docutils literal notranslate"><span class="pre">graph</span></code> attribute. This should be
 called after editing the contained <code class="docutils literal notranslate"><span class="pre">graph</span></code>, otherwise the generated
 code of this <code class="docutils literal notranslate"><span class="pre">GraphModule</span></code> will be out of date.</p>
@@ -2009,7 +2009,7 @@ <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.GraphModule.to_folder">
-<span class="sig-name descname"><span class="pre">to_folder</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">folder</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">module_name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'FxModule'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/graph_module.html#GraphModule.to_folder"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/graph_module.py#L562"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.GraphModule.to_folder" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">to_folder</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">folder</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">module_name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'FxModule'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/graph_module.html#GraphModule.to_folder"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/fx/graph_module.py#L562"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.GraphModule.to_folder" title="Permalink to this definition">¶</a></dt>
 <dd><dl>
 <dt>Dumps out module to <code class="docutils literal notranslate"><span class="pre">folder</span></code> with <code class="docutils literal notranslate"><span class="pre">module_name</span></code> so that it can be</dt><dd><p>imported with <code class="docutils literal notranslate"><span class="pre">from</span> <span class="pre">&lt;folder&gt;</span> <span class="pre">import</span> <span class="pre">&lt;module_name&gt;</span></code></p>
 <p>Args:</p>
@@ -2034,7 +2034,7 @@ <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.fx.Graph">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.fx.</span></span><span class="sig-name descname"><span class="pre">Graph</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">owning_module</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tracer_cls</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tracer_extras</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/graph.html#Graph"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/graph.py#L924"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Graph" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.fx.</span></span><span class="sig-name descname"><span class="pre">Graph</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">owning_module</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tracer_cls</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tracer_extras</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/graph.html#Graph"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/fx/graph.py#L924"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Graph" title="Permalink to this definition">¶</a></dt>
 <dd><p><code class="docutils literal notranslate"><span class="pre">Graph</span></code> is the main data structure used in the FX Intermediate Representation.
 It consists of a series of <code class="docutils literal notranslate"><span class="pre">Node</span></code> s, each representing callsites (or other
 syntactic constructs). The list of <code class="docutils literal notranslate"><span class="pre">Node</span></code> s, taken together, constitute a
@@ -2083,7 +2083,7 @@ <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.Graph.__init__">
-<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">owning_module</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tracer_cls</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tracer_extras</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/graph.html#Graph.__init__"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/graph.py#L973"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Graph.__init__" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">owning_module</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tracer_cls</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tracer_extras</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/graph.html#Graph.__init__"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/fx/graph.py#L973"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Graph.__init__" title="Permalink to this definition">¶</a></dt>
 <dd><p>Construct an empty Graph.</p>
 <div class="admonition note">
 <p class="admonition-title">Note</p>
@@ -2095,7 +2095,7 @@ <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.Graph.call_function">
-<span class="sig-name descname"><span class="pre">call_function</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">the_function</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kwargs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">type_expr</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/graph.html#Graph.call_function"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/graph.py#L1450"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Graph.call_function" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">call_function</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">the_function</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kwargs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">type_expr</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/graph.html#Graph.call_function"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/fx/graph.py#L1450"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Graph.call_function" title="Permalink to this definition">¶</a></dt>
 <dd><p>Insert a <code class="docutils literal notranslate"><span class="pre">call_function</span></code> <code class="docutils literal notranslate"><span class="pre">Node</span></code> into the <code class="docutils literal notranslate"><span class="pre">Graph</span></code>. A <code class="docutils literal notranslate"><span class="pre">call_function</span></code> node
 represents a call to a Python callable, specified by <code class="docutils literal notranslate"><span class="pre">the_function</span></code>.</p>
 <dl class="field-list simple">
@@ -2132,7 +2132,7 @@ <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.Graph.call_method">
-<span class="sig-name descname"><span class="pre">call_method</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">method_name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kwargs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">type_expr</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/graph.html#Graph.call_method"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/graph.py#L1411"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Graph.call_method" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">call_method</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">method_name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kwargs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">type_expr</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/graph.html#Graph.call_method"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/fx/graph.py#L1411"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Graph.call_method" title="Permalink to this definition">¶</a></dt>
 <dd><p>Insert a <code class="docutils literal notranslate"><span class="pre">call_method</span></code> <code class="docutils literal notranslate"><span class="pre">Node</span></code> into the <code class="docutils literal notranslate"><span class="pre">Graph</span></code>. A <code class="docutils literal notranslate"><span class="pre">call_method</span></code> node
 represents a call to a given method on the 0th element of <code class="docutils literal notranslate"><span class="pre">args</span></code>.</p>
 <dl class="field-list simple">
@@ -2169,7 +2169,7 @@ <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.Graph.call_module">
-<span class="sig-name descname"><span class="pre">call_module</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module_name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kwargs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">type_expr</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/graph.html#Graph.call_module"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/graph.py#L1361"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Graph.call_module" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">call_module</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module_name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kwargs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">type_expr</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/graph.html#Graph.call_module"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/fx/graph.py#L1361"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Graph.call_module" title="Permalink to this definition">¶</a></dt>
 <dd><p>Insert a <code class="docutils literal notranslate"><span class="pre">call_module</span></code> <code class="docutils literal notranslate"><span class="pre">Node</span></code> into the <code class="docutils literal notranslate"><span class="pre">Graph</span></code>. A <code class="docutils literal notranslate"><span class="pre">call_module</span></code> node
 represents a call to the forward() function of a <code class="docutils literal notranslate"><span class="pre">Module</span></code> in the <code class="docutils literal notranslate"><span class="pre">Module</span></code>
 hierarchy.</p>
@@ -2209,7 +2209,7 @@ <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.Graph.create_node">
-<span class="sig-name descname"><span class="pre">create_node</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">op</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kwargs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">type_expr</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/graph.html#Graph.create_node"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/graph.py#L1100"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Graph.create_node" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">create_node</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">op</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kwargs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">type_expr</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/graph.html#Graph.create_node"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/fx/graph.py#L1100"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Graph.create_node" title="Permalink to this definition">¶</a></dt>
 <dd><p>Create a <code class="docutils literal notranslate"><span class="pre">Node</span></code> and add it to the <code class="docutils literal notranslate"><span class="pre">Graph</span></code> at the current insert-point.
 Note that the current insert-point can be set via <a class="reference internal" href="#torch.fx.Graph.inserting_before" title="torch.fx.Graph.inserting_before"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Graph.inserting_before()</span></code></a>
 and <a class="reference internal" href="#torch.fx.Graph.inserting_after" title="torch.fx.Graph.inserting_after"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Graph.inserting_after()</span></code></a>.</p>
@@ -2243,7 +2243,7 @@ <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.Graph.eliminate_dead_code">
-<span class="sig-name descname"><span class="pre">eliminate_dead_code</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">is_impure_node</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/graph.html#Graph.eliminate_dead_code"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/graph.py#L1804"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Graph.eliminate_dead_code" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">eliminate_dead_code</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">is_impure_node</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/graph.html#Graph.eliminate_dead_code"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/fx/graph.py#L1804"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Graph.eliminate_dead_code" title="Permalink to this definition">¶</a></dt>
 <dd><p>Remove all dead code from the graph, based on each node’s number of
 users, and whether the nodes have any side effects. The graph must be
 topologically sorted before calling.</p>
@@ -2294,7 +2294,7 @@ <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.Graph.erase_node">
-<span class="sig-name descname"><span class="pre">erase_node</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">to_erase</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/graph.html#Graph.erase_node"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/graph.py#L1170"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Graph.erase_node" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">erase_node</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">to_erase</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/graph.html#Graph.erase_node"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/fx/graph.py#L1170"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Graph.erase_node" title="Permalink to this definition">¶</a></dt>
 <dd><p>Erases a <code class="docutils literal notranslate"><span class="pre">Node</span></code> from the <code class="docutils literal notranslate"><span class="pre">Graph</span></code>. Throws an exception if
 there are still users of that node in the <code class="docutils literal notranslate"><span class="pre">Graph</span></code>.</p>
 <dl class="field-list simple">
@@ -2310,7 +2310,7 @@ <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.Graph.find_nodes">
-<span class="sig-name descname"><span class="pre">find_nodes</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">op</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sort</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/graph.html#Graph.find_nodes"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/graph.py#L1024"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Graph.find_nodes" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">find_nodes</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">op</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sort</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/graph.html#Graph.find_nodes"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/fx/graph.py#L1024"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Graph.find_nodes" title="Permalink to this definition">¶</a></dt>
 <dd><p>Allows for fast query of nodes</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -2334,7 +2334,7 @@ <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.Graph.get_attr">
-<span class="sig-name descname"><span class="pre">get_attr</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">qualified_name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">type_expr</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/graph.html#Graph.get_attr"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/graph.py#L1293"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Graph.get_attr" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">get_attr</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">qualified_name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">type_expr</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/graph.html#Graph.get_attr"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/fx/graph.py#L1293"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Graph.get_attr" title="Permalink to this definition">¶</a></dt>
 <dd><p>Insert a <code class="docutils literal notranslate"><span class="pre">get_attr</span></code> node into the Graph. A <code class="docutils literal notranslate"><span class="pre">get_attr</span></code> <code class="docutils literal notranslate"><span class="pre">Node</span></code> represents the
 fetch of an attribute from the <code class="docutils literal notranslate"><span class="pre">Module</span></code> hierarchy.</p>
 <dl class="field-list simple">
@@ -2368,7 +2368,7 @@ <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.Graph.graph_copy">
-<span class="sig-name descname"><span class="pre">graph_copy</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">g</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">val_map</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_output_node</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/graph.html#Graph.graph_copy"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/graph.py#L1050"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Graph.graph_copy" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">graph_copy</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">g</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">val_map</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_output_node</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/graph.html#Graph.graph_copy"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/fx/graph.py#L1050"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Graph.graph_copy" title="Permalink to this definition">¶</a></dt>
 <dd><p>Copy all nodes from a given graph into <code class="docutils literal notranslate"><span class="pre">self</span></code>.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -2395,7 +2395,7 @@ <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.Graph.inserting_after">
-<span class="sig-name descname"><span class="pre">inserting_after</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/graph.html#Graph.inserting_after"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/graph.py#L1236"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Graph.inserting_after" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">inserting_after</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/graph.html#Graph.inserting_after"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/fx/graph.py#L1236"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Graph.inserting_after" title="Permalink to this definition">¶</a></dt>
 <dd><dl>
 <dt>Set the point at which create_node and companion methods will insert into the graph.</dt><dd><p>When used within a ‘with’ statement, this will temporary set the insert point and
 then restore it when the with statement exits:</p>
@@ -2428,7 +2428,7 @@ <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.Graph.inserting_before">
-<span class="sig-name descname"><span class="pre">inserting_before</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/graph.html#Graph.inserting_before"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/graph.py#L1212"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Graph.inserting_before" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">inserting_before</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/graph.html#Graph.inserting_before"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/fx/graph.py#L1212"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Graph.inserting_before" title="Permalink to this definition">¶</a></dt>
 <dd><dl>
 <dt>Set the point at which create_node and companion methods will insert into the graph.</dt><dd><p>When used within a ‘with’ statement, this will temporary set the insert point and
 then restore it when the with statement exits:</p>
@@ -2461,7 +2461,7 @@ <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.Graph.lint">
-<span class="sig-name descname"><span class="pre">lint</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/graph.html#Graph.lint"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/graph.py#L1695"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Graph.lint" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">lint</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/graph.html#Graph.lint"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/fx/graph.py#L1695"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Graph.lint" title="Permalink to this definition">¶</a></dt>
 <dd><p>Runs various checks on this Graph to make sure it is well-formed. In
 particular:
 - Checks Nodes have correct ownership (owned by this graph)
@@ -2476,7 +2476,7 @@ <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.Graph.node_copy">
-<span class="sig-name descname"><span class="pre">node_copy</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">node</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">arg_transform=&lt;function</span> <span class="pre">Graph.&lt;lambda&gt;&gt;</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/graph.html#Graph.node_copy"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/graph.py#L1489"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Graph.node_copy" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">node_copy</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">node</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">arg_transform=&lt;function</span> <span class="pre">Graph.&lt;lambda&gt;&gt;</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/graph.html#Graph.node_copy"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/fx/graph.py#L1489"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Graph.node_copy" title="Permalink to this definition">¶</a></dt>
 <dd><p>Copy a node from one graph into another. <code class="docutils literal notranslate"><span class="pre">arg_transform</span></code> needs to transform arguments from
 the graph of node to the graph of self. Example:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># Copying all the nodes in `g` into `new_graph`</span>
@@ -2524,7 +2524,7 @@ <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.Graph.on_generate_code">
-<span class="sig-name descname"><span class="pre">on_generate_code</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">make_transformer</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/graph.html#Graph.on_generate_code"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/graph.py#L1873"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Graph.on_generate_code" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">on_generate_code</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">make_transformer</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/graph.html#Graph.on_generate_code"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/fx/graph.py#L1873"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Graph.on_generate_code" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register a transformer function when python code is generated</p>
 <blockquote>
 <div><dl>
@@ -2597,7 +2597,7 @@ <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.Graph.output">
-<span class="sig-name descname"><span class="pre">output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">result</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">type_expr</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/graph.html#Graph.output"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/graph.py#L1524"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Graph.output" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">result</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">type_expr</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/graph.html#Graph.output"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/fx/graph.py#L1524"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Graph.output" title="Permalink to this definition">¶</a></dt>
 <dd><p>Insert an <code class="docutils literal notranslate"><span class="pre">output</span></code> <code class="docutils literal notranslate"><span class="pre">Node</span></code> into the <code class="docutils literal notranslate"><span class="pre">Graph</span></code>. An <code class="docutils literal notranslate"><span class="pre">output</span></code> node represents
 a <code class="docutils literal notranslate"><span class="pre">return</span></code> statement in Python code. <code class="docutils literal notranslate"><span class="pre">result</span></code> is the value that should
 be returned.</p>
@@ -2623,7 +2623,7 @@ <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.Graph.output_node">
-<span class="sig-name descname"><span class="pre">output_node</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/graph.html#Graph.output_node"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/graph.py#L1018"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Graph.output_node" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">output_node</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/graph.html#Graph.output_node"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/fx/graph.py#L1018"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Graph.output_node" title="Permalink to this definition">¶</a></dt>
 <dd><div class="admonition warning">
 <p class="admonition-title">Warning</p>
 <p>This API is experimental and is <em>NOT</em> backward-compatible.</p>
@@ -2637,7 +2637,7 @@ <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.Graph.placeholder">
-<span class="sig-name descname"><span class="pre">placeholder</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">type_expr=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">default_value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/graph.html#Graph.placeholder"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/graph.py#L1260"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Graph.placeholder" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">placeholder</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">type_expr=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">default_value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/graph.html#Graph.placeholder"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/fx/graph.py#L1260"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Graph.placeholder" title="Permalink to this definition">¶</a></dt>
 <dd><p>Insert a <code class="docutils literal notranslate"><span class="pre">placeholder</span></code> node into the Graph. A <code class="docutils literal notranslate"><span class="pre">placeholder</span></code> represents
 a function input.</p>
 <dl class="field-list simple">
@@ -2672,7 +2672,7 @@ <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.Graph.print_tabular">
-<span class="sig-name descname"><span class="pre">print_tabular</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/graph.html#Graph.print_tabular"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/graph.py#L1673"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Graph.print_tabular" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">print_tabular</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/graph.html#Graph.print_tabular"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/fx/graph.py#L1673"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Graph.print_tabular" title="Permalink to this definition">¶</a></dt>
 <dd><p>Prints the intermediate representation of the graph in tabular
 format. Note that this API requires the <code class="docutils literal notranslate"><span class="pre">tabulate</span></code> module to be
 installed.</p>
@@ -2684,7 +2684,7 @@ <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.Graph.process_inputs">
-<span class="sig-name descname"><span class="pre">process_inputs</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/graph.html#Graph.process_inputs"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/graph.py#L1159"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Graph.process_inputs" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">process_inputs</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/graph.html#Graph.process_inputs"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/fx/graph.py#L1159"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Graph.process_inputs" title="Permalink to this definition">¶</a></dt>
 <dd><p>Processes args so that they can be passed to the FX graph.</p>
 <div class="admonition warning">
 <p class="admonition-title">Warning</p>
@@ -2694,7 +2694,7 @@ <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.Graph.process_outputs">
-<span class="sig-name descname"><span class="pre">process_outputs</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">out</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/graph.html#Graph.process_outputs"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/graph.py#L1166"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Graph.process_outputs" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">process_outputs</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">out</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/graph.html#Graph.process_outputs"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/fx/graph.py#L1166"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Graph.process_outputs" title="Permalink to this definition">¶</a></dt>
 <dd><div class="admonition warning">
 <p class="admonition-title">Warning</p>
 <p>This API is experimental and is <em>NOT</em> backward-compatible.</p>
@@ -2703,7 +2703,7 @@ <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.Graph.python_code">
-<span class="sig-name descname"><span class="pre">python_code</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">root_module</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">include_stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">include_device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">colored</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/graph.html#Graph.python_code"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/graph.py#L1558"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Graph.python_code" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">python_code</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">root_module</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">include_stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">include_device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">colored</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/graph.html#Graph.python_code"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/fx/graph.py#L1558"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Graph.python_code" title="Permalink to this definition">¶</a></dt>
 <dd><p>Turn this <code class="docutils literal notranslate"><span class="pre">Graph</span></code> into valid Python code.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -2726,7 +2726,7 @@ <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.Graph.set_codegen">
-<span class="sig-name descname"><span class="pre">set_codegen</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">codegen</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/graph.html#Graph.set_codegen"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/graph.py#L1869"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Graph.set_codegen" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">set_codegen</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">codegen</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/graph.html#Graph.set_codegen"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/fx/graph.py#L1869"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Graph.set_codegen" title="Permalink to this definition">¶</a></dt>
 <dd><div class="admonition warning">
 <p class="admonition-title">Warning</p>
 <p>This API is experimental and is <em>NOT</em> backward-compatible.</p>
@@ -2739,7 +2739,7 @@ <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.fx.Node">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.fx.</span></span><span class="sig-name descname"><span class="pre">Node</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">graph</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">op</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kwargs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_type</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/node.html#Node"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/node.py#L192"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Node" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.fx.</span></span><span class="sig-name descname"><span class="pre">Node</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">graph</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">op</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kwargs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_type</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/node.html#Node"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/fx/node.py#L196"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Node" title="Permalink to this definition">¶</a></dt>
 <dd><p><code class="docutils literal notranslate"><span class="pre">Node</span></code> is the data structure that represents individual operations within
 a <code class="docutils literal notranslate"><span class="pre">Graph</span></code>. For the most part, Nodes represent callsites to various entities,
 such as operators, methods, and Modules (some exceptions include nodes that
@@ -2787,7 +2787,7 @@ <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.Node.append">
-<span class="sig-name descname"><span class="pre">append</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/node.html#Node.append"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/node.py#L420"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Node.append" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">append</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/node.html#Node.append"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/fx/node.py#L424"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Node.append" title="Permalink to this definition">¶</a></dt>
 <dd><p>Insert <code class="docutils literal notranslate"><span class="pre">x</span></code> after this node in the list of nodes in the graph.
 Equivalent to <code class="docutils literal notranslate"><span class="pre">self.next.prepend(x)</span></code></p>
 <dl class="field-list simple">
@@ -2813,7 +2813,7 @@ <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.Node.format_node">
-<span class="sig-name descname"><span class="pre">format_node</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">placeholder_names</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">maybe_return_typename</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/node.html#Node.format_node"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/node.py#L625"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Node.format_node" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">format_node</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">placeholder_names</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">maybe_return_typename</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/node.html#Node.format_node"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/fx/node.py#L629"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Node.format_node" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return a descriptive string representation of <code class="docutils literal notranslate"><span class="pre">self</span></code>.</p>
 <p>This method can be used with no arguments as a debugging
 utility.</p>
@@ -2856,7 +2856,7 @@ <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.Node.insert_arg">
-<span class="sig-name descname"><span class="pre">insert_arg</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">idx</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">arg</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/node.html#Node.insert_arg"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/node.py#L510"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Node.insert_arg" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">insert_arg</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">idx</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">arg</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/node.html#Node.insert_arg"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/fx/node.py#L514"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Node.insert_arg" title="Permalink to this definition">¶</a></dt>
 <dd><p>Insert an positional argument to the argument list with given index.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -2874,7 +2874,7 @@ <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.Node.is_impure">
-<span class="sig-name descname"><span class="pre">is_impure</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/node.html#Node.is_impure"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/node.py#L750"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Node.is_impure" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">is_impure</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/node.html#Node.is_impure"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/fx/node.py#L754"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Node.is_impure" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns whether this op is impure, i.e. if its op is a placeholder or
 output, or if a call_function or call_module which is impure.</p>
 <dl class="field-list simple">
@@ -2914,7 +2914,7 @@ <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.Node.normalized_arguments">
-<span class="sig-name descname"><span class="pre">normalized_arguments</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">root</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">arg_types</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kwarg_types</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">normalize_to_only_use_kwargs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/node.html#Node.normalized_arguments"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/node.py#L788"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Node.normalized_arguments" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">normalized_arguments</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">root</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">arg_types</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kwarg_types</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">normalize_to_only_use_kwargs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/node.html#Node.normalized_arguments"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/fx/node.py#L792"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Node.normalized_arguments" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns normalized arguments to Python targets. This means that
 <cite>args/kwargs</cite> will be matched up to the module/functional’s
 signature and return exclusively kwargs in positional order
@@ -2947,7 +2947,7 @@ <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.Node.prepend">
-<span class="sig-name descname"><span class="pre">prepend</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/node.html#Node.prepend"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/node.py#L371"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Node.prepend" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">prepend</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/node.html#Node.prepend"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/fx/node.py#L375"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Node.prepend" title="Permalink to this definition">¶</a></dt>
 <dd><p>Insert x before this node in the list of nodes in the graph. Example:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">Before</span><span class="p">:</span> <span class="n">p</span> <span class="o">-&gt;</span> <span class="bp">self</span>
         <span class="n">bx</span> <span class="o">-&gt;</span> <span class="n">x</span> <span class="o">-&gt;</span> <span class="n">ax</span>
@@ -2979,7 +2979,7 @@ <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.Node.replace_all_uses_with">
-<span class="sig-name descname"><span class="pre">replace_all_uses_with</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">replace_with</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">delete_user_cb=&lt;function</span> <span class="pre">Node.&lt;lambda&gt;&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">propagate_meta=False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/node.html#Node.replace_all_uses_with"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/node.py#L691"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Node.replace_all_uses_with" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">replace_all_uses_with</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">replace_with</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">delete_user_cb=&lt;function</span> <span class="pre">Node.&lt;lambda&gt;&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">propagate_meta=False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/node.html#Node.replace_all_uses_with"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/fx/node.py#L695"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Node.replace_all_uses_with" title="Permalink to this definition">¶</a></dt>
 <dd><p>Replace all uses of <code class="docutils literal notranslate"><span class="pre">self</span></code> in the Graph with the Node <code class="docutils literal notranslate"><span class="pre">replace_with</span></code>.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -3008,7 +3008,7 @@ <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.Node.replace_input_with">
-<span class="sig-name descname"><span class="pre">replace_input_with</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">old_input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">new_input</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/node.html#Node.replace_input_with"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/node.py#L833"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Node.replace_input_with" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">replace_input_with</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">old_input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">new_input</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/node.html#Node.replace_input_with"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/fx/node.py#L837"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Node.replace_input_with" title="Permalink to this definition">¶</a></dt>
 <dd><p>Loop through input nodes of <code class="docutils literal notranslate"><span class="pre">self</span></code>, and replace all instances of
 <code class="docutils literal notranslate"><span class="pre">old_input</span></code> with <code class="docutils literal notranslate"><span class="pre">new_input</span></code>.</p>
 <dl class="field-list simple">
@@ -3039,7 +3039,7 @@ <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.Node.update_arg">
-<span class="sig-name descname"><span class="pre">update_arg</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">idx</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">arg</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/node.html#Node.update_arg"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/node.py#L495"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Node.update_arg" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">update_arg</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">idx</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">arg</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/node.html#Node.update_arg"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/fx/node.py#L499"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Node.update_arg" title="Permalink to this definition">¶</a></dt>
 <dd><p>Update an existing positional argument to contain the new value
 <code class="docutils literal notranslate"><span class="pre">arg</span></code>. After calling, <code class="docutils literal notranslate"><span class="pre">self.args[idx]</span> <span class="pre">==</span> <span class="pre">arg</span></code>.</p>
 <dl class="field-list simple">
@@ -3058,7 +3058,7 @@ <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.Node.update_kwarg">
-<span class="sig-name descname"><span class="pre">update_kwarg</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">key</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">arg</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/node.html#Node.update_kwarg"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/node.py#L536"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Node.update_kwarg" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">update_kwarg</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">key</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">arg</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/node.html#Node.update_kwarg"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/fx/node.py#L540"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Node.update_kwarg" title="Permalink to this definition">¶</a></dt>
 <dd><p>Update an existing keyword argument to contain the new value
 <code class="docutils literal notranslate"><span class="pre">arg</span></code>. After calling, <code class="docutils literal notranslate"><span class="pre">self.kwargs[key]</span> <span class="pre">==</span> <span class="pre">arg</span></code>.</p>
 <dl class="field-list simple">
@@ -3079,7 +3079,7 @@ <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.fx.Tracer">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.fx.</span></span><span class="sig-name descname"><span class="pre">Tracer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">autowrap_modules</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">(math,)</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">autowrap_functions</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">()</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/_symbolic_trace.html#Tracer"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/_symbolic_trace.py#L230"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Tracer" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.fx.</span></span><span class="sig-name descname"><span class="pre">Tracer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">autowrap_modules</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">(math,)</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">autowrap_functions</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">()</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/_symbolic_trace.html#Tracer"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/fx/_symbolic_trace.py#L230"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Tracer" title="Permalink to this definition">¶</a></dt>
 <dd><blockquote>
 <div><p><code class="docutils literal notranslate"><span class="pre">Tracer</span></code> is the class that implements the symbolic tracing functionality
 of <code class="docutils literal notranslate"><span class="pre">torch.fx.symbolic_trace</span></code>. A call to <code class="docutils literal notranslate"><span class="pre">symbolic_trace(m)</span></code> is equivalent
@@ -3096,7 +3096,7 @@ <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.Tracer.call_module">
-<span class="sig-name descname"><span class="pre">call_module</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">m</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">forward</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/_symbolic_trace.html#Tracer.call_module"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/_symbolic_trace.py#L479"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Tracer.call_module" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">call_module</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">m</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">forward</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/_symbolic_trace.html#Tracer.call_module"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/fx/_symbolic_trace.py#L479"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Tracer.call_module" title="Permalink to this definition">¶</a></dt>
 <dd><p>Method that specifies the behavior of this <code class="docutils literal notranslate"><span class="pre">Tracer</span></code> when it encounters
 a call to an <code class="docutils literal notranslate"><span class="pre">nn.Module</span></code> instance.</p>
 <p>By default, the behavior is to check if the called module is a leaf module
@@ -3132,7 +3132,7 @@ <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.Tracer.create_arg">
-<span class="sig-name descname"><span class="pre">create_arg</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">a</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/_symbolic_trace.html#Tracer.create_arg"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/_symbolic_trace.py#L338"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Tracer.create_arg" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">create_arg</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">a</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/_symbolic_trace.html#Tracer.create_arg"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/fx/_symbolic_trace.py#L338"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Tracer.create_arg" title="Permalink to this definition">¶</a></dt>
 <dd><p>A method to specify the behavior of tracing when preparing values to
 be used as arguments to nodes in the <code class="docutils literal notranslate"><span class="pre">Graph</span></code>.</p>
 <p>By default, the behavior includes:</p>
@@ -3170,7 +3170,7 @@ <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.Tracer.create_args_for_root">
-<span class="sig-name descname"><span class="pre">create_args_for_root</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">root_fn</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">is_module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">concrete_args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/_symbolic_trace.html#Tracer.create_args_for_root"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/_symbolic_trace.py#L602"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Tracer.create_args_for_root" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">create_args_for_root</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">root_fn</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">is_module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">concrete_args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/_symbolic_trace.html#Tracer.create_args_for_root"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/fx/_symbolic_trace.py#L602"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Tracer.create_args_for_root" title="Permalink to this definition">¶</a></dt>
 <dd><p>Create <code class="docutils literal notranslate"><span class="pre">placeholder</span></code> nodes corresponding to the signature of the <code class="docutils literal notranslate"><span class="pre">root</span></code>
 Module. This method introspects root’s signature and emits those
 nodes accordingly, also supporting <code class="docutils literal notranslate"><span class="pre">*args</span></code> and <code class="docutils literal notranslate"><span class="pre">**kwargs</span></code>.</p>
@@ -3182,7 +3182,7 @@ <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.Tracer.create_node">
-<span class="sig-name descname"><span class="pre">create_node</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">kind</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kwargs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">type_expr</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/proxy.py#L145"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Tracer.create_node" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">create_node</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">kind</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kwargs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">type_expr</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/fx/proxy.py#L145"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Tracer.create_node" title="Permalink to this definition">¶</a></dt>
 <dd><p>Inserts a graph node given target, args, kwargs, and name.</p>
 <p>This method can be overridden to do extra checking, validation, or
 modification of values used in node creation. For example, one might
@@ -3200,7 +3200,7 @@ <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.Tracer.create_proxy">
-<span class="sig-name descname"><span class="pre">create_proxy</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">kind</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kwargs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">type_expr</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">proxy_factory_fn</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/proxy.py#L211"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Tracer.create_proxy" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">create_proxy</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">kind</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kwargs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">type_expr</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">proxy_factory_fn</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/fx/proxy.py#L211"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Tracer.create_proxy" title="Permalink to this definition">¶</a></dt>
 <dd><p>Create a Node from the given arguments, then return the Node
 wrapped in a Proxy object.</p>
 <p>If kind = ‘placeholder’, then we’re creating a Node that
@@ -3217,7 +3217,7 @@ <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.Tracer.get_fresh_qualname">
-<span class="sig-name descname"><span class="pre">get_fresh_qualname</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">prefix</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/_symbolic_trace.html#Tracer.get_fresh_qualname"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/_symbolic_trace.py#L312"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Tracer.get_fresh_qualname" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">get_fresh_qualname</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">prefix</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/_symbolic_trace.html#Tracer.get_fresh_qualname"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/fx/_symbolic_trace.py#L312"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Tracer.get_fresh_qualname" title="Permalink to this definition">¶</a></dt>
 <dd><p>Gets a fresh name for a prefix and returns it. This function ensures
 that it will not clash with an existing attribute on the graph.</p>
 <div class="admonition note">
@@ -3233,7 +3233,7 @@ <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.Tracer.getattr">
-<span class="sig-name descname"><span class="pre">getattr</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">attr</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">attr_val</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">parameter_proxy_cache</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/_symbolic_trace.html#Tracer.getattr"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/_symbolic_trace.py#L538"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Tracer.getattr" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">getattr</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">attr</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">attr_val</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">parameter_proxy_cache</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/_symbolic_trace.html#Tracer.getattr"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/fx/_symbolic_trace.py#L538"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Tracer.getattr" title="Permalink to this definition">¶</a></dt>
 <dd><p>Method that specifies the behavior of this <code class="docutils literal notranslate"><span class="pre">Tracer</span></code> when we call getattr
 on a call to an <code class="docutils literal notranslate"><span class="pre">nn.Module</span></code> instance.</p>
 <p>By default, the behavior is to return a proxy value for the attribute. It
@@ -3261,7 +3261,7 @@ <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.Tracer.is_leaf_module">
-<span class="sig-name descname"><span class="pre">is_leaf_module</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">m</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">module_qualified_name</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/_symbolic_trace.html#Tracer.is_leaf_module"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/_symbolic_trace.py#L427"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Tracer.is_leaf_module" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">is_leaf_module</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">m</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">module_qualified_name</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/_symbolic_trace.html#Tracer.is_leaf_module"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/fx/_symbolic_trace.py#L427"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Tracer.is_leaf_module" title="Permalink to this definition">¶</a></dt>
 <dd><p>A method to specify whether a given <code class="docutils literal notranslate"><span class="pre">nn.Module</span></code> is a “leaf” module.</p>
 <p>Leaf modules are the atomic units that appear in
 the IR, referenced by <code class="docutils literal notranslate"><span class="pre">call_module</span></code> calls. By default,
@@ -3291,7 +3291,7 @@ <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.Tracer.iter">
-<span class="sig-name descname"><span class="pre">iter</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">obj</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/proxy.py#L366"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Tracer.iter" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">iter</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">obj</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/fx/proxy.py#L366"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Tracer.iter" title="Permalink to this definition">¶</a></dt>
 <dd><dl class="simple">
 <dt>Called when a proxy object is being iterated over, such as</dt><dd><p>when used in control flow.  Normally we don’t know what to do because
 we don’t know the value of the proxy, but a custom tracer can attach more
@@ -3311,7 +3311,7 @@ <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.Tracer.keys">
-<span class="sig-name descname"><span class="pre">keys</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">obj</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/proxy.py#L384"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Tracer.keys" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">keys</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">obj</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/fx/proxy.py#L384"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Tracer.keys" title="Permalink to this definition">¶</a></dt>
 <dd><dl class="simple">
 <dt>Called when a proxy object is has the keys() method called.</dt><dd><p>This is what happens when ** is called on a proxy. This should return an
 iterator it ** is suppose to work in your custom tracer.</p>
@@ -3330,7 +3330,7 @@ <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.Tracer.path_of_module">
-<span class="sig-name descname"><span class="pre">path_of_module</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mod</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/_symbolic_trace.html#Tracer.path_of_module"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/_symbolic_trace.py#L452"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Tracer.path_of_module" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">path_of_module</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mod</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/_symbolic_trace.html#Tracer.path_of_module"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/fx/_symbolic_trace.py#L452"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Tracer.path_of_module" title="Permalink to this definition">¶</a></dt>
 <dd><p>Helper method to find the qualified name of <code class="docutils literal notranslate"><span class="pre">mod</span></code> in the Module hierarchy
 of <code class="docutils literal notranslate"><span class="pre">root</span></code>. For example, if <code class="docutils literal notranslate"><span class="pre">root</span></code> has a submodule named <code class="docutils literal notranslate"><span class="pre">foo</span></code>, which has
 a submodule named <code class="docutils literal notranslate"><span class="pre">bar</span></code>, passing <code class="docutils literal notranslate"><span class="pre">bar</span></code> into this function will return
@@ -3351,7 +3351,7 @@ <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.Tracer.proxy">
-<span class="sig-name descname"><span class="pre">proxy</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">node</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/proxy.py#L207"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Tracer.proxy" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">proxy</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">node</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/fx/proxy.py#L207"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Tracer.proxy" title="Permalink to this definition">¶</a></dt>
 <dd><div class="admonition note">
 <p class="admonition-title">Note</p>
 <p>Backwards-compatibility for this API is guaranteed.</p>
@@ -3365,7 +3365,7 @@ <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.Tracer.to_bool">
-<span class="sig-name descname"><span class="pre">to_bool</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">obj</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/proxy.py#L355"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Tracer.to_bool" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">to_bool</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">obj</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/fx/proxy.py#L355"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Tracer.to_bool" title="Permalink to this definition">¶</a></dt>
 <dd><dl class="simple">
 <dt>Called when a proxy object is being converted to a boolean, such as</dt><dd><p>when used in control flow.  Normally we don’t know what to do because
 we don’t know the value of the proxy, but a custom tracer can attach more
@@ -3385,7 +3385,7 @@ <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.Tracer.trace">
-<span class="sig-name descname"><span class="pre">trace</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">root</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">concrete_args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/_symbolic_trace.html#Tracer.trace"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/_symbolic_trace.py#L700"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Tracer.trace" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">trace</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">root</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">concrete_args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/_symbolic_trace.html#Tracer.trace"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/fx/_symbolic_trace.py#L700"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Tracer.trace" title="Permalink to this definition">¶</a></dt>
 <dd><p>Trace <code class="docutils literal notranslate"><span class="pre">root</span></code> and return the corresponding FX <code class="docutils literal notranslate"><span class="pre">Graph</span></code> representation. <code class="docutils literal notranslate"><span class="pre">root</span></code>
 can either be an <code class="docutils literal notranslate"><span class="pre">nn.Module</span></code> instance or a Python callable.</p>
 <p>Note that after this call, <code class="docutils literal notranslate"><span class="pre">self.root</span></code> may be different from the <code class="docutils literal notranslate"><span class="pre">root</span></code> passed
@@ -3420,7 +3420,7 @@ <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.fx.Proxy">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.fx.</span></span><span class="sig-name descname"><span class="pre">Proxy</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">node</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tracer</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/proxy.html#Proxy"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/proxy.py#L414"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Proxy" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.fx.</span></span><span class="sig-name descname"><span class="pre">Proxy</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">node</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tracer</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/proxy.html#Proxy"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/fx/proxy.py#L414"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Proxy" title="Permalink to this definition">¶</a></dt>
 <dd><p><code class="docutils literal notranslate"><span class="pre">Proxy</span></code> objects are <code class="docutils literal notranslate"><span class="pre">Node</span></code> wrappers that flow through the
 program during symbolic tracing and record all the operations
 (<code class="docutils literal notranslate"><span class="pre">torch</span></code> function calls, method calls, operators) that they touch
@@ -3453,7 +3453,7 @@ <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.fx.Interpreter">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.fx.</span></span><span class="sig-name descname"><span class="pre">Interpreter</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">garbage_collect_values</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">graph</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/interpreter.html#Interpreter"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/interpreter.py#L27"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Interpreter" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.fx.</span></span><span class="sig-name descname"><span class="pre">Interpreter</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">garbage_collect_values</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">graph</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/interpreter.html#Interpreter"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/fx/interpreter.py#L27"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Interpreter" title="Permalink to this definition">¶</a></dt>
 <dd><p>An Interpreter executes an FX graph Node-by-Node. This pattern
 can be useful for many things, including writing code
 transformations as well as analysis passes.</p>
@@ -3517,7 +3517,7 @@ <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.Interpreter.boxed_run">
-<span class="sig-name descname"><span class="pre">boxed_run</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">args_list</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/interpreter.html#Interpreter.boxed_run"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/interpreter.py#L194"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Interpreter.boxed_run" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">boxed_run</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">args_list</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/interpreter.html#Interpreter.boxed_run"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/fx/interpreter.py#L194"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Interpreter.boxed_run" title="Permalink to this definition">¶</a></dt>
 <dd><p>Run <cite>module</cite> via interpretation and return the result.  This uses the “boxed”
 calling convention, where you pass a list of arguments, which will be cleared
 by the interpreter.  This ensures that input tensors are promptly deallocated.</p>
@@ -3529,7 +3529,7 @@ <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.Interpreter.call_function">
-<span class="sig-name descname"><span class="pre">call_function</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">target</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/interpreter.html#Interpreter.call_function"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/interpreter.py#L294"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Interpreter.call_function" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">call_function</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">target</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/interpreter.html#Interpreter.call_function"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/fx/interpreter.py#L294"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Interpreter.call_function" title="Permalink to this definition">¶</a></dt>
 <dd><p>Execute a <code class="docutils literal notranslate"><span class="pre">call_function</span></code> node and return the result.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -3557,7 +3557,7 @@ <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.Interpreter.call_method">
-<span class="sig-name descname"><span class="pre">call_method</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">target</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/interpreter.html#Interpreter.call_method"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/interpreter.py#L316"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Interpreter.call_method" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">call_method</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">target</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/interpreter.html#Interpreter.call_method"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/fx/interpreter.py#L316"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Interpreter.call_method" title="Permalink to this definition">¶</a></dt>
 <dd><p>Execute a <code class="docutils literal notranslate"><span class="pre">call_method</span></code> node and return the result.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -3585,7 +3585,7 @@ <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.Interpreter.call_module">
-<span class="sig-name descname"><span class="pre">call_module</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">target</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/interpreter.html#Interpreter.call_module"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/interpreter.py#L340"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Interpreter.call_module" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">call_module</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">target</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/interpreter.html#Interpreter.call_module"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/fx/interpreter.py#L340"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Interpreter.call_module" title="Permalink to this definition">¶</a></dt>
 <dd><p>Execute a <code class="docutils literal notranslate"><span class="pre">call_module</span></code> node and return the result.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -3613,7 +3613,7 @@ <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.Interpreter.fetch_args_kwargs_from_env">
-<span class="sig-name descname"><span class="pre">fetch_args_kwargs_from_env</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/interpreter.html#Interpreter.fetch_args_kwargs_from_env"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/interpreter.py#L407"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Interpreter.fetch_args_kwargs_from_env" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">fetch_args_kwargs_from_env</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/interpreter.html#Interpreter.fetch_args_kwargs_from_env"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/fx/interpreter.py#L407"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Interpreter.fetch_args_kwargs_from_env" title="Permalink to this definition">¶</a></dt>
 <dd><p>Fetch the concrete values of <code class="docutils literal notranslate"><span class="pre">args</span></code> and <code class="docutils literal notranslate"><span class="pre">kwargs</span></code> of node <code class="docutils literal notranslate"><span class="pre">n</span></code>
 from the current execution environment.</p>
 <dl class="field-list simple">
@@ -3635,7 +3635,7 @@ <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.Interpreter.fetch_attr">
-<span class="sig-name descname"><span class="pre">fetch_attr</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">target</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/interpreter.html#Interpreter.fetch_attr"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/interpreter.py#L386"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Interpreter.fetch_attr" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">fetch_attr</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">target</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/interpreter.html#Interpreter.fetch_attr"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/fx/interpreter.py#L386"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Interpreter.fetch_attr" title="Permalink to this definition">¶</a></dt>
 <dd><p>Fetch an attribute from the <code class="docutils literal notranslate"><span class="pre">Module</span></code> hierarchy of <code class="docutils literal notranslate"><span class="pre">self.module</span></code>.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -3656,7 +3656,7 @@ <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.Interpreter.get_attr">
-<span class="sig-name descname"><span class="pre">get_attr</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">target</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/interpreter.html#Interpreter.get_attr"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/interpreter.py#L273"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Interpreter.get_attr" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">get_attr</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">target</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/interpreter.html#Interpreter.get_attr"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/fx/interpreter.py#L273"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Interpreter.get_attr" title="Permalink to this definition">¶</a></dt>
 <dd><p>Execute a <code class="docutils literal notranslate"><span class="pre">get_attr</span></code> node. Will retrieve an attribute
 value from the <code class="docutils literal notranslate"><span class="pre">Module</span></code> hierarchy of <code class="docutils literal notranslate"><span class="pre">self.module</span></code>.</p>
 <dl class="field-list simple">
@@ -3684,7 +3684,7 @@ <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.Interpreter.map_nodes_to_values">
-<span class="sig-name descname"><span class="pre">map_nodes_to_values</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/interpreter.html#Interpreter.map_nodes_to_values"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/interpreter.py#L425"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Interpreter.map_nodes_to_values" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">map_nodes_to_values</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/interpreter.html#Interpreter.map_nodes_to_values"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/fx/interpreter.py#L425"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Interpreter.map_nodes_to_values" title="Permalink to this definition">¶</a></dt>
 <dd><p>Recursively descend through <code class="docutils literal notranslate"><span class="pre">args</span></code> and look up the concrete value
 for each <code class="docutils literal notranslate"><span class="pre">Node</span></code> in the current execution environment.</p>
 <dl class="field-list simple">
@@ -3706,7 +3706,7 @@ <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.Interpreter.output">
-<span class="sig-name descname"><span class="pre">output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">target</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/interpreter.html#Interpreter.output"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/interpreter.py#L365"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Interpreter.output" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">target</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/interpreter.html#Interpreter.output"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/fx/interpreter.py#L365"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Interpreter.output" title="Permalink to this definition">¶</a></dt>
 <dd><p>Execute an <code class="docutils literal notranslate"><span class="pre">output</span></code> node. This really just retrieves
 the value referenced by the <code class="docutils literal notranslate"><span class="pre">output</span></code> node and returns it.</p>
 <dl class="field-list simple">
@@ -3734,7 +3734,7 @@ <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.Interpreter.placeholder">
-<span class="sig-name descname"><span class="pre">placeholder</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">target</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/interpreter.html#Interpreter.placeholder"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/interpreter.py#L237"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Interpreter.placeholder" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">placeholder</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">target</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/interpreter.html#Interpreter.placeholder"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/fx/interpreter.py#L237"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Interpreter.placeholder" title="Permalink to this definition">¶</a></dt>
 <dd><p>Execute a <code class="docutils literal notranslate"><span class="pre">placeholder</span></code> node. Note that this is stateful:
 <code class="docutils literal notranslate"><span class="pre">Interpreter</span></code> maintains an internal iterator over
 arguments passed to <code class="docutils literal notranslate"><span class="pre">run</span></code> and this method returns
@@ -3764,7 +3764,7 @@ <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.Interpreter.run">
-<span class="sig-name descname"><span class="pre">run</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">initial_env</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">enable_io_processing</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/interpreter.html#Interpreter.run"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/interpreter.py#L121"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Interpreter.run" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">run</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">initial_env</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">enable_io_processing</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/interpreter.html#Interpreter.run"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/fx/interpreter.py#L121"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Interpreter.run" title="Permalink to this definition">¶</a></dt>
 <dd><p>Run <cite>module</cite> via interpretation and return the result.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -3793,7 +3793,7 @@ <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.Interpreter.run_node">
-<span class="sig-name descname"><span class="pre">run_node</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/interpreter.html#Interpreter.run_node"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/interpreter.py#L216"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Interpreter.run_node" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">run_node</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/interpreter.html#Interpreter.run_node"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/fx/interpreter.py#L216"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Interpreter.run_node" title="Permalink to this definition">¶</a></dt>
 <dd><p>Run a specific node <code class="docutils literal notranslate"><span class="pre">n</span></code> and return the result.
 Calls into placeholder, get_attr, call_function,
 call_method, call_module, or output depending
@@ -3819,7 +3819,7 @@ <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.fx.Transformer">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.fx.</span></span><span class="sig-name descname"><span class="pre">Transformer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/interpreter.html#Transformer"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/interpreter.py#L448"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Transformer" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.fx.</span></span><span class="sig-name descname"><span class="pre">Transformer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/interpreter.html#Transformer"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/fx/interpreter.py#L448"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Transformer" title="Permalink to this definition">¶</a></dt>
 <dd><p><code class="docutils literal notranslate"><span class="pre">Transformer</span></code> is a special type of interpreter that produces a
 new <code class="docutils literal notranslate"><span class="pre">Module</span></code>. It exposes a <code class="docutils literal notranslate"><span class="pre">transform()</span></code> method that returns
 the transformed <code class="docutils literal notranslate"><span class="pre">Module</span></code>. <code class="docutils literal notranslate"><span class="pre">Transformer</span></code> does not require
@@ -3868,7 +3868,7 @@ <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.Transformer.call_function">
-<span class="sig-name descname"><span class="pre">call_function</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">target</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/interpreter.html#Transformer.call_function"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/interpreter.py#L562"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Transformer.call_function" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">call_function</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">target</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/interpreter.html#Transformer.call_function"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/fx/interpreter.py#L562"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Transformer.call_function" title="Permalink to this definition">¶</a></dt>
 <dd><div class="admonition note">
 <p class="admonition-title">Note</p>
 <p>Backwards-compatibility for this API is guaranteed.</p>
@@ -3882,7 +3882,7 @@ <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.Transformer.call_module">
-<span class="sig-name descname"><span class="pre">call_module</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">target</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/interpreter.html#Transformer.call_module"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/interpreter.py#L553"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Transformer.call_module" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">call_module</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">target</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/interpreter.html#Transformer.call_module"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/fx/interpreter.py#L553"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Transformer.call_module" title="Permalink to this definition">¶</a></dt>
 <dd><div class="admonition note">
 <p class="admonition-title">Note</p>
 <p>Backwards-compatibility for this API is guaranteed.</p>
@@ -3896,7 +3896,7 @@ <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.Transformer.get_attr">
-<span class="sig-name descname"><span class="pre">get_attr</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">target</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/interpreter.html#Transformer.get_attr"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/interpreter.py#L534"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Transformer.get_attr" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">get_attr</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">target</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/interpreter.html#Transformer.get_attr"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/fx/interpreter.py#L534"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Transformer.get_attr" title="Permalink to this definition">¶</a></dt>
 <dd><p>Execute a <code class="docutils literal notranslate"><span class="pre">get_attr</span></code> node. In <code class="docutils literal notranslate"><span class="pre">Transformer</span></code>, this is
 overridden to insert a new <code class="docutils literal notranslate"><span class="pre">get_attr</span></code> node into the output
 graph.</p>
@@ -3922,7 +3922,7 @@ <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.Transformer.placeholder">
-<span class="sig-name descname"><span class="pre">placeholder</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">target</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/interpreter.html#Transformer.placeholder"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/interpreter.py#L512"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Transformer.placeholder" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">placeholder</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">target</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/interpreter.html#Transformer.placeholder"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/fx/interpreter.py#L512"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Transformer.placeholder" title="Permalink to this definition">¶</a></dt>
 <dd><p>Execute a <code class="docutils literal notranslate"><span class="pre">placeholder</span></code> node. In <code class="docutils literal notranslate"><span class="pre">Transformer</span></code>, this is
 overridden to insert a new <code class="docutils literal notranslate"><span class="pre">placeholder</span></code> into the output
 graph.</p>
@@ -3948,7 +3948,7 @@ <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.Transformer.transform">
-<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/interpreter.html#Transformer.transform"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/interpreter.py#L569"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Transformer.transform" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">transform</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/interpreter.html#Transformer.transform"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/fx/interpreter.py#L569"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.Transformer.transform" title="Permalink to this definition">¶</a></dt>
 <dd><p>Transform <code class="docutils literal notranslate"><span class="pre">self.module</span></code> and return the transformed
 <code class="docutils literal notranslate"><span class="pre">GraphModule</span></code>.</p>
 <div class="admonition note">
@@ -3966,7 +3966,7 @@ <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.fx.replace_pattern">
-<span class="sig-prename descclassname"><span class="pre">torch.fx.</span></span><span class="sig-name descname"><span class="pre">replace_pattern</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">gm</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pattern</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">replacement</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/subgraph_rewriter.html#replace_pattern"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/subgraph_rewriter.py#L94"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.replace_pattern" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.fx.</span></span><span class="sig-name descname"><span class="pre">replace_pattern</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">gm</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pattern</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">replacement</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/fx/subgraph_rewriter.html#replace_pattern"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/fx/subgraph_rewriter.py#L94"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.replace_pattern" title="Permalink to this definition">¶</a></dt>
 <dd><p>Matches all possible non-overlapping sets of operators and their
 data dependencies (<code class="docutils literal notranslate"><span class="pre">pattern</span></code>) in the Graph of a GraphModule
 (<code class="docutils literal notranslate"><span class="pre">gm</span></code>), then replaces each of these matched subgraphs with another
diff --git a/main/generated/exportdb/index.html b/main/generated/exportdb/index.html
index 418fc4c699e..9697280c19d 100644
--- a/main/generated/exportdb/index.html
+++ b/main/generated/exportdb/index.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -1017,7 +1017,7 @@ <h3>cond_branch_nonlocal_variables<a class="headerlink" href="#cond-branch-nonlo
 <h3>cond_closed_over_variable<a class="headerlink" href="#cond-closed-over-variable" title="Permalink to this heading">¶</a></h3>
 <div class="admonition note">
 <p class="admonition-title">Note</p>
-<p>Tags: <a class="reference internal" href="torch.cond.html"><span class="doc">torch.cond</span></a>, <a class="reference internal" href="python.closure.html"><span class="doc">python.closure</span></a></p>
+<p>Tags: <a class="reference internal" href="python.closure.html"><span class="doc">python.closure</span></a>, <a class="reference internal" href="torch.cond.html"><span class="doc">torch.cond</span></a></p>
 <p>Support Level: SUPPORTED</p>
 </div>
 <p>Original source code:</p>
@@ -1207,7 +1207,7 @@ <h3>cond_predicate<a class="headerlink" href="#cond-predicate" title="Permalink
 <h3>constrain_as_size_example<a class="headerlink" href="#constrain-as-size-example" title="Permalink to this heading">¶</a></h3>
 <div class="admonition note">
 <p class="admonition-title">Note</p>
-<p>Tags: <a class="reference internal" href="torch.dynamic-value.html"><span class="doc">torch.dynamic-value</span></a>, <a class="reference internal" href="torch.escape-hatch.html"><span class="doc">torch.escape-hatch</span></a></p>
+<p>Tags: <a class="reference internal" href="torch.escape-hatch.html"><span class="doc">torch.escape-hatch</span></a>, <a class="reference internal" href="torch.dynamic-value.html"><span class="doc">torch.dynamic-value</span></a></p>
 <p>Support Level: SUPPORTED</p>
 </div>
 <p>Original source code:</p>
@@ -1267,7 +1267,7 @@ <h3>constrain_as_size_example<a class="headerlink" href="#constrain-as-size-exam
 <h3>constrain_as_value_example<a class="headerlink" href="#constrain-as-value-example" title="Permalink to this heading">¶</a></h3>
 <div class="admonition note">
 <p class="admonition-title">Note</p>
-<p>Tags: <a class="reference internal" href="torch.dynamic-value.html"><span class="doc">torch.dynamic-value</span></a>, <a class="reference internal" href="torch.escape-hatch.html"><span class="doc">torch.escape-hatch</span></a></p>
+<p>Tags: <a class="reference internal" href="torch.escape-hatch.html"><span class="doc">torch.escape-hatch</span></a>, <a class="reference internal" href="torch.dynamic-value.html"><span class="doc">torch.dynamic-value</span></a></p>
 <p>Support Level: SUPPORTED</p>
 </div>
 <p>Original source code:</p>
@@ -1746,7 +1746,7 @@ <h3>fn_with_kwargs<a class="headerlink" href="#fn-with-kwargs" title="Permalink
 <h3>list_contains<a class="headerlink" href="#list-contains" title="Permalink to this heading">¶</a></h3>
 <div class="admonition note">
 <p class="admonition-title">Note</p>
-<p>Tags: <a class="reference internal" href="python.assert.html"><span class="doc">python.assert</span></a>, <a class="reference internal" href="torch.dynamic-shape.html"><span class="doc">torch.dynamic-shape</span></a>, <a class="reference internal" href="python.data-structure.html"><span class="doc">python.data-structure</span></a></p>
+<p>Tags: <a class="reference internal" href="python.assert.html"><span class="doc">python.assert</span></a>, <a class="reference internal" href="python.data-structure.html"><span class="doc">python.data-structure</span></a>, <a class="reference internal" href="torch.dynamic-shape.html"><span class="doc">torch.dynamic-shape</span></a></p>
 <p>Support Level: SUPPORTED</p>
 </div>
 <p>Original source code:</p>
@@ -2460,7 +2460,7 @@ <h3>unsupported_operator<a class="headerlink" href="#unsupported-operator" title
 </pre></div>
 </div>
 <p>Result:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">Unsupported</span><span class="p">:</span> <span class="n">torch</span><span class="o">.*</span> <span class="n">op</span> <span class="n">returned</span> <span class="n">non</span><span class="o">-</span><span class="n">Tensor</span> <span class="nb">int</span> <span class="n">call_function</span> <span class="o">&lt;</span><span class="n">function</span> <span class="n">sym_min</span> <span class="n">at</span> <span class="mh">0x7ff65abf64c0</span><span class="o">&gt;</span>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">Unsupported</span><span class="p">:</span> <span class="n">torch</span><span class="o">.*</span> <span class="n">op</span> <span class="n">returned</span> <span class="n">non</span><span class="o">-</span><span class="n">Tensor</span> <span class="nb">int</span> <span class="n">call_function</span> <span class="o">&lt;</span><span class="n">function</span> <span class="n">sym_min</span> <span class="n">at</span> <span class="mh">0x7f7a79cff3a0</span><span class="o">&gt;</span>
 </pre></div>
 </div>
 </div>
diff --git a/main/generated/exportdb/python.assert.html b/main/generated/exportdb/python.assert.html
index 1840fd25642..e77a1828aa4 100644
--- a/main/generated/exportdb/python.assert.html
+++ b/main/generated/exportdb/python.assert.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -658,7 +658,7 @@ <h2>dynamic_shape_assert<a class="headerlink" href="#dynamic-shape-assert" title
 <h2>list_contains<a class="headerlink" href="#list-contains" title="Permalink to this heading">¶</a></h2>
 <div class="admonition note">
 <p class="admonition-title">Note</p>
-<p>Tags: <a class="reference internal" href="#"><span class="doc">python.assert</span></a>, <a class="reference internal" href="torch.dynamic-shape.html"><span class="doc">torch.dynamic-shape</span></a>, <a class="reference internal" href="python.data-structure.html"><span class="doc">python.data-structure</span></a></p>
+<p>Tags: <a class="reference internal" href="#"><span class="doc">python.assert</span></a>, <a class="reference internal" href="python.data-structure.html"><span class="doc">python.data-structure</span></a>, <a class="reference internal" href="torch.dynamic-shape.html"><span class="doc">torch.dynamic-shape</span></a></p>
 <p>Support Level: SUPPORTED</p>
 </div>
 <p>Original source code:</p>
diff --git a/main/generated/exportdb/python.builtin.html b/main/generated/exportdb/python.builtin.html
index f22a4db0c21..26476470239 100644
--- a/main/generated/exportdb/python.builtin.html
+++ b/main/generated/exportdb/python.builtin.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/exportdb/python.closure.html b/main/generated/exportdb/python.closure.html
index bbffffd91d6..4b0eea51561 100644
--- a/main/generated/exportdb/python.closure.html
+++ b/main/generated/exportdb/python.closure.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -616,7 +616,7 @@ <h1>python.closure<a class="headerlink" href="#python-closure" title="Permalink
 <h2>cond_closed_over_variable<a class="headerlink" href="#cond-closed-over-variable" title="Permalink to this heading">¶</a></h2>
 <div class="admonition note">
 <p class="admonition-title">Note</p>
-<p>Tags: <a class="reference internal" href="torch.cond.html"><span class="doc">torch.cond</span></a>, <a class="reference internal" href="#"><span class="doc">python.closure</span></a></p>
+<p>Tags: <a class="reference internal" href="#"><span class="doc">python.closure</span></a>, <a class="reference internal" href="torch.cond.html"><span class="doc">torch.cond</span></a></p>
 <p>Support Level: SUPPORTED</p>
 </div>
 <p>Original source code:</p>
diff --git a/main/generated/exportdb/python.context-manager.html b/main/generated/exportdb/python.context-manager.html
index 71cec6fd011..f683d3b24d2 100644
--- a/main/generated/exportdb/python.context-manager.html
+++ b/main/generated/exportdb/python.context-manager.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/exportdb/python.control-flow.html b/main/generated/exportdb/python.control-flow.html
index d6ab5bcfa1d..3ab628191bb 100644
--- a/main/generated/exportdb/python.control-flow.html
+++ b/main/generated/exportdb/python.control-flow.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/exportdb/python.data-structure.html b/main/generated/exportdb/python.data-structure.html
index b472b9e918d..1983342114c 100644
--- a/main/generated/exportdb/python.data-structure.html
+++ b/main/generated/exportdb/python.data-structure.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -724,7 +724,7 @@ <h2>fn_with_kwargs<a class="headerlink" href="#fn-with-kwargs" title="Permalink
 <h2>list_contains<a class="headerlink" href="#list-contains" title="Permalink to this heading">¶</a></h2>
 <div class="admonition note">
 <p class="admonition-title">Note</p>
-<p>Tags: <a class="reference internal" href="python.assert.html"><span class="doc">python.assert</span></a>, <a class="reference internal" href="torch.dynamic-shape.html"><span class="doc">torch.dynamic-shape</span></a>, <a class="reference internal" href="#"><span class="doc">python.data-structure</span></a></p>
+<p>Tags: <a class="reference internal" href="python.assert.html"><span class="doc">python.assert</span></a>, <a class="reference internal" href="#"><span class="doc">python.data-structure</span></a>, <a class="reference internal" href="torch.dynamic-shape.html"><span class="doc">torch.dynamic-shape</span></a></p>
 <p>Support Level: SUPPORTED</p>
 </div>
 <p>Original source code:</p>
diff --git a/main/generated/exportdb/python.object-model.html b/main/generated/exportdb/python.object-model.html
index 6b1032cf81e..59c529f0d9f 100644
--- a/main/generated/exportdb/python.object-model.html
+++ b/main/generated/exportdb/python.object-model.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/exportdb/torch.cond.html b/main/generated/exportdb/torch.cond.html
index 7bd14810780..a146b43f348 100644
--- a/main/generated/exportdb/torch.cond.html
+++ b/main/generated/exportdb/torch.cond.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -841,7 +841,7 @@ <h2>cond_branch_nonlocal_variables<a class="headerlink" href="#cond-branch-nonlo
 <h2>cond_closed_over_variable<a class="headerlink" href="#cond-closed-over-variable" title="Permalink to this heading">¶</a></h2>
 <div class="admonition note">
 <p class="admonition-title">Note</p>
-<p>Tags: <a class="reference internal" href="#"><span class="doc">torch.cond</span></a>, <a class="reference internal" href="python.closure.html"><span class="doc">python.closure</span></a></p>
+<p>Tags: <a class="reference internal" href="python.closure.html"><span class="doc">python.closure</span></a>, <a class="reference internal" href="#"><span class="doc">torch.cond</span></a></p>
 <p>Support Level: SUPPORTED</p>
 </div>
 <p>Original source code:</p>
diff --git a/main/generated/exportdb/torch.dynamic-shape.html b/main/generated/exportdb/torch.dynamic-shape.html
index 8c9fc1e35c0..fda7b85b693 100644
--- a/main/generated/exportdb/torch.dynamic-shape.html
+++ b/main/generated/exportdb/torch.dynamic-shape.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -1228,7 +1228,7 @@ <h2>dynamic_shape_view<a class="headerlink" href="#dynamic-shape-view" title="Pe
 <h2>list_contains<a class="headerlink" href="#list-contains" title="Permalink to this heading">¶</a></h2>
 <div class="admonition note">
 <p class="admonition-title">Note</p>
-<p>Tags: <a class="reference internal" href="python.assert.html"><span class="doc">python.assert</span></a>, <a class="reference internal" href="#"><span class="doc">torch.dynamic-shape</span></a>, <a class="reference internal" href="python.data-structure.html"><span class="doc">python.data-structure</span></a></p>
+<p>Tags: <a class="reference internal" href="python.assert.html"><span class="doc">python.assert</span></a>, <a class="reference internal" href="python.data-structure.html"><span class="doc">python.data-structure</span></a>, <a class="reference internal" href="#"><span class="doc">torch.dynamic-shape</span></a></p>
 <p>Support Level: SUPPORTED</p>
 </div>
 <p>Original source code:</p>
diff --git a/main/generated/exportdb/torch.dynamic-value.html b/main/generated/exportdb/torch.dynamic-value.html
index 70972b26611..13a4216866a 100644
--- a/main/generated/exportdb/torch.dynamic-value.html
+++ b/main/generated/exportdb/torch.dynamic-value.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -616,7 +616,7 @@ <h1>torch.dynamic-value<a class="headerlink" href="#torch-dynamic-value" title="
 <h2>constrain_as_size_example<a class="headerlink" href="#constrain-as-size-example" title="Permalink to this heading">¶</a></h2>
 <div class="admonition note">
 <p class="admonition-title">Note</p>
-<p>Tags: <a class="reference internal" href="#"><span class="doc">torch.dynamic-value</span></a>, <a class="reference internal" href="torch.escape-hatch.html"><span class="doc">torch.escape-hatch</span></a></p>
+<p>Tags: <a class="reference internal" href="torch.escape-hatch.html"><span class="doc">torch.escape-hatch</span></a>, <a class="reference internal" href="#"><span class="doc">torch.dynamic-value</span></a></p>
 <p>Support Level: SUPPORTED</p>
 </div>
 <p>Original source code:</p>
@@ -676,7 +676,7 @@ <h2>constrain_as_size_example<a class="headerlink" href="#constrain-as-size-exam
 <h2>constrain_as_value_example<a class="headerlink" href="#constrain-as-value-example" title="Permalink to this heading">¶</a></h2>
 <div class="admonition note">
 <p class="admonition-title">Note</p>
-<p>Tags: <a class="reference internal" href="#"><span class="doc">torch.dynamic-value</span></a>, <a class="reference internal" href="torch.escape-hatch.html"><span class="doc">torch.escape-hatch</span></a></p>
+<p>Tags: <a class="reference internal" href="torch.escape-hatch.html"><span class="doc">torch.escape-hatch</span></a>, <a class="reference internal" href="#"><span class="doc">torch.dynamic-value</span></a></p>
 <p>Support Level: SUPPORTED</p>
 </div>
 <p>Original source code:</p>
diff --git a/main/generated/exportdb/torch.escape-hatch.html b/main/generated/exportdb/torch.escape-hatch.html
index 10820754d5c..8691963b4b5 100644
--- a/main/generated/exportdb/torch.escape-hatch.html
+++ b/main/generated/exportdb/torch.escape-hatch.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -661,7 +661,7 @@ <h2>assume_constant_result<a class="headerlink" href="#assume-constant-result" t
 <h2>constrain_as_size_example<a class="headerlink" href="#constrain-as-size-example" title="Permalink to this heading">¶</a></h2>
 <div class="admonition note">
 <p class="admonition-title">Note</p>
-<p>Tags: <a class="reference internal" href="torch.dynamic-value.html"><span class="doc">torch.dynamic-value</span></a>, <a class="reference internal" href="#"><span class="doc">torch.escape-hatch</span></a></p>
+<p>Tags: <a class="reference internal" href="#"><span class="doc">torch.escape-hatch</span></a>, <a class="reference internal" href="torch.dynamic-value.html"><span class="doc">torch.dynamic-value</span></a></p>
 <p>Support Level: SUPPORTED</p>
 </div>
 <p>Original source code:</p>
@@ -721,7 +721,7 @@ <h2>constrain_as_size_example<a class="headerlink" href="#constrain-as-size-exam
 <h2>constrain_as_value_example<a class="headerlink" href="#constrain-as-value-example" title="Permalink to this heading">¶</a></h2>
 <div class="admonition note">
 <p class="admonition-title">Note</p>
-<p>Tags: <a class="reference internal" href="torch.dynamic-value.html"><span class="doc">torch.dynamic-value</span></a>, <a class="reference internal" href="#"><span class="doc">torch.escape-hatch</span></a></p>
+<p>Tags: <a class="reference internal" href="#"><span class="doc">torch.escape-hatch</span></a>, <a class="reference internal" href="torch.dynamic-value.html"><span class="doc">torch.dynamic-value</span></a></p>
 <p>Support Level: SUPPORTED</p>
 </div>
 <p>Original source code:</p>
diff --git a/main/generated/exportdb/torch.map.html b/main/generated/exportdb/torch.map.html
index 5d382275225..bdbc188d5c9 100644
--- a/main/generated/exportdb/torch.map.html
+++ b/main/generated/exportdb/torch.map.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/exportdb/torch.mutation.html b/main/generated/exportdb/torch.mutation.html
index 436f81b4a1c..59b1fb47c74 100644
--- a/main/generated/exportdb/torch.mutation.html
+++ b/main/generated/exportdb/torch.mutation.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/exportdb/torch.operator.html b/main/generated/exportdb/torch.operator.html
index 6e5f159d34b..0402b15d840 100644
--- a/main/generated/exportdb/torch.operator.html
+++ b/main/generated/exportdb/torch.operator.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -644,7 +644,7 @@ <h2>unsupported_operator<a class="headerlink" href="#unsupported-operator" title
 </pre></div>
 </div>
 <p>Result:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">Unsupported</span><span class="p">:</span> <span class="n">torch</span><span class="o">.*</span> <span class="n">op</span> <span class="n">returned</span> <span class="n">non</span><span class="o">-</span><span class="n">Tensor</span> <span class="nb">int</span> <span class="n">call_function</span> <span class="o">&lt;</span><span class="n">function</span> <span class="n">sym_min</span> <span class="n">at</span> <span class="mh">0x7ff65abf64c0</span><span class="o">&gt;</span>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">Unsupported</span><span class="p">:</span> <span class="n">torch</span><span class="o">.*</span> <span class="n">op</span> <span class="n">returned</span> <span class="n">non</span><span class="o">-</span><span class="n">Tensor</span> <span class="nb">int</span> <span class="n">call_function</span> <span class="o">&lt;</span><span class="n">function</span> <span class="n">sym_min</span> <span class="n">at</span> <span class="mh">0x7f7a79cff3a0</span><span class="o">&gt;</span>
 </pre></div>
 </div>
 </div>
diff --git a/main/generated/torch.Event.html b/main/generated/torch.Event.html
index 0229d1c7360..c24b56bd0ec 100644
--- a/main/generated/torch.Event.html
+++ b/main/generated/torch.Event.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.Generator.html b/main/generated/torch.Generator.html
index 803482778f6..65d9891c0af 100644
--- a/main/generated/torch.Generator.html
+++ b/main/generated/torch.Generator.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.Stream.html b/main/generated/torch.Stream.html
index 3e5b54dc57e..3af94a509e2 100644
--- a/main/generated/torch.Stream.html
+++ b/main/generated/torch.Stream.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
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     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.Tensor.abs.html b/main/generated/torch.Tensor.abs.html
index f3283a81610..8d9164047d9 100644
--- a/main/generated/torch.Tensor.abs.html
+++ b/main/generated/torch.Tensor.abs.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
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     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.Tensor.abs_.html b/main/generated/torch.Tensor.abs_.html
index dd23f678dfb..dedc5a66305 100644
--- a/main/generated/torch.Tensor.abs_.html
+++ b/main/generated/torch.Tensor.abs_.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
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     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.Tensor.absolute.html b/main/generated/torch.Tensor.absolute.html
index 7b47e729bab..5ab606e4b77 100644
--- a/main/generated/torch.Tensor.absolute.html
+++ b/main/generated/torch.Tensor.absolute.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
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     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.Tensor.absolute_.html b/main/generated/torch.Tensor.absolute_.html
index e8c8ba313b3..741d235ba0a 100644
--- a/main/generated/torch.Tensor.absolute_.html
+++ b/main/generated/torch.Tensor.absolute_.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
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     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.Tensor.acos.html b/main/generated/torch.Tensor.acos.html
index ca1176eb874..6a1c1ef1192 100644
--- a/main/generated/torch.Tensor.acos.html
+++ b/main/generated/torch.Tensor.acos.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
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     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.Tensor.acos_.html b/main/generated/torch.Tensor.acos_.html
index bb1c7c9f992..d9ae5b1681f 100644
--- a/main/generated/torch.Tensor.acos_.html
+++ b/main/generated/torch.Tensor.acos_.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
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     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.Tensor.acosh.html b/main/generated/torch.Tensor.acosh.html
index 5cc5058b03c..9b866ac9f4a 100644
--- a/main/generated/torch.Tensor.acosh.html
+++ b/main/generated/torch.Tensor.acosh.html
@@ -300,7 +300,7 @@
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     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
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     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.Tensor.acosh_.html b/main/generated/torch.Tensor.acosh_.html
index ab43f83aacc..650d30445da 100644
--- a/main/generated/torch.Tensor.acosh_.html
+++ b/main/generated/torch.Tensor.acosh_.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
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     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.Tensor.add.html b/main/generated/torch.Tensor.add.html
index 404e220558d..88780b89efd 100644
--- a/main/generated/torch.Tensor.add.html
+++ b/main/generated/torch.Tensor.add.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.Tensor.add_.html b/main/generated/torch.Tensor.add_.html
index c0724ae0450..2c50f22a224 100644
--- a/main/generated/torch.Tensor.add_.html
+++ b/main/generated/torch.Tensor.add_.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.Tensor.addbmm.html b/main/generated/torch.Tensor.addbmm.html
index c7fe769948b..d690f8300c3 100644
--- a/main/generated/torch.Tensor.addbmm.html
+++ b/main/generated/torch.Tensor.addbmm.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
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     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.Tensor.addbmm_.html b/main/generated/torch.Tensor.addbmm_.html
index d4a06f48aae..ece8f1f147d 100644
--- a/main/generated/torch.Tensor.addbmm_.html
+++ b/main/generated/torch.Tensor.addbmm_.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.Tensor.addcdiv.html b/main/generated/torch.Tensor.addcdiv.html
index 6d359162e1c..11e246b5513 100644
--- a/main/generated/torch.Tensor.addcdiv.html
+++ b/main/generated/torch.Tensor.addcdiv.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.Tensor.addcdiv_.html b/main/generated/torch.Tensor.addcdiv_.html
index 79b532f538e..5760db560dc 100644
--- a/main/generated/torch.Tensor.addcdiv_.html
+++ b/main/generated/torch.Tensor.addcdiv_.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
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     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.Tensor.addcmul.html b/main/generated/torch.Tensor.addcmul.html
index 561e317a7ab..7e550ce6519 100644
--- a/main/generated/torch.Tensor.addcmul.html
+++ b/main/generated/torch.Tensor.addcmul.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
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     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.Tensor.addcmul_.html b/main/generated/torch.Tensor.addcmul_.html
index 82b93b639da..77ae09bb940 100644
--- a/main/generated/torch.Tensor.addcmul_.html
+++ b/main/generated/torch.Tensor.addcmul_.html
@@ -300,7 +300,7 @@
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+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.Tensor.addmm.html b/main/generated/torch.Tensor.addmm.html
index 7761a00345e..8e98abeadba 100644
--- a/main/generated/torch.Tensor.addmm.html
+++ b/main/generated/torch.Tensor.addmm.html
@@ -300,7 +300,7 @@
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     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
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     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.Tensor.addmm_.html b/main/generated/torch.Tensor.addmm_.html
index fea8264c49c..8584c1f9833 100644
--- a/main/generated/torch.Tensor.addmm_.html
+++ b/main/generated/torch.Tensor.addmm_.html
@@ -300,7 +300,7 @@
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     <div class="version">
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     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.Tensor.addmv.html b/main/generated/torch.Tensor.addmv.html
index ac9439e4385..a74d3dc516d 100644
--- a/main/generated/torch.Tensor.addmv.html
+++ b/main/generated/torch.Tensor.addmv.html
@@ -300,7 +300,7 @@
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     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
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     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.Tensor.addmv_.html b/main/generated/torch.Tensor.addmv_.html
index c7ba04875d8..b7b77f48506 100644
--- a/main/generated/torch.Tensor.addmv_.html
+++ b/main/generated/torch.Tensor.addmv_.html
@@ -300,7 +300,7 @@
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     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
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     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.Tensor.addr.html b/main/generated/torch.Tensor.addr.html
index ec46d820ce5..a877ff18099 100644
--- a/main/generated/torch.Tensor.addr.html
+++ b/main/generated/torch.Tensor.addr.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.Tensor.addr_.html b/main/generated/torch.Tensor.addr_.html
index 0da73c673d2..25cc2031091 100644
--- a/main/generated/torch.Tensor.addr_.html
+++ b/main/generated/torch.Tensor.addr_.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
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     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.Tensor.adjoint.html b/main/generated/torch.Tensor.adjoint.html
index 8c4b1cdce42..5ed04c9d710 100644
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+++ b/main/generated/torch.Tensor.adjoint.html
@@ -300,7 +300,7 @@
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diff --git a/main/generated/torch.Tensor.all.html b/main/generated/torch.Tensor.all.html
index 2a2952edb99..42b754cdf59 100644
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 <h1>torch.Tensor.backward<a class="headerlink" href="#torch-tensor-backward" title="Permalink to this heading">¶</a></h1>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.Tensor.backward">
-<span class="sig-prename descclassname"><span class="pre">Tensor.</span></span><span class="sig-name descname"><span class="pre">backward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">gradient</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">retain_graph</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">create_graph</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inputs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/_tensor.html#Tensor.backward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/_tensor.py#L592"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.Tensor.backward" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">Tensor.</span></span><span class="sig-name descname"><span class="pre">backward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">gradient</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">retain_graph</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">create_graph</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inputs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/_tensor.html#Tensor.backward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/_tensor.py#L592"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.Tensor.backward" title="Permalink to this definition">¶</a></dt>
 <dd><p>Computes the gradient of current tensor wrt graph leaves.</p>
 <p>The graph is differentiated using the chain rule. If the tensor is
 non-scalar (i.e. its data has more than one element) and requires
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 <h1>torch.Tensor.dim_order<a class="headerlink" href="#torch-tensor-dim-order" title="Permalink to this heading">¶</a></h1>
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-<span class="sig-prename descclassname"><span class="pre">Tensor.</span></span><span class="sig-name descname"><span class="pre">dim_order</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">ambiguity_check</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.13)"><span class="pre">tuple</span></a></span></span><a class="reference internal" href="../_modules/torch/_tensor.html#Tensor.dim_order"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/_tensor.py#L1495"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.Tensor.dim_order" title="Permalink to this definition">¶</a></dt>
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 <dd><p>Returns the uniquely determined tuple of int describing the dim order or
 physical layout of <code class="xref py py-attr docutils literal notranslate"><span class="pre">self</span></code>.</p>
 <p>The dim order represents how dimensions are laid out in memory of dense tensors,
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 <dd><p>Checks if tensor is in shared memory.</p>
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 <dl class="py method">
 <dt class="sig sig-object py" id="torch.Tensor.istft">
-<span class="sig-prename descclassname"><span class="pre">Tensor.</span></span><span class="sig-name descname"><span class="pre">istft</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_fft</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">hop_length</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">win_length</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">window</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">center</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">normalized</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">onesided</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">length</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_complex</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/_tensor.html#Tensor.istft"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/_tensor.py#L978"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.Tensor.istft" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">Tensor.</span></span><span class="sig-name descname"><span class="pre">istft</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_fft</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">hop_length</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">win_length</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">window</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">center</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">normalized</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">onesided</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">length</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_complex</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/_tensor.html#Tensor.istft"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/_tensor.py#L978"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.Tensor.istft" title="Permalink to this definition">¶</a></dt>
 <dd><p>See <a class="reference internal" href="torch.istft.html#torch.istft" title="torch.istft"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.istft()</span></code></a></p>
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 <h1>torch.Tensor.lu<a class="headerlink" href="#torch-tensor-lu" title="Permalink to this heading">¶</a></h1>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.Tensor.lu">
-<span class="sig-prename descclassname"><span class="pre">Tensor.</span></span><span class="sig-name descname"><span class="pre">lu</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">pivot</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">get_infos</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/_tensor.html#Tensor.lu"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/_tensor.py#L916"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.Tensor.lu" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">Tensor.</span></span><span class="sig-name descname"><span class="pre">lu</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">pivot</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">get_infos</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/_tensor.html#Tensor.lu"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/_tensor.py#L916"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.Tensor.lu" title="Permalink to this definition">¶</a></dt>
 <dd><p>See <a class="reference internal" href="torch.lu.html#torch.lu" title="torch.lu"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.lu()</span></code></a></p>
 </dd></dl>
 
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 <h1>torch.Tensor.module_load<a class="headerlink" href="#torch-tensor-module-load" title="Permalink to this heading">¶</a></h1>
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 <dt class="sig sig-object py" id="torch.Tensor.module_load">
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 <h1>torch.Tensor.norm<a class="headerlink" href="#torch-tensor-norm" title="Permalink to this heading">¶</a></h1>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.Tensor.norm">
-<span class="sig-prename descclassname"><span class="pre">Tensor.</span></span><span class="sig-name descname"><span class="pre">norm</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">p</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'fro'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">keepdim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/_tensor.html#Tensor.norm"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/_tensor.py#L882"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.Tensor.norm" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">Tensor.</span></span><span class="sig-name descname"><span class="pre">norm</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">p</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'fro'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">keepdim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/_tensor.html#Tensor.norm"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/_tensor.py#L882"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.Tensor.norm" title="Permalink to this definition">¶</a></dt>
 <dd><p>See <a class="reference internal" href="torch.norm.html#torch.norm" title="torch.norm"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.norm()</span></code></a></p>
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 <h1>torch.Tensor.register_hook<a class="headerlink" href="#torch-tensor-register-hook" title="Permalink to this heading">¶</a></h1>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.Tensor.register_hook">
-<span class="sig-prename descclassname"><span class="pre">Tensor.</span></span><span class="sig-name descname"><span class="pre">register_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/_tensor.html#Tensor.register_hook"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/_tensor.py#L652"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.Tensor.register_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">Tensor.</span></span><span class="sig-name descname"><span class="pre">register_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/_tensor.html#Tensor.register_hook"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/_tensor.py#L652"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.Tensor.register_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Registers a backward hook.</p>
 <p>The hook will be called every time a gradient with respect to the
 Tensor is computed. The hook should have the following signature:</p>
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 <h1>torch.Tensor.register_post_accumulate_grad_hook<a class="headerlink" href="#torch-tensor-register-post-accumulate-grad-hook" title="Permalink to this heading">¶</a></h1>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.Tensor.register_post_accumulate_grad_hook">
-<span class="sig-prename descclassname"><span class="pre">Tensor.</span></span><span class="sig-name descname"><span class="pre">register_post_accumulate_grad_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/_tensor.html#Tensor.register_post_accumulate_grad_hook"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/_tensor.py#L702"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.Tensor.register_post_accumulate_grad_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">Tensor.</span></span><span class="sig-name descname"><span class="pre">register_post_accumulate_grad_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/_tensor.html#Tensor.register_post_accumulate_grad_hook"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/_tensor.py#L702"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.Tensor.register_post_accumulate_grad_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Registers a backward hook that runs after grad accumulation.</p>
 <p>The hook will be called after all gradients for a tensor have been accumulated,
 meaning that the .grad field has been updated on that tensor. The post
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 <h1>torch.Tensor.share_memory_<a class="headerlink" href="#torch-tensor-share-memory" title="Permalink to this heading">¶</a></h1>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.Tensor.share_memory_">
-<span class="sig-prename descclassname"><span class="pre">Tensor.</span></span><span class="sig-name descname"><span class="pre">share_memory_</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/_tensor.html#Tensor.share_memory_"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/_tensor.py#L830"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.Tensor.share_memory_" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">Tensor.</span></span><span class="sig-name descname"><span class="pre">share_memory_</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/_tensor.html#Tensor.share_memory_"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/_tensor.py#L830"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.Tensor.share_memory_" title="Permalink to this definition">¶</a></dt>
 <dd><p>Moves the underlying storage to shared memory.</p>
 <p>This is a no-op if the underlying storage is already in shared memory
 and for CUDA tensors. Tensors in shared memory cannot be resized.</p>
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diff --git a/main/generated/torch.Tensor.sparse_resize_and_clear_.html b/main/generated/torch.Tensor.sparse_resize_and_clear_.html
index b4e957d6e4d..74bf5823a48 100644
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diff --git a/main/generated/torch.Tensor.split.html b/main/generated/torch.Tensor.split.html
index d448071d7e3..113558c2e6f 100644
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@@ -612,7 +612,7 @@
 <h1>torch.Tensor.split<a class="headerlink" href="#torch-tensor-split" title="Permalink to this heading">¶</a></h1>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.Tensor.split">
-<span class="sig-prename descclassname"><span class="pre">Tensor.</span></span><span class="sig-name descname"><span class="pre">split</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">split_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/_tensor.html#Tensor.split"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/_tensor.py#L1035"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.Tensor.split" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">Tensor.</span></span><span class="sig-name descname"><span class="pre">split</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">split_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/_tensor.html#Tensor.split"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/_tensor.py#L1035"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.Tensor.split" title="Permalink to this definition">¶</a></dt>
 <dd><p>See <a class="reference internal" href="torch.split.html#torch.split" title="torch.split"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.split()</span></code></a></p>
 </dd></dl>
 
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diff --git a/main/generated/torch.Tensor.sqrt_.html b/main/generated/torch.Tensor.sqrt_.html
index c634e0dbad7..b5498cf80ca 100644
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diff --git a/main/generated/torch.Tensor.square.html b/main/generated/torch.Tensor.square.html
index b69fb0f4563..58f704aadb8 100644
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diff --git a/main/generated/torch.Tensor.squeeze_.html b/main/generated/torch.Tensor.squeeze_.html
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diff --git a/main/generated/torch.Tensor.stft.html b/main/generated/torch.Tensor.stft.html
index 13dca69f84e..ff63f3f8e46 100644
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@@ -612,7 +612,7 @@
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 <dl class="py method">
 <dt class="sig sig-object py" id="torch.Tensor.stft">
-<span class="sig-prename descclassname"><span class="pre">Tensor.</span></span><span class="sig-name descname"><span class="pre">stft</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_fft</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">hop_length</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">win_length</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">window</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">center</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pad_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'reflect'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">normalized</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">onesided</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_complex</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/_tensor.html#Tensor.stft"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/_tensor.py#L932"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.Tensor.stft" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">Tensor.</span></span><span class="sig-name descname"><span class="pre">stft</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n_fft</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">hop_length</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">win_length</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">window</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">center</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pad_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'reflect'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">normalized</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">onesided</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_complex</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/_tensor.html#Tensor.stft"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/_tensor.py#L932"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.Tensor.stft" title="Permalink to this definition">¶</a></dt>
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diff --git a/main/generated/torch.Tensor.storage.html b/main/generated/torch.Tensor.storage.html
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--- a/main/generated/torch.Tensor.storage.html
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@@ -612,7 +612,7 @@
 <h1>torch.Tensor.storage<a class="headerlink" href="#torch-tensor-storage" title="Permalink to this heading">¶</a></h1>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.Tensor.storage">
-<span class="sig-prename descclassname"><span class="pre">Tensor.</span></span><span class="sig-name descname"><span class="pre">storage</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="../storage.html#torch.TypedStorage" title="torch.TypedStorage"><span class="pre">torch.TypedStorage</span></a></span></span><a class="reference internal" href="../_modules/torch/_tensor.html#Tensor.storage"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/_tensor.py#L288"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.Tensor.storage" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">Tensor.</span></span><span class="sig-name descname"><span class="pre">storage</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="../storage.html#torch.TypedStorage" title="torch.TypedStorage"><span class="pre">torch.TypedStorage</span></a></span></span><a class="reference internal" href="../_modules/torch/_tensor.html#Tensor.storage"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/_tensor.py#L288"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.Tensor.storage" title="Permalink to this definition">¶</a></dt>
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diff --git a/main/generated/torch.Tensor.storage_type.html b/main/generated/torch.Tensor.storage_type.html
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@@ -612,7 +612,7 @@
 <h1>torch.Tensor.storage_type<a class="headerlink" href="#torch-tensor-storage-type" title="Permalink to this heading">¶</a></h1>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.Tensor.storage_type">
-<span class="sig-prename descclassname"><span class="pre">Tensor.</span></span><span class="sig-name descname"><span class="pre">storage_type</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference external" href="https://docs.python.org/3/library/functions.html#type" title="(in Python v3.13)"><span class="pre">type</span></a></span></span><a class="reference internal" href="../_modules/torch/_tensor.html#Tensor.storage_type"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/_tensor.py#L1301"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.Tensor.storage_type" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">Tensor.</span></span><span class="sig-name descname"><span class="pre">storage_type</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference external" href="https://docs.python.org/3/library/functions.html#type" title="(in Python v3.13)"><span class="pre">type</span></a></span></span><a class="reference internal" href="../_modules/torch/_tensor.html#Tensor.storage_type"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/_tensor.py#L1301"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.Tensor.storage_type" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns the type of the underlying storage.</p>
 </dd></dl>
 
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 <h1>torch.Tensor.to_sparse_coo<a class="headerlink" href="#torch-tensor-to-sparse-coo" title="Permalink to this heading">¶</a></h1>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.Tensor.to_sparse_coo">
-<span class="sig-prename descclassname"><span class="pre">Tensor.</span></span><span class="sig-name descname"><span class="pre">to_sparse_coo</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/_tensor.html#Tensor.to_sparse_coo"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/_tensor.py#L1482"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.Tensor.to_sparse_coo" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">Tensor.</span></span><span class="sig-name descname"><span class="pre">to_sparse_coo</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/_tensor.html#Tensor.to_sparse_coo"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/_tensor.py#L1482"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.Tensor.to_sparse_coo" title="Permalink to this definition">¶</a></dt>
 <dd><p>Convert a tensor to <a class="reference internal" href="../sparse.html#sparse-coo-docs"><span class="std std-ref">coordinate format</span></a>.</p>
 <p>Examples:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">dense</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">5</span><span class="p">,</span> <span class="mi">5</span><span class="p">)</span>
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 <h1>torch.Tensor.unflatten<a class="headerlink" href="#torch-tensor-unflatten" title="Permalink to this heading">¶</a></h1>
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-<span class="sig-prename descclassname"><span class="pre">Tensor.</span></span><span class="sig-name descname"><span class="pre">unflatten</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dim</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sizes</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><span class="pre">Tensor</span></a></span></span><a class="reference internal" href="../_modules/torch/_tensor.html#Tensor.unflatten"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/_tensor.py#L1403"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.Tensor.unflatten" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">Tensor.</span></span><span class="sig-name descname"><span class="pre">unflatten</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dim</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sizes</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><span class="pre">Tensor</span></a></span></span><a class="reference internal" href="../_modules/torch/_tensor.html#Tensor.unflatten"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/_tensor.py#L1403"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.Tensor.unflatten" title="Permalink to this definition">¶</a></dt>
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 <dl class="py method">
 <dt class="sig sig-object py" id="torch.Tensor.unique">
-<span class="sig-prename descclassname"><span class="pre">Tensor.</span></span><span class="sig-name descname"><span class="pre">unique</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sorted</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_inverse</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_counts</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/_tensor.html#Tensor.unique"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/_tensor.py#L1052"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.Tensor.unique" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">Tensor.</span></span><span class="sig-name descname"><span class="pre">unique</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sorted</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_inverse</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_counts</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/_tensor.html#Tensor.unique"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/_tensor.py#L1052"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.Tensor.unique" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns the unique elements of the input tensor.</p>
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 <h1>torch.Tensor.unique_consecutive<a class="headerlink" href="#torch-tensor-unique-consecutive" title="Permalink to this heading">¶</a></h1>
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 <dt class="sig sig-object py" id="torch.Tensor.unique_consecutive">
-<span class="sig-prename descclassname"><span class="pre">Tensor.</span></span><span class="sig-name descname"><span class="pre">unique_consecutive</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">return_inverse</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_counts</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/_tensor.html#Tensor.unique_consecutive"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/_tensor.py#L1075"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.Tensor.unique_consecutive" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">Tensor.</span></span><span class="sig-name descname"><span class="pre">unique_consecutive</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">return_inverse</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_counts</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/_tensor.html#Tensor.unique_consecutive"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/_tensor.py#L1075"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.Tensor.unique_consecutive" title="Permalink to this definition">¶</a></dt>
 <dd><p>Eliminates all but the first element from every consecutive group of equivalent elements.</p>
 <p>See <a class="reference internal" href="torch.unique_consecutive.html#torch.unique_consecutive" title="torch.unique_consecutive"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.unique_consecutive()</span></code></a></p>
 </dd></dl>
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diff --git a/main/generated/torch.Tensor.view.html b/main/generated/torch.Tensor.view.html
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diff --git a/main/generated/torch._assert.html b/main/generated/torch._assert.html
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 <h1>torch._assert<a class="headerlink" href="#torch-assert" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch._assert">
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+<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">_assert</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">condition</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">message</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch.html#_assert"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/__init__.py#L2134"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch._assert" title="Permalink to this definition">¶</a></dt>
 <dd><p>A wrapper around Python’s assert which is symbolically traceable.</p>
 </dd></dl>
 
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+++ b/main/generated/torch._foreach_sigmoid.html
@@ -300,7 +300,7 @@
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     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch._foreach_sigmoid_.html b/main/generated/torch._foreach_sigmoid_.html
index ed5dc01985e..e59da522c17 100644
--- a/main/generated/torch._foreach_sigmoid_.html
+++ b/main/generated/torch._foreach_sigmoid_.html
@@ -300,7 +300,7 @@
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     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
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     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch._foreach_sin.html b/main/generated/torch._foreach_sin.html
index d1459e52c72..50300b10f10 100644
--- a/main/generated/torch._foreach_sin.html
+++ b/main/generated/torch._foreach_sin.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch._foreach_sin_.html b/main/generated/torch._foreach_sin_.html
index 74ccbbfb4c0..4d2c2c99922 100644
--- a/main/generated/torch._foreach_sin_.html
+++ b/main/generated/torch._foreach_sin_.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch._foreach_sinh.html b/main/generated/torch._foreach_sinh.html
index 3a084263c57..b8fe164939e 100644
--- a/main/generated/torch._foreach_sinh.html
+++ b/main/generated/torch._foreach_sinh.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch._foreach_sinh_.html b/main/generated/torch._foreach_sinh_.html
index f39ada24e22..104a7163214 100644
--- a/main/generated/torch._foreach_sinh_.html
+++ b/main/generated/torch._foreach_sinh_.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch._foreach_sqrt.html b/main/generated/torch._foreach_sqrt.html
index d1ddd486831..1c78fe0f179 100644
--- a/main/generated/torch._foreach_sqrt.html
+++ b/main/generated/torch._foreach_sqrt.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch._foreach_sqrt_.html b/main/generated/torch._foreach_sqrt_.html
index 794d80d9b1f..2f51b78a132 100644
--- a/main/generated/torch._foreach_sqrt_.html
+++ b/main/generated/torch._foreach_sqrt_.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch._foreach_tan.html b/main/generated/torch._foreach_tan.html
index fb608983d98..526cba5d292 100644
--- a/main/generated/torch._foreach_tan.html
+++ b/main/generated/torch._foreach_tan.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch._foreach_tan_.html b/main/generated/torch._foreach_tan_.html
index cd6ddeff6d1..d226818a0fe 100644
--- a/main/generated/torch._foreach_tan_.html
+++ b/main/generated/torch._foreach_tan_.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch._foreach_trunc.html b/main/generated/torch._foreach_trunc.html
index 182a8979c7a..bc177803227 100644
--- a/main/generated/torch._foreach_trunc.html
+++ b/main/generated/torch._foreach_trunc.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch._foreach_trunc_.html b/main/generated/torch._foreach_trunc_.html
index 6f759d3cab2..ca5f307c00f 100644
--- a/main/generated/torch._foreach_trunc_.html
+++ b/main/generated/torch._foreach_trunc_.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch._foreach_zero_.html b/main/generated/torch._foreach_zero_.html
index 6cc09cc157a..9f69f729ce2 100644
--- a/main/generated/torch._foreach_zero_.html
+++ b/main/generated/torch._foreach_zero_.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch._logging.set_logs.html b/main/generated/torch._logging.set_logs.html
index f9a2011e361..d63cb5b7d5c 100644
--- a/main/generated/torch._logging.set_logs.html
+++ b/main/generated/torch._logging.set_logs.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -616,7 +616,7 @@
 <h1>torch._logging.set_logs<a class="headerlink" href="#torch-logging-set-logs" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch._logging.set_logs">
-<span class="sig-prename descclassname"><span class="pre">torch._logging.</span></span><span class="sig-name descname"><span class="pre">set_logs</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">all</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dynamo</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">aot</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">autograd</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dynamic</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inductor</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">distributed</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">c10d</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ddp</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fsdp</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtensor</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">onnx</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bytecode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">aot_graphs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">aot_joint_graph</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ddp_graphs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">graph</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">graph_code</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">graph_breaks</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">graph_sizes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">guards</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">recompiles</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">recompiles_verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">trace_source</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">trace_call</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">trace_bytecode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_code</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_code</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">schedule</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">perf_hints</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pre_grad_graphs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">post_grad_graphs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">onnx_diagnostics</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fusion</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">overlap</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">export</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">modules</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cudagraphs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sym_node</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">compiled_autograd</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">compiled_autograd_verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cudagraph_static_inputs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">benchmarking</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">graph_region_expansion</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/_logging/_internal.html#set_logs"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/_logging/_internal.py#L200"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch._logging.set_logs" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch._logging.</span></span><span class="sig-name descname"><span class="pre">set_logs</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">all</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dynamo</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">aot</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">autograd</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dynamic</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inductor</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">distributed</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">c10d</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ddp</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fsdp</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtensor</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">onnx</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bytecode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">aot_graphs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">aot_joint_graph</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ddp_graphs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">graph</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">graph_code</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">graph_breaks</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">graph_sizes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">guards</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">recompiles</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">recompiles_verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">trace_source</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">trace_call</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">trace_bytecode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_code</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_code</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">schedule</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">perf_hints</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pre_grad_graphs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">post_grad_graphs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">onnx_diagnostics</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fusion</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">overlap</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">export</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">modules</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cudagraphs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sym_node</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">compiled_autograd</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">compiled_autograd_verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cudagraph_static_inputs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">benchmarking</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">graph_region_expansion</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/_logging/_internal.html#set_logs"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/_logging/_internal.py#L200"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch._logging.set_logs" title="Permalink to this definition">¶</a></dt>
 <dd><p>Sets the log level for individual components and toggles individual log
 artifact types.</p>
 <div class="admonition warning">
diff --git a/main/generated/torch.abs.html b/main/generated/torch.abs.html
index 6595d8057fb..c9b276c2299 100644
--- a/main/generated/torch.abs.html
+++ b/main/generated/torch.abs.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.absolute.html b/main/generated/torch.absolute.html
index 1de65b82a75..8d6cacbbb37 100644
--- a/main/generated/torch.absolute.html
+++ b/main/generated/torch.absolute.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.accelerator.current_accelerator.html b/main/generated/torch.accelerator.current_accelerator.html
index 56cea5467d3..51bc7391676 100644
--- a/main/generated/torch.accelerator.current_accelerator.html
+++ b/main/generated/torch.accelerator.current_accelerator.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.accelerator.current_accelerator<a class="headerlink" href="#torch-accelerator-current-accelerator" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.accelerator.current_accelerator">
-<span class="sig-prename descclassname"><span class="pre">torch.accelerator.</span></span><span class="sig-name descname"><span class="pre">current_accelerator</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/accelerator.html#current_accelerator"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/accelerator/__init__.py#L49"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.accelerator.current_accelerator" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.accelerator.</span></span><span class="sig-name descname"><span class="pre">current_accelerator</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/accelerator.html#current_accelerator"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/accelerator/__init__.py#L49"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.accelerator.current_accelerator" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the device of the current <a class="reference internal" href="../torch.html#accelerators"><span class="std std-ref">accelerator</span></a>.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Returns</dt>
diff --git a/main/generated/torch.accelerator.current_device_idx.html b/main/generated/torch.accelerator.current_device_idx.html
index 4bf6e59b3ec..3f0423ee97f 100644
--- a/main/generated/torch.accelerator.current_device_idx.html
+++ b/main/generated/torch.accelerator.current_device_idx.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.accelerator.current_device_idx<a class="headerlink" href="#torch-accelerator-current-device-idx" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.accelerator.current_device_idx">
-<span class="sig-prename descclassname"><span class="pre">torch.accelerator.</span></span><span class="sig-name descname"><span class="pre">current_device_idx</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/accelerator/__init__.py#L77"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.accelerator.current_device_idx" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.accelerator.</span></span><span class="sig-name descname"><span class="pre">current_device_idx</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/accelerator/__init__.py#L77"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.accelerator.current_device_idx" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the index of a currently selected device for the current <a class="reference internal" href="../torch.html#accelerators"><span class="std std-ref">accelerator</span></a>.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Returns</dt>
diff --git a/main/generated/torch.accelerator.current_device_index.html b/main/generated/torch.accelerator.current_device_index.html
index 0d3bdb6a682..ff62ac517b2 100644
--- a/main/generated/torch.accelerator.current_device_index.html
+++ b/main/generated/torch.accelerator.current_device_index.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.accelerator.current_device_index<a class="headerlink" href="#torch-accelerator-current-device-index" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.accelerator.current_device_index">
-<span class="sig-prename descclassname"><span class="pre">torch.accelerator.</span></span><span class="sig-name descname"><span class="pre">current_device_index</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/accelerator.html#current_device_index"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/accelerator/__init__.py#L77"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.accelerator.current_device_index" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.accelerator.</span></span><span class="sig-name descname"><span class="pre">current_device_index</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/accelerator.html#current_device_index"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/accelerator/__init__.py#L77"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.accelerator.current_device_index" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the index of a currently selected device for the current <a class="reference internal" href="../torch.html#accelerators"><span class="std std-ref">accelerator</span></a>.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Returns</dt>
diff --git a/main/generated/torch.accelerator.current_stream.html b/main/generated/torch.accelerator.current_stream.html
index 513dfdc7329..dbc34531b85 100644
--- a/main/generated/torch.accelerator.current_stream.html
+++ b/main/generated/torch.accelerator.current_stream.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.accelerator.current_stream<a class="headerlink" href="#torch-accelerator-current-stream" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.accelerator.current_stream">
-<span class="sig-prename descclassname"><span class="pre">torch.accelerator.</span></span><span class="sig-name descname"><span class="pre">current_stream</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">/</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/accelerator.html#current_stream"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/accelerator/__init__.py#L111"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.accelerator.current_stream" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.accelerator.</span></span><span class="sig-name descname"><span class="pre">current_stream</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">/</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/accelerator.html#current_stream"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/accelerator/__init__.py#L111"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.accelerator.current_stream" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the currently selected stream for a given device.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.accelerator.device_count.html b/main/generated/torch.accelerator.device_count.html
index 4fb3c0ca6a6..ba13bed7a45 100644
--- a/main/generated/torch.accelerator.device_count.html
+++ b/main/generated/torch.accelerator.device_count.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.accelerator.device_count<a class="headerlink" href="#torch-accelerator-device-count" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.accelerator.device_count">
-<span class="sig-prename descclassname"><span class="pre">torch.accelerator.</span></span><span class="sig-name descname"><span class="pre">device_count</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/accelerator.html#device_count"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/accelerator/__init__.py#L26"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.accelerator.device_count" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.accelerator.</span></span><span class="sig-name descname"><span class="pre">device_count</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/accelerator.html#device_count"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/accelerator/__init__.py#L26"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.accelerator.device_count" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the number of current <a class="reference internal" href="../torch.html#accelerators"><span class="std std-ref">accelerator</span></a> available.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Returns</dt>
diff --git a/main/generated/torch.accelerator.is_available.html b/main/generated/torch.accelerator.is_available.html
index 49e65056bba..5570e661426 100644
--- a/main/generated/torch.accelerator.is_available.html
+++ b/main/generated/torch.accelerator.is_available.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.accelerator.is_available<a class="headerlink" href="#torch-accelerator-is-available" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.accelerator.is_available">
-<span class="sig-prename descclassname"><span class="pre">torch.accelerator.</span></span><span class="sig-name descname"><span class="pre">is_available</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/accelerator.html#is_available"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/accelerator/__init__.py#L36"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.accelerator.is_available" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.accelerator.</span></span><span class="sig-name descname"><span class="pre">is_available</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/accelerator.html#is_available"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/accelerator/__init__.py#L36"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.accelerator.is_available" title="Permalink to this definition">¶</a></dt>
 <dd><p>Check if there is an available <a class="reference internal" href="../torch.html#accelerators"><span class="std std-ref">accelerator</span></a>.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Returns</dt>
diff --git a/main/generated/torch.accelerator.set_device_idx.html b/main/generated/torch.accelerator.set_device_idx.html
index c97addf45d7..7b9b18307a9 100644
--- a/main/generated/torch.accelerator.set_device_idx.html
+++ b/main/generated/torch.accelerator.set_device_idx.html
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@@ -612,7 +612,7 @@
 <h1>torch.accelerator.set_device_idx<a class="headerlink" href="#torch-accelerator-set-device-idx" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.accelerator.set_device_idx">
-<span class="sig-prename descclassname"><span class="pre">torch.accelerator.</span></span><span class="sig-name descname"><span class="pre">set_device_idx</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">/</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/accelerator/__init__.py#L92"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.accelerator.set_device_idx" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.accelerator.</span></span><span class="sig-name descname"><span class="pre">set_device_idx</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">/</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/accelerator/__init__.py#L92"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.accelerator.set_device_idx" title="Permalink to this definition">¶</a></dt>
 <dd><p>Set the current device index to a given device.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.accelerator.set_device_index.html b/main/generated/torch.accelerator.set_device_index.html
index 9ad6e47f183..9df3efd2385 100644
--- a/main/generated/torch.accelerator.set_device_index.html
+++ b/main/generated/torch.accelerator.set_device_index.html
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@@ -612,7 +612,7 @@
 <h1>torch.accelerator.set_device_index<a class="headerlink" href="#torch-accelerator-set-device-index" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.accelerator.set_device_index">
-<span class="sig-prename descclassname"><span class="pre">torch.accelerator.</span></span><span class="sig-name descname"><span class="pre">set_device_index</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">/</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/accelerator.html#set_device_index"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/accelerator/__init__.py#L92"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.accelerator.set_device_index" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.accelerator.</span></span><span class="sig-name descname"><span class="pre">set_device_index</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">/</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/accelerator.html#set_device_index"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/accelerator/__init__.py#L92"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.accelerator.set_device_index" title="Permalink to this definition">¶</a></dt>
 <dd><p>Set the current device index to a given device.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.accelerator.set_stream.html b/main/generated/torch.accelerator.set_stream.html
index 72286b5c511..8d27c3e0527 100644
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+++ b/main/generated/torch.accelerator.set_stream.html
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@@ -612,7 +612,7 @@
 <h1>torch.accelerator.set_stream<a class="headerlink" href="#torch-accelerator-set-stream" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.accelerator.set_stream">
-<span class="sig-prename descclassname"><span class="pre">torch.accelerator.</span></span><span class="sig-name descname"><span class="pre">set_stream</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">stream</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/accelerator.html#set_stream"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/accelerator/__init__.py#L126"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.accelerator.set_stream" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.accelerator.</span></span><span class="sig-name descname"><span class="pre">set_stream</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">stream</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/accelerator.html#set_stream"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/accelerator/__init__.py#L126"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.accelerator.set_stream" title="Permalink to this definition">¶</a></dt>
 <dd><p>Set the current stream to a given stream.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.accelerator.synchronize.html b/main/generated/torch.accelerator.synchronize.html
index 38e52b505ed..73a8c23a67f 100644
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+++ b/main/generated/torch.accelerator.synchronize.html
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@@ -612,7 +612,7 @@
 <h1>torch.accelerator.synchronize<a class="headerlink" href="#torch-accelerator-synchronize" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.accelerator.synchronize">
-<span class="sig-prename descclassname"><span class="pre">torch.accelerator.</span></span><span class="sig-name descname"><span class="pre">synchronize</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">/</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/accelerator.html#synchronize"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/accelerator/__init__.py#L137"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.accelerator.synchronize" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.accelerator.</span></span><span class="sig-name descname"><span class="pre">synchronize</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">/</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/accelerator.html#synchronize"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/accelerator/__init__.py#L137"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.accelerator.synchronize" title="Permalink to this definition">¶</a></dt>
 <dd><p>Wait for all kernels in all streams on the given device to complete.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.acos.html b/main/generated/torch.acos.html
index 889580f7f1e..266933fa2c1 100644
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diff --git a/main/generated/torch.acosh.html b/main/generated/torch.acosh.html
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index 15c83c3577d..5373737da17 100644
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diff --git a/main/generated/torch.angle.html b/main/generated/torch.angle.html
index d13bb8823b4..4372fe13c87 100644
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-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
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     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.any.html b/main/generated/torch.any.html
index 9136948f296..b3919cbbdea 100644
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diff --git a/main/generated/torch.ao.nn.intrinsic.BNReLU2d.html b/main/generated/torch.ao.nn.intrinsic.BNReLU2d.html
index 6f9cc6011a6..01021f2f074 100644
--- a/main/generated/torch.ao.nn.intrinsic.BNReLU2d.html
+++ b/main/generated/torch.ao.nn.intrinsic.BNReLU2d.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>BNReLU2d<a class="headerlink" href="#bnrelu2d" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.nn.intrinsic.BNReLU2d">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.intrinsic.</span></span><span class="sig-name descname"><span class="pre">BNReLU2d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">batch_norm</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">relu</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/intrinsic/modules/fused.html#BNReLU2d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/nn/intrinsic/modules/fused.py#L165"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.intrinsic.BNReLU2d" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.intrinsic.</span></span><span class="sig-name descname"><span class="pre">BNReLU2d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">batch_norm</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">relu</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/intrinsic/modules/fused.html#BNReLU2d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/nn/intrinsic/modules/fused.py#L165"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.intrinsic.BNReLU2d" title="Permalink to this definition">¶</a></dt>
 <dd><p>This is a sequential container which calls the BatchNorm 2d and ReLU modules.
 During quantization this will be replaced with the corresponding fused module.</p>
 </dd></dl>
diff --git a/main/generated/torch.ao.nn.intrinsic.BNReLU3d.html b/main/generated/torch.ao.nn.intrinsic.BNReLU3d.html
index 642823fcbb3..655c800b720 100644
--- a/main/generated/torch.ao.nn.intrinsic.BNReLU3d.html
+++ b/main/generated/torch.ao.nn.intrinsic.BNReLU3d.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
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     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>BNReLU3d<a class="headerlink" href="#bnrelu3d" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.nn.intrinsic.BNReLU3d">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.intrinsic.</span></span><span class="sig-name descname"><span class="pre">BNReLU3d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">batch_norm</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">relu</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/intrinsic/modules/fused.html#BNReLU3d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/nn/intrinsic/modules/fused.py#L177"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.intrinsic.BNReLU3d" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.intrinsic.</span></span><span class="sig-name descname"><span class="pre">BNReLU3d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">batch_norm</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">relu</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/intrinsic/modules/fused.html#BNReLU3d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/nn/intrinsic/modules/fused.py#L177"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.intrinsic.BNReLU3d" title="Permalink to this definition">¶</a></dt>
 <dd><p>This is a sequential container which calls the BatchNorm 3d and ReLU modules.
 During quantization this will be replaced with the corresponding fused module.</p>
 </dd></dl>
diff --git a/main/generated/torch.ao.nn.intrinsic.ConvBn1d.html b/main/generated/torch.ao.nn.intrinsic.ConvBn1d.html
index 1663a320d50..591c68b6a69 100644
--- a/main/generated/torch.ao.nn.intrinsic.ConvBn1d.html
+++ b/main/generated/torch.ao.nn.intrinsic.ConvBn1d.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>ConvBn1d<a class="headerlink" href="#convbn1d" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.nn.intrinsic.ConvBn1d">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.intrinsic.</span></span><span class="sig-name descname"><span class="pre">ConvBn1d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">conv</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bn</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/intrinsic/modules/fused.html#ConvBn1d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/nn/intrinsic/modules/fused.py#L90"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.intrinsic.ConvBn1d" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.intrinsic.</span></span><span class="sig-name descname"><span class="pre">ConvBn1d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">conv</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bn</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/intrinsic/modules/fused.html#ConvBn1d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/nn/intrinsic/modules/fused.py#L90"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.intrinsic.ConvBn1d" title="Permalink to this definition">¶</a></dt>
 <dd><p>This is a sequential container which calls the Conv 1d and Batch Norm 1d modules.
 During quantization this will be replaced with the corresponding fused module.</p>
 </dd></dl>
diff --git a/main/generated/torch.ao.nn.intrinsic.ConvBn2d.html b/main/generated/torch.ao.nn.intrinsic.ConvBn2d.html
index e792d5eb120..4efce38d5ff 100644
--- a/main/generated/torch.ao.nn.intrinsic.ConvBn2d.html
+++ b/main/generated/torch.ao.nn.intrinsic.ConvBn2d.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>ConvBn2d<a class="headerlink" href="#convbn2d" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.nn.intrinsic.ConvBn2d">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.intrinsic.</span></span><span class="sig-name descname"><span class="pre">ConvBn2d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">conv</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bn</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/intrinsic/modules/fused.html#ConvBn2d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/nn/intrinsic/modules/fused.py#L102"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.intrinsic.ConvBn2d" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.intrinsic.</span></span><span class="sig-name descname"><span class="pre">ConvBn2d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">conv</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bn</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/intrinsic/modules/fused.html#ConvBn2d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/nn/intrinsic/modules/fused.py#L102"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.intrinsic.ConvBn2d" title="Permalink to this definition">¶</a></dt>
 <dd><p>This is a sequential container which calls the Conv 2d and Batch Norm 2d modules.
 During quantization this will be replaced with the corresponding fused module.</p>
 </dd></dl>
diff --git a/main/generated/torch.ao.nn.intrinsic.ConvBn3d.html b/main/generated/torch.ao.nn.intrinsic.ConvBn3d.html
index 8694d514f02..1a99a4ce50f 100644
--- a/main/generated/torch.ao.nn.intrinsic.ConvBn3d.html
+++ b/main/generated/torch.ao.nn.intrinsic.ConvBn3d.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>ConvBn3d<a class="headerlink" href="#convbn3d" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.nn.intrinsic.ConvBn3d">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.intrinsic.</span></span><span class="sig-name descname"><span class="pre">ConvBn3d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">conv</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bn</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/intrinsic/modules/fused.html#ConvBn3d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/nn/intrinsic/modules/fused.py#L140"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.intrinsic.ConvBn3d" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.intrinsic.</span></span><span class="sig-name descname"><span class="pre">ConvBn3d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">conv</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bn</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/intrinsic/modules/fused.html#ConvBn3d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/nn/intrinsic/modules/fused.py#L140"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.intrinsic.ConvBn3d" title="Permalink to this definition">¶</a></dt>
 <dd><p>This is a sequential container which calls the Conv 3d and Batch Norm 3d modules.
 During quantization this will be replaced with the corresponding fused module.</p>
 </dd></dl>
diff --git a/main/generated/torch.ao.nn.intrinsic.ConvBnReLU1d.html b/main/generated/torch.ao.nn.intrinsic.ConvBnReLU1d.html
index 5ba999316d1..eff283a5a4d 100644
--- a/main/generated/torch.ao.nn.intrinsic.ConvBnReLU1d.html
+++ b/main/generated/torch.ao.nn.intrinsic.ConvBnReLU1d.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>ConvBnReLU1d<a class="headerlink" href="#convbnrelu1d" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.nn.intrinsic.ConvBnReLU1d">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.intrinsic.</span></span><span class="sig-name descname"><span class="pre">ConvBnReLU1d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">conv</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bn</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">relu</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/intrinsic/modules/fused.html#ConvBnReLU1d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/nn/intrinsic/modules/fused.py#L114"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.intrinsic.ConvBnReLU1d" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.intrinsic.</span></span><span class="sig-name descname"><span class="pre">ConvBnReLU1d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">conv</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bn</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">relu</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/intrinsic/modules/fused.html#ConvBnReLU1d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/nn/intrinsic/modules/fused.py#L114"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.intrinsic.ConvBnReLU1d" title="Permalink to this definition">¶</a></dt>
 <dd><p>This is a sequential container which calls the Conv 1d, Batch Norm 1d, and ReLU modules.
 During quantization this will be replaced with the corresponding fused module.</p>
 </dd></dl>
diff --git a/main/generated/torch.ao.nn.intrinsic.ConvBnReLU2d.html b/main/generated/torch.ao.nn.intrinsic.ConvBnReLU2d.html
index b79b25985d5..ece904e2c4f 100644
--- a/main/generated/torch.ao.nn.intrinsic.ConvBnReLU2d.html
+++ b/main/generated/torch.ao.nn.intrinsic.ConvBnReLU2d.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>ConvBnReLU2d<a class="headerlink" href="#convbnrelu2d" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.nn.intrinsic.ConvBnReLU2d">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.intrinsic.</span></span><span class="sig-name descname"><span class="pre">ConvBnReLU2d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">conv</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bn</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">relu</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/intrinsic/modules/fused.html#ConvBnReLU2d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/nn/intrinsic/modules/fused.py#L127"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.intrinsic.ConvBnReLU2d" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.intrinsic.</span></span><span class="sig-name descname"><span class="pre">ConvBnReLU2d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">conv</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bn</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">relu</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/intrinsic/modules/fused.html#ConvBnReLU2d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/nn/intrinsic/modules/fused.py#L127"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.intrinsic.ConvBnReLU2d" title="Permalink to this definition">¶</a></dt>
 <dd><p>This is a sequential container which calls the Conv 2d, Batch Norm 2d, and ReLU modules.
 During quantization this will be replaced with the corresponding fused module.</p>
 </dd></dl>
diff --git a/main/generated/torch.ao.nn.intrinsic.ConvBnReLU3d.html b/main/generated/torch.ao.nn.intrinsic.ConvBnReLU3d.html
index acaee729e59..cb117a6468e 100644
--- a/main/generated/torch.ao.nn.intrinsic.ConvBnReLU3d.html
+++ b/main/generated/torch.ao.nn.intrinsic.ConvBnReLU3d.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
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@@ -614,7 +614,7 @@
 <h1>ConvBnReLU3d<a class="headerlink" href="#convbnrelu3d" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.nn.intrinsic.ConvBnReLU3d">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.intrinsic.</span></span><span class="sig-name descname"><span class="pre">ConvBnReLU3d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">conv</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bn</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">relu</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/intrinsic/modules/fused.html#ConvBnReLU3d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/nn/intrinsic/modules/fused.py#L152"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.intrinsic.ConvBnReLU3d" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.intrinsic.</span></span><span class="sig-name descname"><span class="pre">ConvBnReLU3d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">conv</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bn</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">relu</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/intrinsic/modules/fused.html#ConvBnReLU3d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/nn/intrinsic/modules/fused.py#L152"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.intrinsic.ConvBnReLU3d" title="Permalink to this definition">¶</a></dt>
 <dd><p>This is a sequential container which calls the Conv 3d, Batch Norm 3d, and ReLU modules.
 During quantization this will be replaced with the corresponding fused module.</p>
 </dd></dl>
diff --git a/main/generated/torch.ao.nn.intrinsic.ConvReLU1d.html b/main/generated/torch.ao.nn.intrinsic.ConvReLU1d.html
index 8b26d3da8c6..07db50dc37e 100644
--- a/main/generated/torch.ao.nn.intrinsic.ConvReLU1d.html
+++ b/main/generated/torch.ao.nn.intrinsic.ConvReLU1d.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>ConvReLU1d<a class="headerlink" href="#convrelu1d" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.nn.intrinsic.ConvReLU1d">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.intrinsic.</span></span><span class="sig-name descname"><span class="pre">ConvReLU1d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">conv</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">relu</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/intrinsic/modules/fused.html#ConvReLU1d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/nn/intrinsic/modules/fused.py#L42"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.intrinsic.ConvReLU1d" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.intrinsic.</span></span><span class="sig-name descname"><span class="pre">ConvReLU1d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">conv</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">relu</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/intrinsic/modules/fused.html#ConvReLU1d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/nn/intrinsic/modules/fused.py#L42"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.intrinsic.ConvReLU1d" title="Permalink to this definition">¶</a></dt>
 <dd><p>This is a sequential container which calls the Conv1d and ReLU modules.
 During quantization this will be replaced with the corresponding fused module.</p>
 </dd></dl>
diff --git a/main/generated/torch.ao.nn.intrinsic.ConvReLU2d.html b/main/generated/torch.ao.nn.intrinsic.ConvReLU2d.html
index 6914628ef1d..c1aab49e9d3 100644
--- a/main/generated/torch.ao.nn.intrinsic.ConvReLU2d.html
+++ b/main/generated/torch.ao.nn.intrinsic.ConvReLU2d.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>ConvReLU2d<a class="headerlink" href="#convrelu2d" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.nn.intrinsic.ConvReLU2d">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.intrinsic.</span></span><span class="sig-name descname"><span class="pre">ConvReLU2d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">conv</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">relu</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/intrinsic/modules/fused.html#ConvReLU2d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/nn/intrinsic/modules/fused.py#L54"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.intrinsic.ConvReLU2d" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.intrinsic.</span></span><span class="sig-name descname"><span class="pre">ConvReLU2d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">conv</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">relu</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/intrinsic/modules/fused.html#ConvReLU2d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/nn/intrinsic/modules/fused.py#L54"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.intrinsic.ConvReLU2d" title="Permalink to this definition">¶</a></dt>
 <dd><p>This is a sequential container which calls the Conv2d and ReLU modules.
 During quantization this will be replaced with the corresponding fused module.</p>
 </dd></dl>
diff --git a/main/generated/torch.ao.nn.intrinsic.ConvReLU3d.html b/main/generated/torch.ao.nn.intrinsic.ConvReLU3d.html
index 40666aeb4ae..1bc32cf24df 100644
--- a/main/generated/torch.ao.nn.intrinsic.ConvReLU3d.html
+++ b/main/generated/torch.ao.nn.intrinsic.ConvReLU3d.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>ConvReLU3d<a class="headerlink" href="#convrelu3d" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.nn.intrinsic.ConvReLU3d">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.intrinsic.</span></span><span class="sig-name descname"><span class="pre">ConvReLU3d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">conv</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">relu</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/intrinsic/modules/fused.html#ConvReLU3d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/nn/intrinsic/modules/fused.py#L66"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.intrinsic.ConvReLU3d" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.intrinsic.</span></span><span class="sig-name descname"><span class="pre">ConvReLU3d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">conv</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">relu</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/intrinsic/modules/fused.html#ConvReLU3d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/nn/intrinsic/modules/fused.py#L66"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.intrinsic.ConvReLU3d" title="Permalink to this definition">¶</a></dt>
 <dd><p>This is a sequential container which calls the Conv3d and ReLU modules.
 During quantization this will be replaced with the corresponding fused module.</p>
 </dd></dl>
diff --git a/main/generated/torch.ao.nn.intrinsic.LinearReLU.html b/main/generated/torch.ao.nn.intrinsic.LinearReLU.html
index 364d2af2ef9..8f7115c144f 100644
--- a/main/generated/torch.ao.nn.intrinsic.LinearReLU.html
+++ b/main/generated/torch.ao.nn.intrinsic.LinearReLU.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>LinearReLU<a class="headerlink" href="#linearrelu" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.nn.intrinsic.LinearReLU">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.intrinsic.</span></span><span class="sig-name descname"><span class="pre">LinearReLU</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">linear</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">relu</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/intrinsic/modules/fused.html#LinearReLU"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/nn/intrinsic/modules/fused.py#L78"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.intrinsic.LinearReLU" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.intrinsic.</span></span><span class="sig-name descname"><span class="pre">LinearReLU</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">linear</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">relu</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/intrinsic/modules/fused.html#LinearReLU"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/nn/intrinsic/modules/fused.py#L78"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.intrinsic.LinearReLU" title="Permalink to this definition">¶</a></dt>
 <dd><p>This is a sequential container which calls the Linear and ReLU modules.
 During quantization this will be replaced with the corresponding fused module.</p>
 </dd></dl>
diff --git a/main/generated/torch.ao.nn.intrinsic.qat.ConvBn1d.html b/main/generated/torch.ao.nn.intrinsic.qat.ConvBn1d.html
index 5b4a8f5dd3f..335f39bfa04 100644
--- a/main/generated/torch.ao.nn.intrinsic.qat.ConvBn1d.html
+++ b/main/generated/torch.ao.nn.intrinsic.qat.ConvBn1d.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>ConvBn1d<a class="headerlink" href="#convbn1d" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.nn.intrinsic.qat.ConvBn1d">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.intrinsic.qat.</span></span><span class="sig-name descname"><span class="pre">ConvBn1d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">in_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dilation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'zeros'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-05</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">momentum</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">freeze_bn</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">qconfig</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/intrinsic/qat/modules/conv_fused.html#ConvBn1d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/nn/intrinsic/qat/modules/conv_fused.py#L442"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.intrinsic.qat.ConvBn1d" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.intrinsic.qat.</span></span><span class="sig-name descname"><span class="pre">ConvBn1d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">in_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dilation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'zeros'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-05</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">momentum</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">freeze_bn</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">qconfig</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/intrinsic/qat/modules/conv_fused.html#ConvBn1d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/nn/intrinsic/qat/modules/conv_fused.py#L442"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.intrinsic.qat.ConvBn1d" title="Permalink to this definition">¶</a></dt>
 <dd><p>A ConvBn1d module is a module fused from Conv1d and BatchNorm1d,
 attached with FakeQuantize modules for weight,
 used in quantization aware training.</p>
diff --git a/main/generated/torch.ao.nn.intrinsic.qat.ConvBn2d.html b/main/generated/torch.ao.nn.intrinsic.qat.ConvBn2d.html
index 6643c233978..6c6a1e04184 100644
--- a/main/generated/torch.ao.nn.intrinsic.qat.ConvBn2d.html
+++ b/main/generated/torch.ao.nn.intrinsic.qat.ConvBn2d.html
@@ -300,7 +300,7 @@
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     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>ConvBn2d<a class="headerlink" href="#convbn2d" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.nn.intrinsic.qat.ConvBn2d">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.intrinsic.qat.</span></span><span class="sig-name descname"><span class="pre">ConvBn2d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">in_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dilation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'zeros'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-05</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">momentum</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">freeze_bn</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">qconfig</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/intrinsic/qat/modules/conv_fused.html#ConvBn2d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/nn/intrinsic/qat/modules/conv_fused.py#L639"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.intrinsic.qat.ConvBn2d" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.intrinsic.qat.</span></span><span class="sig-name descname"><span class="pre">ConvBn2d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">in_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dilation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'zeros'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-05</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">momentum</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">freeze_bn</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">qconfig</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/intrinsic/qat/modules/conv_fused.html#ConvBn2d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/nn/intrinsic/qat/modules/conv_fused.py#L639"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.intrinsic.qat.ConvBn2d" title="Permalink to this definition">¶</a></dt>
 <dd><p>A ConvBn2d module is a module fused from Conv2d and BatchNorm2d,
 attached with FakeQuantize modules for weight,
 used in quantization aware training.</p>
diff --git a/main/generated/torch.ao.nn.intrinsic.qat.ConvBn3d.html b/main/generated/torch.ao.nn.intrinsic.qat.ConvBn3d.html
index 7db26aba97b..c6a5d327af3 100644
--- a/main/generated/torch.ao.nn.intrinsic.qat.ConvBn3d.html
+++ b/main/generated/torch.ao.nn.intrinsic.qat.ConvBn3d.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>ConvBn3d<a class="headerlink" href="#convbn3d" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.nn.intrinsic.qat.ConvBn3d">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.intrinsic.qat.</span></span><span class="sig-name descname"><span class="pre">ConvBn3d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">in_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dilation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'zeros'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-05</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">momentum</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">freeze_bn</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">qconfig</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/intrinsic/qat/modules/conv_fused.html#ConvBn3d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/nn/intrinsic/qat/modules/conv_fused.py#L836"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.intrinsic.qat.ConvBn3d" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.intrinsic.qat.</span></span><span class="sig-name descname"><span class="pre">ConvBn3d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">in_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dilation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'zeros'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-05</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">momentum</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">freeze_bn</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">qconfig</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/intrinsic/qat/modules/conv_fused.html#ConvBn3d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/nn/intrinsic/qat/modules/conv_fused.py#L836"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.intrinsic.qat.ConvBn3d" title="Permalink to this definition">¶</a></dt>
 <dd><p>A ConvBn3d module is a module fused from Conv3d and BatchNorm3d,
 attached with FakeQuantize modules for weight,
 used in quantization aware training.</p>
diff --git a/main/generated/torch.ao.nn.intrinsic.qat.ConvBnReLU1d.html b/main/generated/torch.ao.nn.intrinsic.qat.ConvBnReLU1d.html
index 07c047f9649..b82588cd17a 100644
--- a/main/generated/torch.ao.nn.intrinsic.qat.ConvBnReLU1d.html
+++ b/main/generated/torch.ao.nn.intrinsic.qat.ConvBnReLU1d.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>ConvBnReLU1d<a class="headerlink" href="#convbnrelu1d" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.nn.intrinsic.qat.ConvBnReLU1d">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.intrinsic.qat.</span></span><span class="sig-name descname"><span class="pre">ConvBnReLU1d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">in_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dilation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'zeros'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-05</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">momentum</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">freeze_bn</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">qconfig</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/intrinsic/qat/modules/conv_fused.html#ConvBnReLU1d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/nn/intrinsic/qat/modules/conv_fused.py#L511"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.intrinsic.qat.ConvBnReLU1d" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.intrinsic.qat.</span></span><span class="sig-name descname"><span class="pre">ConvBnReLU1d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">in_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dilation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'zeros'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-05</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">momentum</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">freeze_bn</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">qconfig</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/intrinsic/qat/modules/conv_fused.html#ConvBnReLU1d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/nn/intrinsic/qat/modules/conv_fused.py#L511"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.intrinsic.qat.ConvBnReLU1d" title="Permalink to this definition">¶</a></dt>
 <dd><p>A ConvBnReLU1d module is a module fused from Conv1d, BatchNorm1d and ReLU,
 attached with FakeQuantize modules for weight,
 used in quantization aware training.</p>
diff --git a/main/generated/torch.ao.nn.intrinsic.qat.ConvBnReLU2d.html b/main/generated/torch.ao.nn.intrinsic.qat.ConvBnReLU2d.html
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+++ b/main/generated/torch.ao.nn.intrinsic.qat.ConvBnReLU2d.html
@@ -300,7 +300,7 @@
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-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>ConvBnReLU2d<a class="headerlink" href="#convbnrelu2d" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.nn.intrinsic.qat.ConvBnReLU2d">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.intrinsic.qat.</span></span><span class="sig-name descname"><span class="pre">ConvBnReLU2d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">in_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dilation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'zeros'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-05</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">momentum</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">freeze_bn</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">qconfig</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/intrinsic/qat/modules/conv_fused.html#ConvBnReLU2d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/nn/intrinsic/qat/modules/conv_fused.py#L708"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.intrinsic.qat.ConvBnReLU2d" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.intrinsic.qat.</span></span><span class="sig-name descname"><span class="pre">ConvBnReLU2d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">in_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dilation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'zeros'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-05</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">momentum</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">freeze_bn</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">qconfig</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/intrinsic/qat/modules/conv_fused.html#ConvBnReLU2d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/nn/intrinsic/qat/modules/conv_fused.py#L708"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.intrinsic.qat.ConvBnReLU2d" title="Permalink to this definition">¶</a></dt>
 <dd><p>A ConvBnReLU2d module is a module fused from Conv2d, BatchNorm2d and ReLU,
 attached with FakeQuantize modules for weight,
 used in quantization aware training.</p>
diff --git a/main/generated/torch.ao.nn.intrinsic.qat.ConvBnReLU3d.html b/main/generated/torch.ao.nn.intrinsic.qat.ConvBnReLU3d.html
index 3129905b838..a58845bd3f9 100644
--- a/main/generated/torch.ao.nn.intrinsic.qat.ConvBnReLU3d.html
+++ b/main/generated/torch.ao.nn.intrinsic.qat.ConvBnReLU3d.html
@@ -300,7 +300,7 @@
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-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>ConvBnReLU3d<a class="headerlink" href="#convbnrelu3d" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.nn.intrinsic.qat.ConvBnReLU3d">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.intrinsic.qat.</span></span><span class="sig-name descname"><span class="pre">ConvBnReLU3d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">in_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dilation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'zeros'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-05</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">momentum</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">freeze_bn</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">qconfig</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/intrinsic/qat/modules/conv_fused.html#ConvBnReLU3d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/nn/intrinsic/qat/modules/conv_fused.py#L905"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.intrinsic.qat.ConvBnReLU3d" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.intrinsic.qat.</span></span><span class="sig-name descname"><span class="pre">ConvBnReLU3d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">in_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dilation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'zeros'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-05</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">momentum</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">freeze_bn</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">qconfig</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/intrinsic/qat/modules/conv_fused.html#ConvBnReLU3d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/nn/intrinsic/qat/modules/conv_fused.py#L905"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.intrinsic.qat.ConvBnReLU3d" title="Permalink to this definition">¶</a></dt>
 <dd><p>A ConvBnReLU3d module is a module fused from Conv3d, BatchNorm3d and ReLU,
 attached with FakeQuantize modules for weight,
 used in quantization aware training.</p>
diff --git a/main/generated/torch.ao.nn.intrinsic.qat.ConvReLU2d.html b/main/generated/torch.ao.nn.intrinsic.qat.ConvReLU2d.html
index 11bff8def70..9cedd19a353 100644
--- a/main/generated/torch.ao.nn.intrinsic.qat.ConvReLU2d.html
+++ b/main/generated/torch.ao.nn.intrinsic.qat.ConvReLU2d.html
@@ -300,7 +300,7 @@
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     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>ConvReLU2d<a class="headerlink" href="#convrelu2d" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.nn.intrinsic.qat.ConvReLU2d">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.intrinsic.qat.</span></span><span class="sig-name descname"><span class="pre">ConvReLU2d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">in_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dilation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'zeros'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">qconfig</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/intrinsic/qat/modules/conv_fused.html#ConvReLU2d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/nn/intrinsic/qat/modules/conv_fused.py#L778"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.intrinsic.qat.ConvReLU2d" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.intrinsic.qat.</span></span><span class="sig-name descname"><span class="pre">ConvReLU2d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">in_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dilation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'zeros'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">qconfig</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/intrinsic/qat/modules/conv_fused.html#ConvReLU2d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/nn/intrinsic/qat/modules/conv_fused.py#L778"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.intrinsic.qat.ConvReLU2d" title="Permalink to this definition">¶</a></dt>
 <dd><p>A ConvReLU2d module is a fused module of Conv2d and ReLU, attached with
 FakeQuantize modules for weight for
 quantization aware training.</p>
diff --git a/main/generated/torch.ao.nn.intrinsic.qat.ConvReLU3d.html b/main/generated/torch.ao.nn.intrinsic.qat.ConvReLU3d.html
index 08a5e867b71..4025d72cc4b 100644
--- a/main/generated/torch.ao.nn.intrinsic.qat.ConvReLU3d.html
+++ b/main/generated/torch.ao.nn.intrinsic.qat.ConvReLU3d.html
@@ -300,7 +300,7 @@
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     </div>
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@@ -614,7 +614,7 @@
 <h1>ConvReLU3d<a class="headerlink" href="#convrelu3d" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.nn.intrinsic.qat.ConvReLU3d">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.intrinsic.qat.</span></span><span class="sig-name descname"><span class="pre">ConvReLU3d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">in_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dilation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'zeros'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">qconfig</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/intrinsic/qat/modules/conv_fused.html#ConvReLU3d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/nn/intrinsic/qat/modules/conv_fused.py#L976"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.intrinsic.qat.ConvReLU3d" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.intrinsic.qat.</span></span><span class="sig-name descname"><span class="pre">ConvReLU3d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">in_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dilation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'zeros'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">qconfig</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/intrinsic/qat/modules/conv_fused.html#ConvReLU3d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/nn/intrinsic/qat/modules/conv_fused.py#L976"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.intrinsic.qat.ConvReLU3d" title="Permalink to this definition">¶</a></dt>
 <dd><p>A ConvReLU3d module is a fused module of Conv3d and ReLU, attached with
 FakeQuantize modules for weight for
 quantization aware training.</p>
diff --git a/main/generated/torch.ao.nn.intrinsic.qat.LinearReLU.html b/main/generated/torch.ao.nn.intrinsic.qat.LinearReLU.html
index a387ddaaf73..b8eb2211585 100644
--- a/main/generated/torch.ao.nn.intrinsic.qat.LinearReLU.html
+++ b/main/generated/torch.ao.nn.intrinsic.qat.LinearReLU.html
@@ -300,7 +300,7 @@
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     <div class="version">
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+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>LinearReLU<a class="headerlink" href="#linearrelu" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.nn.intrinsic.qat.LinearReLU">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.intrinsic.qat.</span></span><span class="sig-name descname"><span class="pre">LinearReLU</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">in_features</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out_features</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">qconfig</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/intrinsic/qat/modules/linear_relu.html#LinearReLU"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/nn/intrinsic/qat/modules/linear_relu.py#L8"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.intrinsic.qat.LinearReLU" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.intrinsic.qat.</span></span><span class="sig-name descname"><span class="pre">LinearReLU</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">in_features</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out_features</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">qconfig</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/intrinsic/qat/modules/linear_relu.html#LinearReLU"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/nn/intrinsic/qat/modules/linear_relu.py#L8"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.intrinsic.qat.LinearReLU" title="Permalink to this definition">¶</a></dt>
 <dd><p>A LinearReLU module fused from Linear and ReLU modules, attached with
 FakeQuantize modules for weight, used in
 quantization aware training.</p>
diff --git a/main/generated/torch.ao.nn.intrinsic.qat.freeze_bn_stats.html b/main/generated/torch.ao.nn.intrinsic.qat.freeze_bn_stats.html
index 9f558309f5c..69b1695a092 100644
--- a/main/generated/torch.ao.nn.intrinsic.qat.freeze_bn_stats.html
+++ b/main/generated/torch.ao.nn.intrinsic.qat.freeze_bn_stats.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>freeze_bn_stats<a class="headerlink" href="#freeze-bn-stats" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.nn.intrinsic.qat.freeze_bn_stats">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.intrinsic.qat.</span></span><span class="sig-name descname"><span class="pre">freeze_bn_stats</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mod</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/intrinsic/qat/modules/conv_fused.html#freeze_bn_stats"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/nn/intrinsic/qat/modules/conv_fused.py#L1046"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.intrinsic.qat.freeze_bn_stats" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.intrinsic.qat.</span></span><span class="sig-name descname"><span class="pre">freeze_bn_stats</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mod</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/intrinsic/qat/modules/conv_fused.html#freeze_bn_stats"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/nn/intrinsic/qat/modules/conv_fused.py#L1046"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.intrinsic.qat.freeze_bn_stats" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 </div>
diff --git a/main/generated/torch.ao.nn.intrinsic.qat.update_bn_stats.html b/main/generated/torch.ao.nn.intrinsic.qat.update_bn_stats.html
index 70eb3e1d3b3..9c8c03bbf05 100644
--- a/main/generated/torch.ao.nn.intrinsic.qat.update_bn_stats.html
+++ b/main/generated/torch.ao.nn.intrinsic.qat.update_bn_stats.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>update_bn_stats<a class="headerlink" href="#update-bn-stats" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.nn.intrinsic.qat.update_bn_stats">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.intrinsic.qat.</span></span><span class="sig-name descname"><span class="pre">update_bn_stats</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mod</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/intrinsic/qat/modules/conv_fused.html#update_bn_stats"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/nn/intrinsic/qat/modules/conv_fused.py#L1034"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.intrinsic.qat.update_bn_stats" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.intrinsic.qat.</span></span><span class="sig-name descname"><span class="pre">update_bn_stats</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mod</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/intrinsic/qat/modules/conv_fused.html#update_bn_stats"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/nn/intrinsic/qat/modules/conv_fused.py#L1034"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.intrinsic.qat.update_bn_stats" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 </div>
diff --git a/main/generated/torch.ao.nn.intrinsic.quantized.BNReLU2d.html b/main/generated/torch.ao.nn.intrinsic.quantized.BNReLU2d.html
index d71b3b2ab9b..9a10034ff1f 100644
--- a/main/generated/torch.ao.nn.intrinsic.quantized.BNReLU2d.html
+++ b/main/generated/torch.ao.nn.intrinsic.quantized.BNReLU2d.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>BNReLU2d<a class="headerlink" href="#bnrelu2d" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.nn.intrinsic.quantized.BNReLU2d">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.intrinsic.quantized.</span></span><span class="sig-name descname"><span class="pre">BNReLU2d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">num_features</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-05</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">momentum</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/intrinsic/quantized/modules/bn_relu.html#BNReLU2d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/nn/intrinsic/quantized/modules/bn_relu.py#L12"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.intrinsic.quantized.BNReLU2d" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.intrinsic.quantized.</span></span><span class="sig-name descname"><span class="pre">BNReLU2d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">num_features</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-05</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">momentum</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/intrinsic/quantized/modules/bn_relu.html#BNReLU2d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/nn/intrinsic/quantized/modules/bn_relu.py#L12"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.intrinsic.quantized.BNReLU2d" title="Permalink to this definition">¶</a></dt>
 <dd><p>A BNReLU2d module is a fused module of BatchNorm2d and ReLU</p>
 <p>We adopt the same interface as <a class="reference internal" href="torch.ao.nn.quantized.BatchNorm2d.html#torch.ao.nn.quantized.BatchNorm2d" title="torch.ao.nn.quantized.BatchNorm2d"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.ao.nn.quantized.BatchNorm2d</span></code></a>.</p>
 <dl class="field-list simple">
diff --git a/main/generated/torch.ao.nn.intrinsic.quantized.BNReLU3d.html b/main/generated/torch.ao.nn.intrinsic.quantized.BNReLU3d.html
index 53a8728cfcf..ee0641d4d5c 100644
--- a/main/generated/torch.ao.nn.intrinsic.quantized.BNReLU3d.html
+++ b/main/generated/torch.ao.nn.intrinsic.quantized.BNReLU3d.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>BNReLU3d<a class="headerlink" href="#bnrelu3d" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.nn.intrinsic.quantized.BNReLU3d">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.intrinsic.quantized.</span></span><span class="sig-name descname"><span class="pre">BNReLU3d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">num_features</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-05</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">momentum</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/intrinsic/quantized/modules/bn_relu.html#BNReLU3d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/nn/intrinsic/quantized/modules/bn_relu.py#L60"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.intrinsic.quantized.BNReLU3d" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.intrinsic.quantized.</span></span><span class="sig-name descname"><span class="pre">BNReLU3d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">num_features</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-05</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">momentum</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/intrinsic/quantized/modules/bn_relu.html#BNReLU3d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/nn/intrinsic/quantized/modules/bn_relu.py#L60"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.intrinsic.quantized.BNReLU3d" title="Permalink to this definition">¶</a></dt>
 <dd><p>A BNReLU3d module is a fused module of BatchNorm3d and ReLU</p>
 <p>We adopt the same interface as <a class="reference internal" href="torch.ao.nn.quantized.BatchNorm3d.html#torch.ao.nn.quantized.BatchNorm3d" title="torch.ao.nn.quantized.BatchNorm3d"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.ao.nn.quantized.BatchNorm3d</span></code></a>.</p>
 <dl class="field-list simple">
diff --git a/main/generated/torch.ao.nn.intrinsic.quantized.ConvReLU1d.html b/main/generated/torch.ao.nn.intrinsic.quantized.ConvReLU1d.html
index 38da02de0d7..f1abce933d4 100644
--- a/main/generated/torch.ao.nn.intrinsic.quantized.ConvReLU1d.html
+++ b/main/generated/torch.ao.nn.intrinsic.quantized.ConvReLU1d.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>ConvReLU1d<a class="headerlink" href="#convrelu1d" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.nn.intrinsic.quantized.ConvReLU1d">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.intrinsic.quantized.</span></span><span class="sig-name descname"><span class="pre">ConvReLU1d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">in_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dilation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'zeros'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/intrinsic/quantized/modules/conv_relu.html#ConvReLU1d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/nn/intrinsic/quantized/modules/conv_relu.py#L21"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.intrinsic.quantized.ConvReLU1d" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.intrinsic.quantized.</span></span><span class="sig-name descname"><span class="pre">ConvReLU1d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">in_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dilation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'zeros'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/intrinsic/quantized/modules/conv_relu.html#ConvReLU1d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/nn/intrinsic/quantized/modules/conv_relu.py#L21"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.intrinsic.quantized.ConvReLU1d" title="Permalink to this definition">¶</a></dt>
 <dd><p>A ConvReLU1d module is a fused module of Conv1d and ReLU</p>
 <p>We adopt the same interface as <a class="reference internal" href="torch.ao.nn.quantized.Conv1d.html#torch.ao.nn.quantized.Conv1d" title="torch.ao.nn.quantized.Conv1d"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.ao.nn.quantized.Conv1d</span></code></a>.</p>
 <dl class="field-list simple">
diff --git a/main/generated/torch.ao.nn.intrinsic.quantized.ConvReLU2d.html b/main/generated/torch.ao.nn.intrinsic.quantized.ConvReLU2d.html
index 2f768822fa0..903fb8219f7 100644
--- a/main/generated/torch.ao.nn.intrinsic.quantized.ConvReLU2d.html
+++ b/main/generated/torch.ao.nn.intrinsic.quantized.ConvReLU2d.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>ConvReLU2d<a class="headerlink" href="#convrelu2d" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.nn.intrinsic.quantized.ConvReLU2d">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.intrinsic.quantized.</span></span><span class="sig-name descname"><span class="pre">ConvReLU2d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">in_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dilation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'zeros'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/intrinsic/quantized/modules/conv_relu.html#ConvReLU2d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/nn/intrinsic/quantized/modules/conv_relu.py#L102"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.intrinsic.quantized.ConvReLU2d" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.intrinsic.quantized.</span></span><span class="sig-name descname"><span class="pre">ConvReLU2d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">in_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dilation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'zeros'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/intrinsic/quantized/modules/conv_relu.html#ConvReLU2d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/nn/intrinsic/quantized/modules/conv_relu.py#L102"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.intrinsic.quantized.ConvReLU2d" title="Permalink to this definition">¶</a></dt>
 <dd><p>A ConvReLU2d module is a fused module of Conv2d and ReLU</p>
 <p>We adopt the same interface as <a class="reference internal" href="torch.ao.nn.quantized.Conv2d.html#torch.ao.nn.quantized.Conv2d" title="torch.ao.nn.quantized.Conv2d"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.ao.nn.quantized.Conv2d</span></code></a>.</p>
 <dl class="field-list simple">
diff --git a/main/generated/torch.ao.nn.intrinsic.quantized.ConvReLU3d.html b/main/generated/torch.ao.nn.intrinsic.quantized.ConvReLU3d.html
index f51c87c3025..c5f7fd550e4 100644
--- a/main/generated/torch.ao.nn.intrinsic.quantized.ConvReLU3d.html
+++ b/main/generated/torch.ao.nn.intrinsic.quantized.ConvReLU3d.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>ConvReLU3d<a class="headerlink" href="#convrelu3d" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.nn.intrinsic.quantized.ConvReLU3d">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.intrinsic.quantized.</span></span><span class="sig-name descname"><span class="pre">ConvReLU3d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">in_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dilation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'zeros'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/intrinsic/quantized/modules/conv_relu.html#ConvReLU3d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/nn/intrinsic/quantized/modules/conv_relu.py#L184"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.intrinsic.quantized.ConvReLU3d" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.intrinsic.quantized.</span></span><span class="sig-name descname"><span class="pre">ConvReLU3d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">in_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dilation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'zeros'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/intrinsic/quantized/modules/conv_relu.html#ConvReLU3d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/nn/intrinsic/quantized/modules/conv_relu.py#L184"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.intrinsic.quantized.ConvReLU3d" title="Permalink to this definition">¶</a></dt>
 <dd><p>A ConvReLU3d module is a fused module of Conv3d and ReLU</p>
 <p>We adopt the same interface as <a class="reference internal" href="torch.ao.nn.quantized.Conv3d.html#torch.ao.nn.quantized.Conv3d" title="torch.ao.nn.quantized.Conv3d"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.ao.nn.quantized.Conv3d</span></code></a>.</p>
 <p>Attributes: Same as torch.ao.nn.quantized.Conv3d</p>
diff --git a/main/generated/torch.ao.nn.intrinsic.quantized.LinearReLU.html b/main/generated/torch.ao.nn.intrinsic.quantized.LinearReLU.html
index 3c6dcd491ef..901be5d9185 100644
--- a/main/generated/torch.ao.nn.intrinsic.quantized.LinearReLU.html
+++ b/main/generated/torch.ao.nn.intrinsic.quantized.LinearReLU.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>LinearReLU<a class="headerlink" href="#linearrelu" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.nn.intrinsic.quantized.LinearReLU">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.intrinsic.quantized.</span></span><span class="sig-name descname"><span class="pre">LinearReLU</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">in_features</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out_features</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.qint8</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/intrinsic/quantized/modules/linear_relu.html#LinearReLU"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/nn/intrinsic/quantized/modules/linear_relu.py#L15"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.intrinsic.quantized.LinearReLU" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.intrinsic.quantized.</span></span><span class="sig-name descname"><span class="pre">LinearReLU</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">in_features</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out_features</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.qint8</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/intrinsic/quantized/modules/linear_relu.html#LinearReLU"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/nn/intrinsic/quantized/modules/linear_relu.py#L15"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.intrinsic.quantized.LinearReLU" title="Permalink to this definition">¶</a></dt>
 <dd><p>A LinearReLU module fused from Linear and ReLU modules</p>
 <p>We adopt the same interface as <a class="reference internal" href="torch.ao.nn.quantized.Linear.html#torch.ao.nn.quantized.Linear" title="torch.ao.nn.quantized.Linear"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.ao.nn.quantized.Linear</span></code></a>.</p>
 <dl class="field-list simple">
diff --git a/main/generated/torch.ao.nn.intrinsic.quantized.dynamic.LinearReLU.html b/main/generated/torch.ao.nn.intrinsic.quantized.dynamic.LinearReLU.html
index 474c6c317f4..f2971b331b4 100644
--- a/main/generated/torch.ao.nn.intrinsic.quantized.dynamic.LinearReLU.html
+++ b/main/generated/torch.ao.nn.intrinsic.quantized.dynamic.LinearReLU.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>LinearReLU<a class="headerlink" href="#linearrelu" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.nn.intrinsic.quantized.dynamic.LinearReLU">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.intrinsic.quantized.dynamic.</span></span><span class="sig-name descname"><span class="pre">LinearReLU</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">in_features</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out_features</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.qint8</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/intrinsic/quantized/dynamic/modules/linear_relu.html#LinearReLU"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/nn/intrinsic/quantized/dynamic/modules/linear_relu.py#L10"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.intrinsic.quantized.dynamic.LinearReLU" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.intrinsic.quantized.dynamic.</span></span><span class="sig-name descname"><span class="pre">LinearReLU</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">in_features</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out_features</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.qint8</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/intrinsic/quantized/dynamic/modules/linear_relu.html#LinearReLU"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/nn/intrinsic/quantized/dynamic/modules/linear_relu.py#L10"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.intrinsic.quantized.dynamic.LinearReLU" title="Permalink to this definition">¶</a></dt>
 <dd><p>A LinearReLU module fused from Linear and ReLU modules that can be used
 for dynamic quantization.
 Supports both, FP16 and INT8 quantization.</p>
diff --git a/main/generated/torch.ao.nn.qat.Conv2d.html b/main/generated/torch.ao.nn.qat.Conv2d.html
index 20644550897..4582f425251 100644
--- a/main/generated/torch.ao.nn.qat.Conv2d.html
+++ b/main/generated/torch.ao.nn.qat.Conv2d.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>Conv2d<a class="headerlink" href="#conv2d" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.nn.qat.Conv2d">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.qat.</span></span><span class="sig-name descname"><span class="pre">Conv2d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">in_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dilation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'zeros'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">qconfig</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/qat/modules/conv.html#Conv2d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/nn/qat/modules/conv.py#L183"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.qat.Conv2d" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.qat.</span></span><span class="sig-name descname"><span class="pre">Conv2d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">in_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dilation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'zeros'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">qconfig</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/qat/modules/conv.html#Conv2d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/nn/qat/modules/conv.py#L183"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.qat.Conv2d" title="Permalink to this definition">¶</a></dt>
 <dd><p>A Conv2d module attached with FakeQuantize modules for weight,
 used for quantization aware training.</p>
 <p>We adopt the same interface as <cite>torch.nn.Conv2d</cite>, please see
diff --git a/main/generated/torch.ao.nn.qat.Conv3d.html b/main/generated/torch.ao.nn.qat.Conv3d.html
index e6bc59d7d88..41a250e20b5 100644
--- a/main/generated/torch.ao.nn.qat.Conv3d.html
+++ b/main/generated/torch.ao.nn.qat.Conv3d.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>Conv3d<a class="headerlink" href="#conv3d" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.nn.qat.Conv3d">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.qat.</span></span><span class="sig-name descname"><span class="pre">Conv3d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">in_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dilation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'zeros'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">qconfig</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/qat/modules/conv.html#Conv3d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/nn/qat/modules/conv.py#L247"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.qat.Conv3d" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.qat.</span></span><span class="sig-name descname"><span class="pre">Conv3d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">in_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dilation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'zeros'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">qconfig</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/qat/modules/conv.html#Conv3d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/nn/qat/modules/conv.py#L247"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.qat.Conv3d" title="Permalink to this definition">¶</a></dt>
 <dd><p>A Conv3d module attached with FakeQuantize modules for weight,
 used for quantization aware training.</p>
 <p>We adopt the same interface as <cite>torch.nn.Conv3d</cite>, please see
diff --git a/main/generated/torch.ao.nn.qat.Linear.html b/main/generated/torch.ao.nn.qat.Linear.html
index ce721cdec92..5143331a54c 100644
--- a/main/generated/torch.ao.nn.qat.Linear.html
+++ b/main/generated/torch.ao.nn.qat.Linear.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>Linear<a class="headerlink" href="#linear" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.nn.qat.Linear">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.qat.</span></span><span class="sig-name descname"><span class="pre">Linear</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">in_features</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out_features</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">qconfig</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/qat/modules/linear.html#Linear"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/nn/qat/modules/linear.py#L16"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.qat.Linear" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.qat.</span></span><span class="sig-name descname"><span class="pre">Linear</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">in_features</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out_features</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">qconfig</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/qat/modules/linear.html#Linear"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/nn/qat/modules/linear.py#L16"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.qat.Linear" title="Permalink to this definition">¶</a></dt>
 <dd><p>A linear module attached with FakeQuantize modules for weight,
 used for quantization aware training.</p>
 <p>We adopt the same interface as <cite>torch.nn.Linear</cite>, please see
@@ -629,7 +629,7 @@ <h1>Linear<a class="headerlink" href="#linear" title="Permalink to this heading"
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.ao.nn.qat.Linear.from_float">
-<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_float</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mod</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_precomputed_fake_quant</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/qat/modules/linear.html#Linear.from_float"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/nn/qat/modules/linear.py#L51"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.qat.Linear.from_float" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_float</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mod</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_precomputed_fake_quant</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/qat/modules/linear.html#Linear.from_float"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/nn/qat/modules/linear.py#L51"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.qat.Linear.from_float" title="Permalink to this definition">¶</a></dt>
 <dd><p>Create a qat module from a float module or qparams_dict
 Args: <cite>mod</cite> a float module, either produced by torch.ao.quantization utilities
 or directly from user</p>
diff --git a/main/generated/torch.ao.nn.qat.dynamic.Linear.html b/main/generated/torch.ao.nn.qat.dynamic.Linear.html
index 64f0299014e..df0512e5048 100644
--- a/main/generated/torch.ao.nn.qat.dynamic.Linear.html
+++ b/main/generated/torch.ao.nn.qat.dynamic.Linear.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>Linear<a class="headerlink" href="#linear" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.nn.qat.dynamic.Linear">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.qat.dynamic.</span></span><span class="sig-name descname"><span class="pre">Linear</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">in_features</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out_features</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">qconfig</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/qat/dynamic/modules/linear.html#Linear"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/nn/qat/dynamic/modules/linear.py#L13"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.qat.dynamic.Linear" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.qat.dynamic.</span></span><span class="sig-name descname"><span class="pre">Linear</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">in_features</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out_features</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">qconfig</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/qat/dynamic/modules/linear.html#Linear"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/nn/qat/dynamic/modules/linear.py#L13"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.qat.dynamic.Linear" title="Permalink to this definition">¶</a></dt>
 <dd><p>A linear module attached with FakeQuantize modules for weight,
 used for dynamic quantization aware training.</p>
 <p>We adopt the same interface as <cite>torch.nn.Linear</cite>, please see
diff --git a/main/generated/torch.ao.nn.quantizable.LSTM.html b/main/generated/torch.ao.nn.quantizable.LSTM.html
index 9c1d3e85ee7..f0f7371f1d8 100644
--- a/main/generated/torch.ao.nn.quantizable.LSTM.html
+++ b/main/generated/torch.ao.nn.quantizable.LSTM.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>LSTM<a class="headerlink" href="#lstm" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.nn.quantizable.LSTM">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.quantizable.</span></span><span class="sig-name descname"><span class="pre">LSTM</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">hidden_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_layers</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">batch_first</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dropout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bidirectional</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">split_gates</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantizable/modules/rnn.html#LSTM"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/nn/quantizable/modules/rnn.py#L397"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantizable.LSTM" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.quantizable.</span></span><span class="sig-name descname"><span class="pre">LSTM</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">hidden_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_layers</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">batch_first</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dropout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bidirectional</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">split_gates</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantizable/modules/rnn.html#LSTM"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/nn/quantizable/modules/rnn.py#L397"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantizable.LSTM" title="Permalink to this definition">¶</a></dt>
 <dd><p>A quantizable long short-term memory (LSTM).</p>
 <p>For the description and the argument types, please, refer to <a class="reference internal" href="torch.nn.LSTM.html#torch.nn.LSTM" title="torch.nn.LSTM"><code class="xref py py-class docutils literal notranslate"><span class="pre">LSTM</span></code></a></p>
 <dl class="field-list simple">
diff --git a/main/generated/torch.ao.nn.quantizable.MultiheadAttention.html b/main/generated/torch.ao.nn.quantizable.MultiheadAttention.html
index c3887b3493c..4d647df694e 100644
--- a/main/generated/torch.ao.nn.quantizable.MultiheadAttention.html
+++ b/main/generated/torch.ao.nn.quantizable.MultiheadAttention.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,12 +614,12 @@
 <h1>MultiheadAttention<a class="headerlink" href="#multiheadattention" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.nn.quantizable.MultiheadAttention">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.quantizable.</span></span><span class="sig-name descname"><span class="pre">MultiheadAttention</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">embed_dim</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_heads</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dropout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">add_bias_kv</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">add_zero_attn</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kdim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">vdim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">batch_first</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantizable/modules/activation.html#MultiheadAttention"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/nn/quantizable/modules/activation.py#L14"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantizable.MultiheadAttention" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.quantizable.</span></span><span class="sig-name descname"><span class="pre">MultiheadAttention</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">embed_dim</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_heads</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dropout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">add_bias_kv</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">add_zero_attn</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kdim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">vdim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">batch_first</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantizable/modules/activation.html#MultiheadAttention"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/nn/quantizable/modules/activation.py#L14"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantizable.MultiheadAttention" title="Permalink to this definition">¶</a></dt>
 <dd><dl class="field-list simple">
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.ao.nn.quantizable.MultiheadAttention.dequantize">
-<span class="sig-name descname"><span class="pre">dequantize</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantizable/modules/activation.html#MultiheadAttention.dequantize"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/nn/quantizable/modules/activation.py#L189"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantizable.MultiheadAttention.dequantize" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">dequantize</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantizable/modules/activation.html#MultiheadAttention.dequantize"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/nn/quantizable/modules/activation.py#L189"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantizable.MultiheadAttention.dequantize" title="Permalink to this definition">¶</a></dt>
 <dd><p>Utility to convert the quantized MHA back to float.</p>
 <p>The motivation for this is that it is not trivial to conver the weights
 from the format that is used in the quantized version back to the
@@ -628,7 +628,7 @@ <h1>MultiheadAttention<a class="headerlink" href="#multiheadattention" title="Pe
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.ao.nn.quantizable.MultiheadAttention.forward">
-<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">query</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">key</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">value</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">key_padding_mask</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">need_weights</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">attn_mask</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">average_attn_weights</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">is_causal</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantizable/modules/activation.html#MultiheadAttention.forward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/nn/quantizable/modules/activation.py#L276"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantizable.MultiheadAttention.forward" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">query</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">key</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">value</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">key_padding_mask</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">need_weights</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">attn_mask</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">average_attn_weights</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">is_causal</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantizable/modules/activation.html#MultiheadAttention.forward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/nn/quantizable/modules/activation.py#L276"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantizable.MultiheadAttention.forward" title="Permalink to this definition">¶</a></dt>
 <dd><dl class="simple">
 <dt>Note::</dt><dd><p>Please, refer to <a class="reference internal" href="torch.nn.MultiheadAttention.html#torch.nn.MultiheadAttention.forward" title="torch.nn.MultiheadAttention.forward"><code class="xref py py-func docutils literal notranslate"><span class="pre">forward()</span></code></a> for more
 information</p>
diff --git a/main/generated/torch.ao.nn.quantized.BatchNorm2d.html b/main/generated/torch.ao.nn.quantized.BatchNorm2d.html
index 8d25c0ad117..814bb594019 100644
--- a/main/generated/torch.ao.nn.quantized.BatchNorm2d.html
+++ b/main/generated/torch.ao.nn.quantized.BatchNorm2d.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>BatchNorm2d<a class="headerlink" href="#batchnorm2d" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.nn.quantized.BatchNorm2d">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.quantized.</span></span><span class="sig-name descname"><span class="pre">BatchNorm2d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">num_features</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-05</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">momentum</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/modules/batchnorm.html#BatchNorm2d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/nn/quantized/modules/batchnorm.py#L51"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.BatchNorm2d" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.quantized.</span></span><span class="sig-name descname"><span class="pre">BatchNorm2d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">num_features</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-05</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">momentum</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/modules/batchnorm.html#BatchNorm2d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/nn/quantized/modules/batchnorm.py#L51"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.BatchNorm2d" title="Permalink to this definition">¶</a></dt>
 <dd><p>This is the quantized version of <a class="reference internal" href="torch.nn.BatchNorm2d.html#torch.nn.BatchNorm2d" title="torch.nn.BatchNorm2d"><code class="xref py py-class docutils literal notranslate"><span class="pre">BatchNorm2d</span></code></a>.</p>
 <dl class="field-list simple">
 </dl>
diff --git a/main/generated/torch.ao.nn.quantized.BatchNorm3d.html b/main/generated/torch.ao.nn.quantized.BatchNorm3d.html
index 43221a1e0ff..24c203a6369 100644
--- a/main/generated/torch.ao.nn.quantized.BatchNorm3d.html
+++ b/main/generated/torch.ao.nn.quantized.BatchNorm3d.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>BatchNorm3d<a class="headerlink" href="#batchnorm3d" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.nn.quantized.BatchNorm3d">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.quantized.</span></span><span class="sig-name descname"><span class="pre">BatchNorm3d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">num_features</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-05</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">momentum</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/modules/batchnorm.html#BatchNorm3d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/nn/quantized/modules/batchnorm.py#L92"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.BatchNorm3d" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.quantized.</span></span><span class="sig-name descname"><span class="pre">BatchNorm3d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">num_features</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-05</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">momentum</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/modules/batchnorm.html#BatchNorm3d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/nn/quantized/modules/batchnorm.py#L92"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.BatchNorm3d" title="Permalink to this definition">¶</a></dt>
 <dd><p>This is the quantized version of <a class="reference internal" href="torch.nn.BatchNorm3d.html#torch.nn.BatchNorm3d" title="torch.nn.BatchNorm3d"><code class="xref py py-class docutils literal notranslate"><span class="pre">BatchNorm3d</span></code></a>.</p>
 <dl class="field-list simple">
 </dl>
diff --git a/main/generated/torch.ao.nn.quantized.Conv1d.html b/main/generated/torch.ao.nn.quantized.Conv1d.html
index 83bc5c12366..764239f0601 100644
--- a/main/generated/torch.ao.nn.quantized.Conv1d.html
+++ b/main/generated/torch.ao.nn.quantized.Conv1d.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>Conv1d<a class="headerlink" href="#conv1d" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.nn.quantized.Conv1d">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.quantized.</span></span><span class="sig-name descname"><span class="pre">Conv1d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">in_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dilation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'zeros'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/modules/conv.html#Conv1d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/nn/quantized/modules/conv.py#L353"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.Conv1d" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.quantized.</span></span><span class="sig-name descname"><span class="pre">Conv1d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">in_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dilation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'zeros'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/modules/conv.html#Conv1d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/nn/quantized/modules/conv.py#L353"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.Conv1d" title="Permalink to this definition">¶</a></dt>
 <dd><p>Applies a 1D convolution over a quantized input signal composed of
 several quantized input planes.</p>
 <p>For details on input arguments, parameters, and implementation see
@@ -649,7 +649,7 @@ <h1>Conv1d<a class="headerlink" href="#conv1d" title="Permalink to this heading"
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.ao.nn.quantized.Conv1d.from_float">
-<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_float</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mod</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_precomputed_fake_quant</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/modules/conv.html#Conv1d.from_float"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/nn/quantized/modules/conv.py#L469"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.Conv1d.from_float" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_float</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mod</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_precomputed_fake_quant</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/modules/conv.html#Conv1d.from_float"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/nn/quantized/modules/conv.py#L469"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.Conv1d.from_float" title="Permalink to this definition">¶</a></dt>
 <dd><p>Creates a quantized module from a float module or qparams_dict.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.ao.nn.quantized.Conv2d.html b/main/generated/torch.ao.nn.quantized.Conv2d.html
index e72b83ec0e2..3ac3c09d1e8 100644
--- a/main/generated/torch.ao.nn.quantized.Conv2d.html
+++ b/main/generated/torch.ao.nn.quantized.Conv2d.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>Conv2d<a class="headerlink" href="#conv2d" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.nn.quantized.Conv2d">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.quantized.</span></span><span class="sig-name descname"><span class="pre">Conv2d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">in_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dilation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'zeros'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/modules/conv.html#Conv2d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/nn/quantized/modules/conv.py#L482"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.Conv2d" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.quantized.</span></span><span class="sig-name descname"><span class="pre">Conv2d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">in_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dilation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'zeros'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/modules/conv.html#Conv2d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/nn/quantized/modules/conv.py#L482"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.Conv2d" title="Permalink to this definition">¶</a></dt>
 <dd><p>Applies a 2D convolution over a quantized input signal composed of
 several quantized input planes.</p>
 <p>For details on input arguments, parameters, and implementation see
@@ -653,7 +653,7 @@ <h1>Conv2d<a class="headerlink" href="#conv2d" title="Permalink to this heading"
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.ao.nn.quantized.Conv2d.from_float">
-<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_float</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mod</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_precomputed_fake_quant</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/modules/conv.html#Conv2d.from_float"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/nn/quantized/modules/conv.py#L598"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.Conv2d.from_float" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_float</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mod</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_precomputed_fake_quant</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/modules/conv.html#Conv2d.from_float"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/nn/quantized/modules/conv.py#L598"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.Conv2d.from_float" title="Permalink to this definition">¶</a></dt>
 <dd><p>Creates a quantized module from a float module or qparams_dict.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.ao.nn.quantized.Conv3d.html b/main/generated/torch.ao.nn.quantized.Conv3d.html
index 068386825e7..5d7ff6cd87f 100644
--- a/main/generated/torch.ao.nn.quantized.Conv3d.html
+++ b/main/generated/torch.ao.nn.quantized.Conv3d.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>Conv3d<a class="headerlink" href="#conv3d" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.nn.quantized.Conv3d">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.quantized.</span></span><span class="sig-name descname"><span class="pre">Conv3d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">in_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dilation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'zeros'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/modules/conv.html#Conv3d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/nn/quantized/modules/conv.py#L611"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.Conv3d" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.quantized.</span></span><span class="sig-name descname"><span class="pre">Conv3d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">in_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dilation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'zeros'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/modules/conv.html#Conv3d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/nn/quantized/modules/conv.py#L611"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.Conv3d" title="Permalink to this definition">¶</a></dt>
 <dd><p>Applies a 3D convolution over a quantized input signal composed of
 several quantized input planes.</p>
 <p>For details on input arguments, parameters, and implementation see
@@ -653,7 +653,7 @@ <h1>Conv3d<a class="headerlink" href="#conv3d" title="Permalink to this heading"
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.ao.nn.quantized.Conv3d.from_float">
-<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_float</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mod</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_precomputed_fake_quant</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/modules/conv.html#Conv3d.from_float"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/nn/quantized/modules/conv.py#L728"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.Conv3d.from_float" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_float</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mod</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_precomputed_fake_quant</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/modules/conv.html#Conv3d.from_float"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/nn/quantized/modules/conv.py#L728"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.Conv3d.from_float" title="Permalink to this definition">¶</a></dt>
 <dd><p>Creates a quantized module from a float module or qparams_dict.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.ao.nn.quantized.ConvTranspose1d.html b/main/generated/torch.ao.nn.quantized.ConvTranspose1d.html
index 06c4537cdb3..c028eb9589f 100644
--- a/main/generated/torch.ao.nn.quantized.ConvTranspose1d.html
+++ b/main/generated/torch.ao.nn.quantized.ConvTranspose1d.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>ConvTranspose1d<a class="headerlink" href="#convtranspose1d" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.nn.quantized.ConvTranspose1d">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.quantized.</span></span><span class="sig-name descname"><span class="pre">ConvTranspose1d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">in_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dilation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'zeros'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/modules/conv.html#ConvTranspose1d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/nn/quantized/modules/conv.py#L871"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.ConvTranspose1d" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.quantized.</span></span><span class="sig-name descname"><span class="pre">ConvTranspose1d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">in_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dilation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'zeros'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/modules/conv.html#ConvTranspose1d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/nn/quantized/modules/conv.py#L871"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.ConvTranspose1d" title="Permalink to this definition">¶</a></dt>
 <dd><p>Applies a 1D transposed convolution operator over an input image
 composed of several input planes.
 For details on input arguments, parameters, and implementation see
diff --git a/main/generated/torch.ao.nn.quantized.ConvTranspose2d.html b/main/generated/torch.ao.nn.quantized.ConvTranspose2d.html
index ab19e347aa4..34ebd6bc03a 100644
--- a/main/generated/torch.ao.nn.quantized.ConvTranspose2d.html
+++ b/main/generated/torch.ao.nn.quantized.ConvTranspose2d.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>ConvTranspose2d<a class="headerlink" href="#convtranspose2d" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.nn.quantized.ConvTranspose2d">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.quantized.</span></span><span class="sig-name descname"><span class="pre">ConvTranspose2d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">in_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dilation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'zeros'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/modules/conv.html#ConvTranspose2d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/nn/quantized/modules/conv.py#L996"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.ConvTranspose2d" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.quantized.</span></span><span class="sig-name descname"><span class="pre">ConvTranspose2d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">in_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dilation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'zeros'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/modules/conv.html#ConvTranspose2d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/nn/quantized/modules/conv.py#L996"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.ConvTranspose2d" title="Permalink to this definition">¶</a></dt>
 <dd><p>Applies a 2D transposed convolution operator over an input image
 composed of several input planes.
 For details on input arguments, parameters, and implementation see
diff --git a/main/generated/torch.ao.nn.quantized.ConvTranspose3d.html b/main/generated/torch.ao.nn.quantized.ConvTranspose3d.html
index 5fa8d41b86e..268269e078a 100644
--- a/main/generated/torch.ao.nn.quantized.ConvTranspose3d.html
+++ b/main/generated/torch.ao.nn.quantized.ConvTranspose3d.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>ConvTranspose3d<a class="headerlink" href="#convtranspose3d" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.nn.quantized.ConvTranspose3d">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.quantized.</span></span><span class="sig-name descname"><span class="pre">ConvTranspose3d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">in_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dilation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'zeros'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/modules/conv.html#ConvTranspose3d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/nn/quantized/modules/conv.py#L1119"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.ConvTranspose3d" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.quantized.</span></span><span class="sig-name descname"><span class="pre">ConvTranspose3d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">in_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dilation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'zeros'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/modules/conv.html#ConvTranspose3d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/nn/quantized/modules/conv.py#L1119"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.ConvTranspose3d" title="Permalink to this definition">¶</a></dt>
 <dd><p>Applies a 3D transposed convolution operator over an input image
 composed of several input planes.
 For details on input arguments, parameters, and implementation see
diff --git a/main/generated/torch.ao.nn.quantized.ELU.html b/main/generated/torch.ao.nn.quantized.ELU.html
index 8da433a27d2..525d158791d 100644
--- a/main/generated/torch.ao.nn.quantized.ELU.html
+++ b/main/generated/torch.ao.nn.quantized.ELU.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>ELU<a class="headerlink" href="#elu" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.nn.quantized.ELU">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.quantized.</span></span><span class="sig-name descname"><span class="pre">ELU</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">scale</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">zero_point</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/modules/activation.html#ELU"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/nn/quantized/modules/activation.py#L89"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.ELU" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.quantized.</span></span><span class="sig-name descname"><span class="pre">ELU</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">scale</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">zero_point</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/modules/activation.html#ELU"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/nn/quantized/modules/activation.py#L89"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.ELU" title="Permalink to this definition">¶</a></dt>
 <dd><p>This is the quantized equivalent of <a class="reference internal" href="torch.nn.ELU.html#torch.nn.ELU" title="torch.nn.ELU"><code class="xref py py-class docutils literal notranslate"><span class="pre">ELU</span></code></a>.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.ao.nn.quantized.Embedding.html b/main/generated/torch.ao.nn.quantized.Embedding.html
index fb4f887f0ad..63ea9fd8e84 100644
--- a/main/generated/torch.ao.nn.quantized.Embedding.html
+++ b/main/generated/torch.ao.nn.quantized.Embedding.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>Embedding<a class="headerlink" href="#embedding" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.nn.quantized.Embedding">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.quantized.</span></span><span class="sig-name descname"><span class="pre">Embedding</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">num_embeddings</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">embedding_dim</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding_idx</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_norm</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">norm_type</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale_grad_by_freq</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sparse</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.quint8</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/modules/embedding_ops.html#Embedding"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/nn/quantized/modules/embedding_ops.py#L98"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.Embedding" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.quantized.</span></span><span class="sig-name descname"><span class="pre">Embedding</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">num_embeddings</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">embedding_dim</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding_idx</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_norm</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">norm_type</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale_grad_by_freq</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sparse</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.quint8</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/modules/embedding_ops.html#Embedding"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/nn/quantized/modules/embedding_ops.py#L98"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.Embedding" title="Permalink to this definition">¶</a></dt>
 <dd><p>A quantized Embedding module with quantized packed weights as inputs.
 We adopt the same interface as <cite>torch.nn.Embedding</cite>, please see
 <a class="reference external" href="https://pytorch.org/docs/stable/generated/torch.nn.Embedding.html">https://pytorch.org/docs/stable/generated/torch.nn.Embedding.html</a> for documentation.</p>
@@ -638,7 +638,7 @@ <h1>Embedding<a class="headerlink" href="#embedding" title="Permalink to this he
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.ao.nn.quantized.Embedding.from_float">
-<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_float</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mod</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_precomputed_fake_quant</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/modules/embedding_ops.html#Embedding.from_float"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/nn/quantized/modules/embedding_ops.py#L190"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.Embedding.from_float" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_float</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mod</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_precomputed_fake_quant</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/modules/embedding_ops.html#Embedding.from_float"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/nn/quantized/modules/embedding_ops.py#L190"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.Embedding.from_float" title="Permalink to this definition">¶</a></dt>
 <dd><p>Create a quantized embedding module from a float module</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.ao.nn.quantized.EmbeddingBag.html b/main/generated/torch.ao.nn.quantized.EmbeddingBag.html
index 71da5c0fdd1..8c775b160f6 100644
--- a/main/generated/torch.ao.nn.quantized.EmbeddingBag.html
+++ b/main/generated/torch.ao.nn.quantized.EmbeddingBag.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>EmbeddingBag<a class="headerlink" href="#embeddingbag" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.nn.quantized.EmbeddingBag">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.quantized.</span></span><span class="sig-name descname"><span class="pre">EmbeddingBag</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">num_embeddings</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">embedding_dim</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_norm</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">norm_type</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale_grad_by_freq</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'sum'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sparse</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">include_last_offset</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.quint8</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/modules/embedding_ops.html#EmbeddingBag"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/nn/quantized/modules/embedding_ops.py#L261"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.EmbeddingBag" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.quantized.</span></span><span class="sig-name descname"><span class="pre">EmbeddingBag</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">num_embeddings</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">embedding_dim</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_norm</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">norm_type</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale_grad_by_freq</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'sum'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sparse</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">include_last_offset</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.quint8</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/modules/embedding_ops.html#EmbeddingBag"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/nn/quantized/modules/embedding_ops.py#L261"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.EmbeddingBag" title="Permalink to this definition">¶</a></dt>
 <dd><p>A quantized EmbeddingBag module with quantized packed weights as inputs.
 We adopt the same interface as <cite>torch.nn.EmbeddingBag</cite>, please see
 <a class="reference external" href="https://pytorch.org/docs/stable/generated/torch.nn.EmbeddingBag.html">https://pytorch.org/docs/stable/generated/torch.nn.EmbeddingBag.html</a> for documentation.</p>
@@ -639,7 +639,7 @@ <h1>EmbeddingBag<a class="headerlink" href="#embeddingbag" title="Permalink to t
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.ao.nn.quantized.EmbeddingBag.from_float">
-<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_float</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mod</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_precomputed_fake_quant</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/modules/embedding_ops.html#EmbeddingBag.from_float"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/nn/quantized/modules/embedding_ops.py#L340"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.EmbeddingBag.from_float" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_float</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mod</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_precomputed_fake_quant</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/modules/embedding_ops.html#EmbeddingBag.from_float"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/nn/quantized/modules/embedding_ops.py#L340"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.EmbeddingBag.from_float" title="Permalink to this definition">¶</a></dt>
 <dd><p>Create a quantized embedding_bag module from a float module</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.ao.nn.quantized.FXFloatFunctional.html b/main/generated/torch.ao.nn.quantized.FXFloatFunctional.html
index 0a37fa27d83..1fe641dc244 100644
--- a/main/generated/torch.ao.nn.quantized.FXFloatFunctional.html
+++ b/main/generated/torch.ao.nn.quantized.FXFloatFunctional.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>FXFloatFunctional<a class="headerlink" href="#fxfloatfunctional" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.nn.quantized.FXFloatFunctional">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.quantized.</span></span><span class="sig-name descname"><span class="pre">FXFloatFunctional</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/modules/functional_modules.html#FXFloatFunctional"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/nn/quantized/modules/functional_modules.py#L101"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.FXFloatFunctional" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.quantized.</span></span><span class="sig-name descname"><span class="pre">FXFloatFunctional</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/modules/functional_modules.html#FXFloatFunctional"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/nn/quantized/modules/functional_modules.py#L101"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.FXFloatFunctional" title="Permalink to this definition">¶</a></dt>
 <dd><p>module to replace FloatFunctional module before FX graph mode quantization,
 since activation_post_process will be inserted in top level module directly</p>
 <dl class="simple">
diff --git a/main/generated/torch.ao.nn.quantized.FloatFunctional.html b/main/generated/torch.ao.nn.quantized.FloatFunctional.html
index e2d46e4b5fc..f667c77d869 100644
--- a/main/generated/torch.ao.nn.quantized.FloatFunctional.html
+++ b/main/generated/torch.ao.nn.quantized.FloatFunctional.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>FloatFunctional<a class="headerlink" href="#floatfunctional" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.nn.quantized.FloatFunctional">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.quantized.</span></span><span class="sig-name descname"><span class="pre">FloatFunctional</span></span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/modules/functional_modules.html#FloatFunctional"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/nn/quantized/modules/functional_modules.py#L11"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.FloatFunctional" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.quantized.</span></span><span class="sig-name descname"><span class="pre">FloatFunctional</span></span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/modules/functional_modules.html#FloatFunctional"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/nn/quantized/modules/functional_modules.py#L11"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.FloatFunctional" title="Permalink to this definition">¶</a></dt>
 <dd><p>State collector class for float operations.</p>
 <p>The instance of this class can be used instead of the <code class="docutils literal notranslate"><span class="pre">torch.</span></code> prefix for
 some operations. See example usage below.</p>
diff --git a/main/generated/torch.ao.nn.quantized.GroupNorm.html b/main/generated/torch.ao.nn.quantized.GroupNorm.html
index 56b64131020..775a6fad5ec 100644
--- a/main/generated/torch.ao.nn.quantized.GroupNorm.html
+++ b/main/generated/torch.ao.nn.quantized.GroupNorm.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>GroupNorm<a class="headerlink" href="#groupnorm" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.nn.quantized.GroupNorm">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.quantized.</span></span><span class="sig-name descname"><span class="pre">GroupNorm</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">num_groups</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weight</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">zero_point</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-05</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">affine</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/modules/normalization.html#GroupNorm"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/nn/quantized/modules/normalization.py#L88"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.GroupNorm" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.quantized.</span></span><span class="sig-name descname"><span class="pre">GroupNorm</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">num_groups</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weight</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">zero_point</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-05</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">affine</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/modules/normalization.html#GroupNorm"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/nn/quantized/modules/normalization.py#L88"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.GroupNorm" title="Permalink to this definition">¶</a></dt>
 <dd><p>This is the quantized version of <a class="reference internal" href="torch.nn.GroupNorm.html#torch.nn.GroupNorm" title="torch.nn.GroupNorm"><code class="xref py py-class docutils literal notranslate"><span class="pre">GroupNorm</span></code></a>.</p>
 <dl class="simple">
 <dt>Additional args:</dt><dd><ul class="simple">
diff --git a/main/generated/torch.ao.nn.quantized.Hardswish.html b/main/generated/torch.ao.nn.quantized.Hardswish.html
index d06fc72a254..04354314232 100644
--- a/main/generated/torch.ao.nn.quantized.Hardswish.html
+++ b/main/generated/torch.ao.nn.quantized.Hardswish.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>Hardswish<a class="headerlink" href="#hardswish" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.nn.quantized.Hardswish">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.quantized.</span></span><span class="sig-name descname"><span class="pre">Hardswish</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">scale</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">zero_point</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/modules/activation.html#Hardswish"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/nn/quantized/modules/activation.py#L59"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.Hardswish" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.quantized.</span></span><span class="sig-name descname"><span class="pre">Hardswish</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">scale</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">zero_point</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/modules/activation.html#Hardswish"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/nn/quantized/modules/activation.py#L59"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.Hardswish" title="Permalink to this definition">¶</a></dt>
 <dd><p>This is the quantized version of <a class="reference internal" href="torch.nn.Hardswish.html#torch.nn.Hardswish" title="torch.nn.Hardswish"><code class="xref py py-class docutils literal notranslate"><span class="pre">Hardswish</span></code></a>.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.ao.nn.quantized.InstanceNorm1d.html b/main/generated/torch.ao.nn.quantized.InstanceNorm1d.html
index ee83e9bfad2..ddf969fb7a9 100644
--- a/main/generated/torch.ao.nn.quantized.InstanceNorm1d.html
+++ b/main/generated/torch.ao.nn.quantized.InstanceNorm1d.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>InstanceNorm1d<a class="headerlink" href="#instancenorm1d" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.nn.quantized.InstanceNorm1d">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.quantized.</span></span><span class="sig-name descname"><span class="pre">InstanceNorm1d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">num_features</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weight</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">zero_point</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-05</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">momentum</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">affine</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">track_running_stats</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/modules/normalization.html#InstanceNorm1d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/nn/quantized/modules/normalization.py#L148"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.InstanceNorm1d" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.quantized.</span></span><span class="sig-name descname"><span class="pre">InstanceNorm1d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">num_features</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weight</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">zero_point</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-05</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">momentum</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">affine</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">track_running_stats</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/modules/normalization.html#InstanceNorm1d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/nn/quantized/modules/normalization.py#L148"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.InstanceNorm1d" title="Permalink to this definition">¶</a></dt>
 <dd><p>This is the quantized version of <a class="reference internal" href="torch.nn.InstanceNorm1d.html#torch.nn.InstanceNorm1d" title="torch.nn.InstanceNorm1d"><code class="xref py py-class docutils literal notranslate"><span class="pre">InstanceNorm1d</span></code></a>.</p>
 <dl class="simple">
 <dt>Additional args:</dt><dd><ul class="simple">
diff --git a/main/generated/torch.ao.nn.quantized.InstanceNorm2d.html b/main/generated/torch.ao.nn.quantized.InstanceNorm2d.html
index dbf68eb6161..2ebd1001249 100644
--- a/main/generated/torch.ao.nn.quantized.InstanceNorm2d.html
+++ b/main/generated/torch.ao.nn.quantized.InstanceNorm2d.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>InstanceNorm2d<a class="headerlink" href="#instancenorm2d" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.nn.quantized.InstanceNorm2d">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.quantized.</span></span><span class="sig-name descname"><span class="pre">InstanceNorm2d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">num_features</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weight</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">zero_point</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-05</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">momentum</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">affine</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">track_running_stats</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/modules/normalization.html#InstanceNorm2d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/nn/quantized/modules/normalization.py#L215"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.InstanceNorm2d" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.quantized.</span></span><span class="sig-name descname"><span class="pre">InstanceNorm2d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">num_features</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weight</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">zero_point</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-05</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">momentum</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">affine</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">track_running_stats</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/modules/normalization.html#InstanceNorm2d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/nn/quantized/modules/normalization.py#L215"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.InstanceNorm2d" title="Permalink to this definition">¶</a></dt>
 <dd><p>This is the quantized version of <a class="reference internal" href="torch.nn.InstanceNorm2d.html#torch.nn.InstanceNorm2d" title="torch.nn.InstanceNorm2d"><code class="xref py py-class docutils literal notranslate"><span class="pre">InstanceNorm2d</span></code></a>.</p>
 <dl class="simple">
 <dt>Additional args:</dt><dd><ul class="simple">
diff --git a/main/generated/torch.ao.nn.quantized.InstanceNorm3d.html b/main/generated/torch.ao.nn.quantized.InstanceNorm3d.html
index 2e2d196c970..a1a2dd97402 100644
--- a/main/generated/torch.ao.nn.quantized.InstanceNorm3d.html
+++ b/main/generated/torch.ao.nn.quantized.InstanceNorm3d.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>InstanceNorm3d<a class="headerlink" href="#instancenorm3d" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.nn.quantized.InstanceNorm3d">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.quantized.</span></span><span class="sig-name descname"><span class="pre">InstanceNorm3d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">num_features</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weight</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">zero_point</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-05</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">momentum</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">affine</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">track_running_stats</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/modules/normalization.html#InstanceNorm3d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/nn/quantized/modules/normalization.py#L282"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.InstanceNorm3d" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.quantized.</span></span><span class="sig-name descname"><span class="pre">InstanceNorm3d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">num_features</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weight</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">zero_point</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-05</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">momentum</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">affine</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">track_running_stats</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/modules/normalization.html#InstanceNorm3d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/nn/quantized/modules/normalization.py#L282"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.InstanceNorm3d" title="Permalink to this definition">¶</a></dt>
 <dd><p>This is the quantized version of <a class="reference internal" href="torch.nn.InstanceNorm3d.html#torch.nn.InstanceNorm3d" title="torch.nn.InstanceNorm3d"><code class="xref py py-class docutils literal notranslate"><span class="pre">InstanceNorm3d</span></code></a>.</p>
 <dl class="simple">
 <dt>Additional args:</dt><dd><ul class="simple">
diff --git a/main/generated/torch.ao.nn.quantized.LayerNorm.html b/main/generated/torch.ao.nn.quantized.LayerNorm.html
index 8bff9abdf47..26b83145f48 100644
--- a/main/generated/torch.ao.nn.quantized.LayerNorm.html
+++ b/main/generated/torch.ao.nn.quantized.LayerNorm.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>LayerNorm<a class="headerlink" href="#layernorm" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.nn.quantized.LayerNorm">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.quantized.</span></span><span class="sig-name descname"><span class="pre">LayerNorm</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">normalized_shape</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weight</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">zero_point</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-05</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">elementwise_affine</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/modules/normalization.html#LayerNorm"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/nn/quantized/modules/normalization.py#L14"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.LayerNorm" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.quantized.</span></span><span class="sig-name descname"><span class="pre">LayerNorm</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">normalized_shape</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weight</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">zero_point</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-05</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">elementwise_affine</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/modules/normalization.html#LayerNorm"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/nn/quantized/modules/normalization.py#L14"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.LayerNorm" title="Permalink to this definition">¶</a></dt>
 <dd><p>This is the quantized version of <a class="reference internal" href="torch.nn.LayerNorm.html#torch.nn.LayerNorm" title="torch.nn.LayerNorm"><code class="xref py py-class docutils literal notranslate"><span class="pre">LayerNorm</span></code></a>.</p>
 <dl class="simple">
 <dt>Additional args:</dt><dd><ul class="simple">
diff --git a/main/generated/torch.ao.nn.quantized.LeakyReLU.html b/main/generated/torch.ao.nn.quantized.LeakyReLU.html
index addafb2d4e0..ae892eb2199 100644
--- a/main/generated/torch.ao.nn.quantized.LeakyReLU.html
+++ b/main/generated/torch.ao.nn.quantized.LeakyReLU.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>LeakyReLU<a class="headerlink" href="#leakyrelu" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.nn.quantized.LeakyReLU">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.quantized.</span></span><span class="sig-name descname"><span class="pre">LeakyReLU</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">scale</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">zero_point</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">negative_slope</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.01</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/modules/activation.html#LeakyReLU"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/nn/quantized/modules/activation.py#L121"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.LeakyReLU" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.quantized.</span></span><span class="sig-name descname"><span class="pre">LeakyReLU</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">scale</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">zero_point</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">negative_slope</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.01</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/modules/activation.html#LeakyReLU"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/nn/quantized/modules/activation.py#L121"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.LeakyReLU" title="Permalink to this definition">¶</a></dt>
 <dd><p>This is the quantized equivalent of <a class="reference internal" href="torch.nn.LeakyReLU.html#torch.nn.LeakyReLU" title="torch.nn.LeakyReLU"><code class="xref py py-class docutils literal notranslate"><span class="pre">LeakyReLU</span></code></a>.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.ao.nn.quantized.Linear.html b/main/generated/torch.ao.nn.quantized.Linear.html
index a7e619e1e1b..7deac0e358a 100644
--- a/main/generated/torch.ao.nn.quantized.Linear.html
+++ b/main/generated/torch.ao.nn.quantized.Linear.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>Linear<a class="headerlink" href="#linear" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.nn.quantized.Linear">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.quantized.</span></span><span class="sig-name descname"><span class="pre">Linear</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">in_features</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out_features</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias_</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.qint8</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/modules/linear.html#Linear"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/nn/quantized/modules/linear.py#L120"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.Linear" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.quantized.</span></span><span class="sig-name descname"><span class="pre">Linear</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">in_features</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out_features</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias_</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.qint8</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/modules/linear.html#Linear"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/nn/quantized/modules/linear.py#L120"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.Linear" title="Permalink to this definition">¶</a></dt>
 <dd><p>A quantized linear module with quantized tensor as inputs and outputs.
 We adopt the same interface as <cite>torch.nn.Linear</cite>, please see
 <a class="reference external" href="https://pytorch.org/docs/stable/nn.html#torch.nn.Linear">https://pytorch.org/docs/stable/nn.html#torch.nn.Linear</a> for documentation.</p>
@@ -643,7 +643,7 @@ <h1>Linear<a class="headerlink" href="#linear" title="Permalink to this heading"
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.ao.nn.quantized.Linear.from_float">
-<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_float</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mod</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_precomputed_fake_quant</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/modules/linear.html#Linear.from_float"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/nn/quantized/modules/linear.py#L284"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.Linear.from_float" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_float</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mod</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_precomputed_fake_quant</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/modules/linear.html#Linear.from_float"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/nn/quantized/modules/linear.py#L284"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.Linear.from_float" title="Permalink to this definition">¶</a></dt>
 <dd><p>Create a quantized module from an observed float module</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -659,7 +659,7 @@ <h1>Linear<a class="headerlink" href="#linear" title="Permalink to this heading"
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.ao.nn.quantized.Linear.from_reference">
-<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_reference</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">ref_qlinear</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_scale</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_zero_point</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/modules/linear.html#Linear.from_reference"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/nn/quantized/modules/linear.py#L346"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.Linear.from_reference" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_reference</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">ref_qlinear</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_scale</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_zero_point</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/modules/linear.html#Linear.from_reference"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/nn/quantized/modules/linear.py#L346"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.Linear.from_reference" title="Permalink to this definition">¶</a></dt>
 <dd><p>Create a (fbgemm/qnnpack) quantized module from a reference quantized module</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.ao.nn.quantized.QFunctional.html b/main/generated/torch.ao.nn.quantized.QFunctional.html
index 066a94b9ee7..9de1d7d2815 100644
--- a/main/generated/torch.ao.nn.quantized.QFunctional.html
+++ b/main/generated/torch.ao.nn.quantized.QFunctional.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>QFunctional<a class="headerlink" href="#qfunctional" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.nn.quantized.QFunctional">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.quantized.</span></span><span class="sig-name descname"><span class="pre">QFunctional</span></span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/modules/functional_modules.html#QFunctional"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/nn/quantized/modules/functional_modules.py#L164"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.QFunctional" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.quantized.</span></span><span class="sig-name descname"><span class="pre">QFunctional</span></span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/modules/functional_modules.html#QFunctional"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/nn/quantized/modules/functional_modules.py#L164"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.QFunctional" title="Permalink to this definition">¶</a></dt>
 <dd><p>Wrapper class for quantized operations.</p>
 <p>The instance of this class can be used instead of the
 <code class="docutils literal notranslate"><span class="pre">torch.ops.quantized</span></code> prefix. See example usage below.</p>
diff --git a/main/generated/torch.ao.nn.quantized.ReLU6.html b/main/generated/torch.ao.nn.quantized.ReLU6.html
index a9082f42c59..9b266892229 100644
--- a/main/generated/torch.ao.nn.quantized.ReLU6.html
+++ b/main/generated/torch.ao.nn.quantized.ReLU6.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>ReLU6<a class="headerlink" href="#relu6" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.nn.quantized.ReLU6">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.quantized.</span></span><span class="sig-name descname"><span class="pre">ReLU6</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/modules/activation.html#ReLU6"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/nn/quantized/modules/activation.py#L19"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.ReLU6" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.quantized.</span></span><span class="sig-name descname"><span class="pre">ReLU6</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/modules/activation.html#ReLU6"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/nn/quantized/modules/activation.py#L19"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.ReLU6" title="Permalink to this definition">¶</a></dt>
 <dd><p>Applies the element-wise function:</p>
 <p><span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mtext>ReLU6</mtext><mo stretchy="false">(</mo><mi>x</mi><mo stretchy="false">)</mo><mo>=</mo><mi>min</mi><mo>⁡</mo><mo stretchy="false">(</mo><mi>max</mi><mo>⁡</mo><mo stretchy="false">(</mo><msub><mi>x</mi><mn>0</mn></msub><mo separator="true">,</mo><mi>x</mi><mo stretchy="false">)</mo><mo separator="true">,</mo><mi>q</mi><mo stretchy="false">(</mo><mn>6</mn><mo stretchy="false">)</mo><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">\text{ReLU6}(x) = \min(\max(x_0, x), q(6))</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mord text"><span class="mord">ReLU6</span></span><span class="mopen">(</span><span class="mord mathnormal">x</span><span class="mclose">)</span><span class="mspace" style="margin-right:0.2778em;"></span><span class="mrel">=</span><span class="mspace" style="margin-right:0.2778em;"></span></span><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mop">min</span><span class="mopen">(</span><span class="mop">max</span><span class="mopen">(</span><span class="mord"><span class="mord mathnormal">x</span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.3011em;"><span style="top:-2.55em;margin-left:0em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight">0</span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.15em;"><span></span></span></span></span></span></span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord mathnormal">x</span><span class="mclose">)</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord mathnormal" style="margin-right:0.03588em;">q</span><span class="mopen">(</span><span class="mord">6</span><span class="mclose">))</span></span></span></span></span>, where <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><msub><mi>x</mi><mn>0</mn></msub></mrow><annotation encoding="application/x-tex">x_0</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.5806em;vertical-align:-0.15em;"></span><span class="mord"><span class="mord mathnormal">x</span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.3011em;"><span style="top:-2.55em;margin-left:0em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight">0</span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.15em;"><span></span></span></span></span></span></span></span></span></span></span> is the
 zero_point, and <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>q</mi><mo stretchy="false">(</mo><mn>6</mn><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">q(6)</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mord mathnormal" style="margin-right:0.03588em;">q</span><span class="mopen">(</span><span class="mord">6</span><span class="mclose">)</span></span></span></span></span> is the quantized representation of number 6.</p>
diff --git a/main/generated/torch.ao.nn.quantized.Sigmoid.html b/main/generated/torch.ao.nn.quantized.Sigmoid.html
index 636bd5c9c7d..48b92e6258b 100644
--- a/main/generated/torch.ao.nn.quantized.Sigmoid.html
+++ b/main/generated/torch.ao.nn.quantized.Sigmoid.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>Sigmoid<a class="headerlink" href="#sigmoid" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.nn.quantized.Sigmoid">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.quantized.</span></span><span class="sig-name descname"><span class="pre">Sigmoid</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">output_scale</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_zero_point</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/modules/activation.html#Sigmoid"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/nn/quantized/modules/activation.py#L162"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.Sigmoid" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.quantized.</span></span><span class="sig-name descname"><span class="pre">Sigmoid</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">output_scale</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_zero_point</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/modules/activation.html#Sigmoid"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/nn/quantized/modules/activation.py#L162"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.Sigmoid" title="Permalink to this definition">¶</a></dt>
 <dd><p>This is the quantized equivalent of <a class="reference internal" href="torch.nn.Sigmoid.html#torch.nn.Sigmoid" title="torch.nn.Sigmoid"><code class="xref py py-class docutils literal notranslate"><span class="pre">Sigmoid</span></code></a>.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.ao.nn.quantized.dynamic.GRU.html b/main/generated/torch.ao.nn.quantized.dynamic.GRU.html
index f1218a5fb99..c71a132b1cd 100644
--- a/main/generated/torch.ao.nn.quantized.dynamic.GRU.html
+++ b/main/generated/torch.ao.nn.quantized.dynamic.GRU.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>GRU<a class="headerlink" href="#gru" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.nn.quantized.dynamic.GRU">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.quantized.dynamic.</span></span><span class="sig-name descname"><span class="pre">GRU</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/dynamic/modules/rnn.html#GRU"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/nn/quantized/dynamic/modules/rnn.py#L683"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.dynamic.GRU" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.quantized.dynamic.</span></span><span class="sig-name descname"><span class="pre">GRU</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/dynamic/modules/rnn.html#GRU"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/nn/quantized/dynamic/modules/rnn.py#L683"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.dynamic.GRU" title="Permalink to this definition">¶</a></dt>
 <dd><p>Applies a multi-layer gated recurrent unit (GRU) RNN to an input sequence.</p>
 <p>For each element in the input sequence, each layer computes the following
 function:</p>
diff --git a/main/generated/torch.ao.nn.quantized.dynamic.GRUCell.html b/main/generated/torch.ao.nn.quantized.dynamic.GRUCell.html
index 0daf9beda46..c6d73a2462c 100644
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+++ b/main/generated/torch.ao.nn.quantized.dynamic.GRUCell.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
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     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>GRUCell<a class="headerlink" href="#grucell" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.nn.quantized.dynamic.GRUCell">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.quantized.dynamic.</span></span><span class="sig-name descname"><span class="pre">GRUCell</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">hidden_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.qint8</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/dynamic/modules/rnn.html#GRUCell"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/nn/quantized/dynamic/modules/rnn.py#L1314"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.dynamic.GRUCell" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.quantized.dynamic.</span></span><span class="sig-name descname"><span class="pre">GRUCell</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">hidden_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.qint8</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/dynamic/modules/rnn.html#GRUCell"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/nn/quantized/dynamic/modules/rnn.py#L1314"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.dynamic.GRUCell" title="Permalink to this definition">¶</a></dt>
 <dd><p>A gated recurrent unit (GRU) cell</p>
 <p>A dynamic quantized GRUCell module with floating point tensor as inputs and outputs.
 Weights are quantized to 8 bits. We adopt the same interface as <cite>torch.nn.GRUCell</cite>,
diff --git a/main/generated/torch.ao.nn.quantized.dynamic.LSTM.html b/main/generated/torch.ao.nn.quantized.dynamic.LSTM.html
index e65f41be861..5aba3c27c00 100644
--- a/main/generated/torch.ao.nn.quantized.dynamic.LSTM.html
+++ b/main/generated/torch.ao.nn.quantized.dynamic.LSTM.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
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     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>LSTM<a class="headerlink" href="#lstm" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.nn.quantized.dynamic.LSTM">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.quantized.dynamic.</span></span><span class="sig-name descname"><span class="pre">LSTM</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/dynamic/modules/rnn.html#LSTM"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/nn/quantized/dynamic/modules/rnn.py#L509"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.dynamic.LSTM" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.quantized.dynamic.</span></span><span class="sig-name descname"><span class="pre">LSTM</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/dynamic/modules/rnn.html#LSTM"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/nn/quantized/dynamic/modules/rnn.py#L509"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.dynamic.LSTM" title="Permalink to this definition">¶</a></dt>
 <dd><p>A dynamic quantized LSTM module with floating point tensor as inputs and outputs.
 We adopt the same interface as <cite>torch.nn.LSTM</cite>, please see
 <a class="reference external" href="https://pytorch.org/docs/stable/nn.html#torch.nn.LSTM">https://pytorch.org/docs/stable/nn.html#torch.nn.LSTM</a> for documentation.</p>
diff --git a/main/generated/torch.ao.nn.quantized.dynamic.LSTMCell.html b/main/generated/torch.ao.nn.quantized.dynamic.LSTMCell.html
index e46861b6ae3..f198bb5d98a 100644
--- a/main/generated/torch.ao.nn.quantized.dynamic.LSTMCell.html
+++ b/main/generated/torch.ao.nn.quantized.dynamic.LSTMCell.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>LSTMCell<a class="headerlink" href="#lstmcell" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.nn.quantized.dynamic.LSTMCell">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.quantized.dynamic.</span></span><span class="sig-name descname"><span class="pre">LSTMCell</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/dynamic/modules/rnn.html#LSTMCell"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/nn/quantized/dynamic/modules/rnn.py#L1261"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.dynamic.LSTMCell" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.quantized.dynamic.</span></span><span class="sig-name descname"><span class="pre">LSTMCell</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/dynamic/modules/rnn.html#LSTMCell"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/nn/quantized/dynamic/modules/rnn.py#L1261"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.dynamic.LSTMCell" title="Permalink to this definition">¶</a></dt>
 <dd><p>A long short-term memory (LSTM) cell.</p>
 <p>A dynamic quantized LSTMCell module with floating point tensor as inputs and outputs.
 Weights are quantized to 8 bits. We adopt the same interface as <cite>torch.nn.LSTMCell</cite>,
diff --git a/main/generated/torch.ao.nn.quantized.dynamic.Linear.html b/main/generated/torch.ao.nn.quantized.dynamic.Linear.html
index aba3191bf04..59943413ffa 100644
--- a/main/generated/torch.ao.nn.quantized.dynamic.Linear.html
+++ b/main/generated/torch.ao.nn.quantized.dynamic.Linear.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>Linear<a class="headerlink" href="#linear" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.nn.quantized.dynamic.Linear">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.quantized.dynamic.</span></span><span class="sig-name descname"><span class="pre">Linear</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">in_features</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out_features</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias_</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.qint8</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/dynamic/modules/linear.html#Linear"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/nn/quantized/dynamic/modules/linear.py#L13"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.dynamic.Linear" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.quantized.dynamic.</span></span><span class="sig-name descname"><span class="pre">Linear</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">in_features</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out_features</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias_</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.qint8</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/dynamic/modules/linear.html#Linear"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/nn/quantized/dynamic/modules/linear.py#L13"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.dynamic.Linear" title="Permalink to this definition">¶</a></dt>
 <dd><p>A dynamic quantized linear module with floating point tensor as inputs and outputs.
 We adopt the same interface as <cite>torch.nn.Linear</cite>, please see
 <a class="reference external" href="https://pytorch.org/docs/stable/nn.html#torch.nn.Linear">https://pytorch.org/docs/stable/nn.html#torch.nn.Linear</a> for documentation.</p>
@@ -641,7 +641,7 @@ <h1>Linear<a class="headerlink" href="#linear" title="Permalink to this heading"
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.ao.nn.quantized.dynamic.Linear.from_float">
-<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_float</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mod</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_precomputed_fake_quant</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/dynamic/modules/linear.html#Linear.from_float"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/nn/quantized/dynamic/modules/linear.py#L99"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.dynamic.Linear.from_float" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_float</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mod</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_precomputed_fake_quant</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/dynamic/modules/linear.html#Linear.from_float"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/nn/quantized/dynamic/modules/linear.py#L99"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.dynamic.Linear.from_float" title="Permalink to this definition">¶</a></dt>
 <dd><p>Create a dynamic quantized module from a float module or qparams_dict</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -653,7 +653,7 @@ <h1>Linear<a class="headerlink" href="#linear" title="Permalink to this heading"
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.ao.nn.quantized.dynamic.Linear.from_reference">
-<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_reference</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">ref_qlinear</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/dynamic/modules/linear.html#Linear.from_reference"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/nn/quantized/dynamic/modules/linear.py#L149"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.dynamic.Linear.from_reference" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_reference</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">ref_qlinear</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/dynamic/modules/linear.html#Linear.from_reference"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/nn/quantized/dynamic/modules/linear.py#L149"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.dynamic.Linear.from_reference" title="Permalink to this definition">¶</a></dt>
 <dd><p>Create a (fbgemm/qnnpack) dynamic quantized module from a reference quantized
 module
 :param ref_qlinear: a reference quantized  module, either produced by
diff --git a/main/generated/torch.ao.nn.quantized.dynamic.RNNCell.html b/main/generated/torch.ao.nn.quantized.dynamic.RNNCell.html
index fd951abac0d..101e9bf9517 100644
--- a/main/generated/torch.ao.nn.quantized.dynamic.RNNCell.html
+++ b/main/generated/torch.ao.nn.quantized.dynamic.RNNCell.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
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@@ -614,7 +614,7 @@
 <h1>RNNCell<a class="headerlink" href="#rnncell" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.nn.quantized.dynamic.RNNCell">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.quantized.dynamic.</span></span><span class="sig-name descname"><span class="pre">RNNCell</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">hidden_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">nonlinearity</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'tanh'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.qint8</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/dynamic/modules/rnn.html#RNNCell"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/nn/quantized/dynamic/modules/rnn.py#L1196"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.dynamic.RNNCell" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.quantized.dynamic.</span></span><span class="sig-name descname"><span class="pre">RNNCell</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">hidden_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">nonlinearity</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'tanh'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.qint8</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/dynamic/modules/rnn.html#RNNCell"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/nn/quantized/dynamic/modules/rnn.py#L1196"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.dynamic.RNNCell" title="Permalink to this definition">¶</a></dt>
 <dd><p>An Elman RNN cell with tanh or ReLU non-linearity.
 A dynamic quantized RNNCell module with floating point tensor as inputs and outputs.
 Weights are quantized to 8 bits. We adopt the same interface as <cite>torch.nn.RNNCell</cite>,
diff --git a/main/generated/torch.ao.nn.quantized.functional.adaptive_avg_pool2d.html b/main/generated/torch.ao.nn.quantized.functional.adaptive_avg_pool2d.html
index 72861f25494..14ad8071c82 100644
--- a/main/generated/torch.ao.nn.quantized.functional.adaptive_avg_pool2d.html
+++ b/main/generated/torch.ao.nn.quantized.functional.adaptive_avg_pool2d.html
@@ -300,7 +300,7 @@
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@@ -614,7 +614,7 @@
 <h1>adaptive_avg_pool2d<a class="headerlink" href="#adaptive-avg-pool2d" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.nn.quantized.functional.adaptive_avg_pool2d">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.quantized.functional.</span></span><span class="sig-name descname"><span class="pre">adaptive_avg_pool2d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_size</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/functional.html#adaptive_avg_pool2d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/nn/quantized/functional.py#L133"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.functional.adaptive_avg_pool2d" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.quantized.functional.</span></span><span class="sig-name descname"><span class="pre">adaptive_avg_pool2d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_size</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/functional.html#adaptive_avg_pool2d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/nn/quantized/functional.py#L133"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.functional.adaptive_avg_pool2d" title="Permalink to this definition">¶</a></dt>
 <dd><p>Applies a 2D adaptive average pooling over a quantized input signal composed
 of several quantized input planes.</p>
 <div class="admonition note">
diff --git a/main/generated/torch.ao.nn.quantized.functional.adaptive_avg_pool3d.html b/main/generated/torch.ao.nn.quantized.functional.adaptive_avg_pool3d.html
index d9e9afb349c..e5f0b1b7e5c 100644
--- a/main/generated/torch.ao.nn.quantized.functional.adaptive_avg_pool3d.html
+++ b/main/generated/torch.ao.nn.quantized.functional.adaptive_avg_pool3d.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
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     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>adaptive_avg_pool3d<a class="headerlink" href="#adaptive-avg-pool3d" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.nn.quantized.functional.adaptive_avg_pool3d">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.quantized.functional.</span></span><span class="sig-name descname"><span class="pre">adaptive_avg_pool3d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_size</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/functional.html#adaptive_avg_pool3d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/nn/quantized/functional.py#L153"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.functional.adaptive_avg_pool3d" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.quantized.functional.</span></span><span class="sig-name descname"><span class="pre">adaptive_avg_pool3d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_size</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/functional.html#adaptive_avg_pool3d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/nn/quantized/functional.py#L153"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.functional.adaptive_avg_pool3d" title="Permalink to this definition">¶</a></dt>
 <dd><p>Applies a 3D adaptive average pooling over a quantized input signal composed
 of several quantized input planes.</p>
 <div class="admonition note">
diff --git a/main/generated/torch.ao.nn.quantized.functional.avg_pool2d.html b/main/generated/torch.ao.nn.quantized.functional.avg_pool2d.html
index ef5be25d7c1..426acc663b9 100644
--- a/main/generated/torch.ao.nn.quantized.functional.avg_pool2d.html
+++ b/main/generated/torch.ao.nn.quantized.functional.avg_pool2d.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>avg_pool2d<a class="headerlink" href="#avg-pool2d" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.nn.quantized.functional.avg_pool2d">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.quantized.functional.</span></span><span class="sig-name descname"><span class="pre">avg_pool2d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ceil_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">count_include_pad</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">divisor_override</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/functional.html#avg_pool2d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/nn/quantized/functional.py#L43"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.functional.avg_pool2d" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.quantized.functional.</span></span><span class="sig-name descname"><span class="pre">avg_pool2d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ceil_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">count_include_pad</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">divisor_override</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/functional.html#avg_pool2d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/nn/quantized/functional.py#L43"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.functional.avg_pool2d" title="Permalink to this definition">¶</a></dt>
 <dd><p>Applies 2D average-pooling operation in <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>k</mi><mi>H</mi><mo>×</mo><mi>k</mi><mi>W</mi></mrow><annotation encoding="application/x-tex">kH \times kW</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.7778em;vertical-align:-0.0833em;"></span><span class="mord mathnormal" style="margin-right:0.03148em;">k</span><span class="mord mathnormal" style="margin-right:0.08125em;">H</span><span class="mspace" style="margin-right:0.2222em;"></span><span class="mbin">×</span><span class="mspace" style="margin-right:0.2222em;"></span></span><span class="base"><span class="strut" style="height:0.6944em;"></span><span class="mord mathnormal" style="margin-right:0.13889em;">kW</span></span></span></span></span> regions by step size
 <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>s</mi><mi>H</mi><mo>×</mo><mi>s</mi><mi>W</mi></mrow><annotation encoding="application/x-tex">sH \times sW</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.7667em;vertical-align:-0.0833em;"></span><span class="mord mathnormal" style="margin-right:0.08125em;">sH</span><span class="mspace" style="margin-right:0.2222em;"></span><span class="mbin">×</span><span class="mspace" style="margin-right:0.2222em;"></span></span><span class="base"><span class="strut" style="height:0.6833em;"></span><span class="mord mathnormal">s</span><span class="mord mathnormal" style="margin-right:0.13889em;">W</span></span></span></span></span> steps. The number of output features is equal to the number of
 input planes.</p>
diff --git a/main/generated/torch.ao.nn.quantized.functional.avg_pool3d.html b/main/generated/torch.ao.nn.quantized.functional.avg_pool3d.html
index 7d98c52f033..580f0693037 100644
--- a/main/generated/torch.ao.nn.quantized.functional.avg_pool3d.html
+++ b/main/generated/torch.ao.nn.quantized.functional.avg_pool3d.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
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     <div id="searchBox">
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@@ -614,7 +614,7 @@
 <h1>avg_pool3d<a class="headerlink" href="#avg-pool3d" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.nn.quantized.functional.avg_pool3d">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.quantized.functional.</span></span><span class="sig-name descname"><span class="pre">avg_pool3d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ceil_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">count_include_pad</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">divisor_override</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/functional.html#avg_pool3d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/nn/quantized/functional.py#L89"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.functional.avg_pool3d" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.quantized.functional.</span></span><span class="sig-name descname"><span class="pre">avg_pool3d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ceil_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">count_include_pad</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">divisor_override</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/functional.html#avg_pool3d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/nn/quantized/functional.py#L89"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.functional.avg_pool3d" title="Permalink to this definition">¶</a></dt>
 <dd><p>Applies 3D average-pooling operation in <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>k</mi><mi>D</mi><mtext> </mtext><mi>t</mi><mi>i</mi><mi>m</mi><mi>e</mi><mi>s</mi><mi>k</mi><mi>H</mi><mo>×</mo><mi>k</mi><mi>W</mi></mrow><annotation encoding="application/x-tex">kD \ times kH \times kW</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.7778em;vertical-align:-0.0833em;"></span><span class="mord mathnormal" style="margin-right:0.03148em;">k</span><span class="mord mathnormal" style="margin-right:0.02778em;">D</span><span class="mspace"> </span><span class="mord mathnormal">t</span><span class="mord mathnormal">im</span><span class="mord mathnormal">es</span><span class="mord mathnormal" style="margin-right:0.03148em;">k</span><span class="mord mathnormal" style="margin-right:0.08125em;">H</span><span class="mspace" style="margin-right:0.2222em;"></span><span class="mbin">×</span><span class="mspace" style="margin-right:0.2222em;"></span></span><span class="base"><span class="strut" style="height:0.6944em;"></span><span class="mord mathnormal" style="margin-right:0.13889em;">kW</span></span></span></span></span> regions by step size
 <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>s</mi><mi>D</mi><mo>×</mo><mi>s</mi><mi>H</mi><mo>×</mo><mi>s</mi><mi>W</mi></mrow><annotation encoding="application/x-tex">sD \times sH \times sW</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.7667em;vertical-align:-0.0833em;"></span><span class="mord mathnormal" style="margin-right:0.02778em;">sD</span><span class="mspace" style="margin-right:0.2222em;"></span><span class="mbin">×</span><span class="mspace" style="margin-right:0.2222em;"></span></span><span class="base"><span class="strut" style="height:0.7667em;vertical-align:-0.0833em;"></span><span class="mord mathnormal" style="margin-right:0.08125em;">sH</span><span class="mspace" style="margin-right:0.2222em;"></span><span class="mbin">×</span><span class="mspace" style="margin-right:0.2222em;"></span></span><span class="base"><span class="strut" style="height:0.6833em;"></span><span class="mord mathnormal">s</span><span class="mord mathnormal" style="margin-right:0.13889em;">W</span></span></span></span></span> steps. The number of output features is equal to the number of
 input planes.</p>
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     </div>
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@@ -614,7 +614,7 @@
 <h1>celu<a class="headerlink" href="#celu" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.nn.quantized.functional.celu">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.quantized.functional.</span></span><span class="sig-name descname"><span class="pre">celu</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">zero_point</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/functional.html#celu"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/nn/quantized/functional.py#L539"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.functional.celu" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.quantized.functional.</span></span><span class="sig-name descname"><span class="pre">celu</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">zero_point</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/functional.html#celu"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/nn/quantized/functional.py#L539"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.functional.celu" title="Permalink to this definition">¶</a></dt>
 <dd><p>Applies the quantized CELU function element-wise.</p>
 <div class="math">
 <span class="katex-display"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML" display="block"><semantics><mrow><mtext>CELU</mtext><mo stretchy="false">(</mo><mi>x</mi><mo stretchy="false">)</mo><mo>=</mo><mi>max</mi><mo>⁡</mo><mo stretchy="false">(</mo><mn>0</mn><mo separator="true">,</mo><mi>x</mi><mo stretchy="false">)</mo><mo>+</mo><mi>min</mi><mo>⁡</mo><mo stretchy="false">(</mo><mn>0</mn><mo separator="true">,</mo><mi>α</mi><mo>∗</mo><mo stretchy="false">(</mo><mi>exp</mi><mo>⁡</mo><mo stretchy="false">(</mo><mi>x</mi><mi mathvariant="normal">/</mi><mi>α</mi><mo stretchy="false">)</mo><mo>−</mo><mn>1</mn><mo stretchy="false">)</mo><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">\text{CELU}(x) = \max(0,x) + \min(0, \alpha * (\exp(x / \alpha) - 1))
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     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>clamp<a class="headerlink" href="#clamp" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.nn.quantized.functional.clamp">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.quantized.functional.</span></span><span class="sig-name descname"><span class="pre">clamp</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/functional.html#clamp"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/nn/quantized/functional.py#L659"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.functional.clamp" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.quantized.functional.</span></span><span class="sig-name descname"><span class="pre">clamp</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/functional.html#clamp"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/nn/quantized/functional.py#L659"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.functional.clamp" title="Permalink to this definition">¶</a></dt>
 <dd><p>float(input, min_, max_) -&gt; Tensor</p>
 <p>Applies the clamp function element-wise.
 See <code class="xref py py-class docutils literal notranslate"><span class="pre">clamp</span></code> for more details.</p>
diff --git a/main/generated/torch.ao.nn.quantized.functional.conv1d.html b/main/generated/torch.ao.nn.quantized.functional.conv1d.html
index 5798dbf4ab8..8ec4318f666 100644
--- a/main/generated/torch.ao.nn.quantized.functional.conv1d.html
+++ b/main/generated/torch.ao.nn.quantized.functional.conv1d.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
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     </div>
     <div id="searchBox">
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@@ -614,7 +614,7 @@
 <h1>conv1d<a class="headerlink" href="#conv1d" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.nn.quantized.functional.conv1d">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.quantized.functional.</span></span><span class="sig-name descname"><span class="pre">conv1d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weight</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dilation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'zeros'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">zero_point</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.quint8</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/functional.html#conv1d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/nn/quantized/functional.py#L173"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.functional.conv1d" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.quantized.functional.</span></span><span class="sig-name descname"><span class="pre">conv1d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weight</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dilation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'zeros'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">zero_point</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.quint8</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/functional.html#conv1d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/nn/quantized/functional.py#L173"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.functional.conv1d" title="Permalink to this definition">¶</a></dt>
 <dd><p>Applies a 1D convolution over a quantized 1D input composed of several input
 planes.</p>
 <p>See <a class="reference internal" href="torch.ao.nn.quantized.Conv1d.html#torch.ao.nn.quantized.Conv1d" title="torch.ao.nn.quantized.Conv1d"><code class="xref py py-class docutils literal notranslate"><span class="pre">Conv1d</span></code></a> for details and output shape.</p>
diff --git a/main/generated/torch.ao.nn.quantized.functional.conv2d.html b/main/generated/torch.ao.nn.quantized.functional.conv2d.html
index 385a06d4e51..9d0ff79ee22 100644
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+++ b/main/generated/torch.ao.nn.quantized.functional.conv2d.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>conv2d<a class="headerlink" href="#conv2d" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.nn.quantized.functional.conv2d">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.quantized.functional.</span></span><span class="sig-name descname"><span class="pre">conv2d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weight</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dilation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'zeros'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">zero_point</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.quint8</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/functional.html#conv2d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/nn/quantized/functional.py#L245"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.functional.conv2d" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.quantized.functional.</span></span><span class="sig-name descname"><span class="pre">conv2d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weight</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dilation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'zeros'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">zero_point</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.quint8</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/functional.html#conv2d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/nn/quantized/functional.py#L245"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.functional.conv2d" title="Permalink to this definition">¶</a></dt>
 <dd><p>Applies a 2D convolution over a quantized 2D input composed of several input
 planes.</p>
 <p>See <a class="reference internal" href="torch.ao.nn.quantized.Conv2d.html#torch.ao.nn.quantized.Conv2d" title="torch.ao.nn.quantized.Conv2d"><code class="xref py py-class docutils literal notranslate"><span class="pre">Conv2d</span></code></a> for details and output shape.</p>
diff --git a/main/generated/torch.ao.nn.quantized.functional.conv3d.html b/main/generated/torch.ao.nn.quantized.functional.conv3d.html
index bff5b7fd3aa..5c1a16264f9 100644
--- a/main/generated/torch.ao.nn.quantized.functional.conv3d.html
+++ b/main/generated/torch.ao.nn.quantized.functional.conv3d.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
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+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>conv3d<a class="headerlink" href="#conv3d" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.nn.quantized.functional.conv3d">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.quantized.functional.</span></span><span class="sig-name descname"><span class="pre">conv3d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weight</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dilation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'zeros'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">zero_point</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.quint8</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/functional.html#conv3d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/nn/quantized/functional.py#L317"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.functional.conv3d" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.quantized.functional.</span></span><span class="sig-name descname"><span class="pre">conv3d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weight</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dilation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'zeros'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">zero_point</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.quint8</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/functional.html#conv3d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/nn/quantized/functional.py#L317"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.functional.conv3d" title="Permalink to this definition">¶</a></dt>
 <dd><p>Applies a 3D convolution over a quantized 3D input composed of several input
 planes.</p>
 <p>See <a class="reference internal" href="torch.ao.nn.quantized.Conv3d.html#torch.ao.nn.quantized.Conv3d" title="torch.ao.nn.quantized.Conv3d"><code class="xref py py-class docutils literal notranslate"><span class="pre">Conv3d</span></code></a> for details and output shape.</p>
diff --git a/main/generated/torch.ao.nn.quantized.functional.elu.html b/main/generated/torch.ao.nn.quantized.functional.elu.html
index 6582f1c3f04..bb2b13c559c 100644
--- a/main/generated/torch.ao.nn.quantized.functional.elu.html
+++ b/main/generated/torch.ao.nn.quantized.functional.elu.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>elu<a class="headerlink" href="#elu" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.nn.quantized.functional.elu">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.quantized.functional.</span></span><span class="sig-name descname"><span class="pre">elu</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">zero_point</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/functional.html#elu"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/nn/quantized/functional.py#L636"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.functional.elu" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.quantized.functional.</span></span><span class="sig-name descname"><span class="pre">elu</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">zero_point</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/functional.html#elu"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/nn/quantized/functional.py#L636"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.functional.elu" title="Permalink to this definition">¶</a></dt>
 <dd><p>This is the quantized version of <a class="reference internal" href="torch.nn.functional.elu.html#torch.nn.functional.elu" title="torch.nn.functional.elu"><code class="xref py py-func docutils literal notranslate"><span class="pre">elu()</span></code></a>.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.ao.nn.quantized.functional.hardsigmoid.html b/main/generated/torch.ao.nn.quantized.functional.hardsigmoid.html
index 377c002c99c..b8348c39b46 100644
--- a/main/generated/torch.ao.nn.quantized.functional.hardsigmoid.html
+++ b/main/generated/torch.ao.nn.quantized.functional.hardsigmoid.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>hardsigmoid<a class="headerlink" href="#hardsigmoid" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.nn.quantized.functional.hardsigmoid">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.quantized.functional.</span></span><span class="sig-name descname"><span class="pre">hardsigmoid</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/functional.html#hardsigmoid"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/nn/quantized/functional.py#L650"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.functional.hardsigmoid" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.quantized.functional.</span></span><span class="sig-name descname"><span class="pre">hardsigmoid</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/functional.html#hardsigmoid"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/nn/quantized/functional.py#L650"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.functional.hardsigmoid" title="Permalink to this definition">¶</a></dt>
 <dd><p>This is the quantized version of <a class="reference internal" href="torch.nn.functional.hardsigmoid.html#torch.nn.functional.hardsigmoid" title="torch.nn.functional.hardsigmoid"><code class="xref py py-func docutils literal notranslate"><span class="pre">hardsigmoid()</span></code></a>.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
diff --git a/main/generated/torch.ao.nn.quantized.functional.hardswish.html b/main/generated/torch.ao.nn.quantized.functional.hardswish.html
index c9f202afeb4..552f0a6b7ee 100644
--- a/main/generated/torch.ao.nn.quantized.functional.hardswish.html
+++ b/main/generated/torch.ao.nn.quantized.functional.hardswish.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>hardswish<a class="headerlink" href="#hardswish" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.nn.quantized.functional.hardswish">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.quantized.functional.</span></span><span class="sig-name descname"><span class="pre">hardswish</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">zero_point</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/functional.html#hardswish"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/nn/quantized/functional.py#L603"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.functional.hardswish" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.quantized.functional.</span></span><span class="sig-name descname"><span class="pre">hardswish</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">zero_point</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/functional.html#hardswish"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/nn/quantized/functional.py#L603"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.functional.hardswish" title="Permalink to this definition">¶</a></dt>
 <dd><p>This is the quantized version of <a class="reference internal" href="torch.nn.functional.hardswish.html#torch.nn.functional.hardswish" title="torch.nn.functional.hardswish"><code class="xref py py-func docutils literal notranslate"><span class="pre">hardswish()</span></code></a>.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.ao.nn.quantized.functional.hardtanh.html b/main/generated/torch.ao.nn.quantized.functional.hardtanh.html
index e303e1a414e..7d78599cbe4 100644
--- a/main/generated/torch.ao.nn.quantized.functional.hardtanh.html
+++ b/main/generated/torch.ao.nn.quantized.functional.hardtanh.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
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@@ -614,7 +614,7 @@
 <h1>hardtanh<a class="headerlink" href="#hardtanh" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.nn.quantized.functional.hardtanh">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.quantized.functional.</span></span><span class="sig-name descname"><span class="pre">hardtanh</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_val</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_val</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/functional.html#hardtanh"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/nn/quantized/functional.py#L592"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.functional.hardtanh" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.quantized.functional.</span></span><span class="sig-name descname"><span class="pre">hardtanh</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_val</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_val</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/functional.html#hardtanh"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/nn/quantized/functional.py#L592"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.functional.hardtanh" title="Permalink to this definition">¶</a></dt>
 <dd><p>This is the quantized version of <a class="reference internal" href="torch.nn.functional.hardtanh.html#torch.nn.functional.hardtanh" title="torch.nn.functional.hardtanh"><code class="xref py py-func docutils literal notranslate"><span class="pre">hardtanh()</span></code></a>.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
diff --git a/main/generated/torch.ao.nn.quantized.functional.interpolate.html b/main/generated/torch.ao.nn.quantized.functional.interpolate.html
index a2b8a781361..a4532775451 100644
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           <div class="pytorch-left-menu-search">
             
     <div class="version">
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     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>interpolate<a class="headerlink" href="#interpolate" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.nn.quantized.functional.interpolate">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.quantized.functional.</span></span><span class="sig-name descname"><span class="pre">interpolate</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale_factor</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'nearest'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">align_corners</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/functional.html#interpolate"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/nn/quantized/functional.py#L393"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.functional.interpolate" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.quantized.functional.</span></span><span class="sig-name descname"><span class="pre">interpolate</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale_factor</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'nearest'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">align_corners</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/functional.html#interpolate"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/nn/quantized/functional.py#L393"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.functional.interpolate" title="Permalink to this definition">¶</a></dt>
 <dd><p>Down/up samples the input to either the given <code class="xref py py-attr docutils literal notranslate"><span class="pre">size</span></code> or the given
 <code class="xref py py-attr docutils literal notranslate"><span class="pre">scale_factor</span></code></p>
 <p>See <a class="reference internal" href="torch.nn.functional.interpolate.html#torch.nn.functional.interpolate" title="torch.nn.functional.interpolate"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.nn.functional.interpolate()</span></code></a> for implementation details.</p>
diff --git a/main/generated/torch.ao.nn.quantized.functional.leaky_relu.html b/main/generated/torch.ao.nn.quantized.functional.leaky_relu.html
index ee7e26f3724..0b8204562d2 100644
--- a/main/generated/torch.ao.nn.quantized.functional.leaky_relu.html
+++ b/main/generated/torch.ao.nn.quantized.functional.leaky_relu.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
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     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>leaky_relu<a class="headerlink" href="#leaky-relu" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.nn.quantized.functional.leaky_relu">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.quantized.functional.</span></span><span class="sig-name descname"><span class="pre">leaky_relu</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">negative_slope</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.01</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">zero_point</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/functional.html#leaky_relu"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/nn/quantized/functional.py#L556"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.functional.leaky_relu" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.quantized.functional.</span></span><span class="sig-name descname"><span class="pre">leaky_relu</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">negative_slope</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.01</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">zero_point</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/functional.html#leaky_relu"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/nn/quantized/functional.py#L556"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.functional.leaky_relu" title="Permalink to this definition">¶</a></dt>
 <dd><p>Quantized version of the.
 leaky_relu(input, negative_slope=0.01, inplace=False, scale, zero_point) -&gt; Tensor</p>
 <p>Applies element-wise,
diff --git a/main/generated/torch.ao.nn.quantized.functional.linear.html b/main/generated/torch.ao.nn.quantized.functional.linear.html
index fe87d75ffd0..6d9b1d3ce13 100644
--- a/main/generated/torch.ao.nn.quantized.functional.linear.html
+++ b/main/generated/torch.ao.nn.quantized.functional.linear.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>linear<a class="headerlink" href="#linear" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.nn.quantized.functional.linear">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.quantized.functional.</span></span><span class="sig-name descname"><span class="pre">linear</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weight</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">zero_point</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/functional.html#linear"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/nn/quantized/functional.py#L438"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.functional.linear" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.quantized.functional.</span></span><span class="sig-name descname"><span class="pre">linear</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weight</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">zero_point</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/functional.html#linear"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/nn/quantized/functional.py#L438"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.functional.linear" title="Permalink to this definition">¶</a></dt>
 <dd><p>Applies a linear transformation to the incoming quantized data:
 <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>y</mi><mo>=</mo><mi>x</mi><msup><mi>A</mi><mi>T</mi></msup><mo>+</mo><mi>b</mi></mrow><annotation encoding="application/x-tex">y = xA^T + b</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.625em;vertical-align:-0.1944em;"></span><span class="mord mathnormal" style="margin-right:0.03588em;">y</span><span class="mspace" style="margin-right:0.2778em;"></span><span class="mrel">=</span><span class="mspace" style="margin-right:0.2778em;"></span></span><span class="base"><span class="strut" style="height:0.9247em;vertical-align:-0.0833em;"></span><span class="mord mathnormal">x</span><span class="mord"><span class="mord mathnormal">A</span><span class="msupsub"><span class="vlist-t"><span class="vlist-r"><span class="vlist" style="height:0.8413em;"><span style="top:-3.063em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mathnormal mtight" style="margin-right:0.13889em;">T</span></span></span></span></span></span></span></span><span class="mspace" style="margin-right:0.2222em;"></span><span class="mbin">+</span><span class="mspace" style="margin-right:0.2222em;"></span></span><span class="base"><span class="strut" style="height:0.6944em;"></span><span class="mord mathnormal">b</span></span></span></span></span>.
 See <a class="reference internal" href="torch.ao.nn.quantized.Linear.html#torch.ao.nn.quantized.Linear" title="torch.ao.nn.quantized.Linear"><code class="xref py py-class docutils literal notranslate"><span class="pre">Linear</span></code></a></p>
diff --git a/main/generated/torch.ao.nn.quantized.functional.max_pool1d.html b/main/generated/torch.ao.nn.quantized.functional.max_pool1d.html
index a978ba50248..23add2fd7d5 100644
--- a/main/generated/torch.ao.nn.quantized.functional.max_pool1d.html
+++ b/main/generated/torch.ao.nn.quantized.functional.max_pool1d.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>max_pool1d<a class="headerlink" href="#max-pool1d" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.nn.quantized.functional.max_pool1d">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.quantized.functional.</span></span><span class="sig-name descname"><span class="pre">max_pool1d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dilation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ceil_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_indices</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/functional.html#max_pool1d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/nn/quantized/functional.py#L477"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.functional.max_pool1d" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.quantized.functional.</span></span><span class="sig-name descname"><span class="pre">max_pool1d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dilation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ceil_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_indices</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/functional.html#max_pool1d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/nn/quantized/functional.py#L477"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.functional.max_pool1d" title="Permalink to this definition">¶</a></dt>
 <dd><p>Applies a 1D max pooling over a quantized input signal composed of
 several quantized input planes.</p>
 <div class="admonition note">
diff --git a/main/generated/torch.ao.nn.quantized.functional.max_pool2d.html b/main/generated/torch.ao.nn.quantized.functional.max_pool2d.html
index 29dea11f3b4..b7878fa6597 100644
--- a/main/generated/torch.ao.nn.quantized.functional.max_pool2d.html
+++ b/main/generated/torch.ao.nn.quantized.functional.max_pool2d.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
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     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>max_pool2d<a class="headerlink" href="#max-pool2d" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.nn.quantized.functional.max_pool2d">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.quantized.functional.</span></span><span class="sig-name descname"><span class="pre">max_pool2d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dilation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ceil_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_indices</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/functional.html#max_pool2d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/nn/quantized/functional.py#L508"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.functional.max_pool2d" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.quantized.functional.</span></span><span class="sig-name descname"><span class="pre">max_pool2d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dilation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ceil_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_indices</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/functional.html#max_pool2d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/nn/quantized/functional.py#L508"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.functional.max_pool2d" title="Permalink to this definition">¶</a></dt>
 <dd><p>Applies a 2D max pooling over a quantized input signal composed of
 several quantized input planes.</p>
 <div class="admonition note">
diff --git a/main/generated/torch.ao.nn.quantized.functional.threshold.html b/main/generated/torch.ao.nn.quantized.functional.threshold.html
index 939322a945a..a5d1a4118c2 100644
--- a/main/generated/torch.ao.nn.quantized.functional.threshold.html
+++ b/main/generated/torch.ao.nn.quantized.functional.threshold.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
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@@ -614,7 +614,7 @@
 <h1>threshold<a class="headerlink" href="#threshold" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.nn.quantized.functional.threshold">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.quantized.functional.</span></span><span class="sig-name descname"><span class="pre">threshold</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">threshold</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/functional.html#threshold"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/nn/quantized/functional.py#L616"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.functional.threshold" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.quantized.functional.</span></span><span class="sig-name descname"><span class="pre">threshold</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">threshold</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/functional.html#threshold"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/nn/quantized/functional.py#L616"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.functional.threshold" title="Permalink to this definition">¶</a></dt>
 <dd><p>Applies the quantized version of the threshold function element-wise:</p>
 <div class="math">
 <span class="katex-display"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML" display="block"><semantics><mrow><mi>x</mi><mo>=</mo><mrow><mo fence="true">{</mo><mtable rowspacing="0.36em" columnalign="left left" columnspacing="1em"><mtr><mtd><mstyle scriptlevel="0" displaystyle="false"><mi>x</mi></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="false"><mrow><mtext>if </mtext><mi>x</mi><mo>&gt;</mo><mtext>threshold</mtext></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="false"><mtext>value</mtext></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="false"><mtext>otherwise</mtext></mstyle></mtd></mtr></mtable></mrow></mrow><annotation encoding="application/x-tex">x = \begin{cases}
diff --git a/main/generated/torch.ao.nn.quantized.functional.upsample.html b/main/generated/torch.ao.nn.quantized.functional.upsample.html
index 64e00e8a78a..24a6173f7c4 100644
--- a/main/generated/torch.ao.nn.quantized.functional.upsample.html
+++ b/main/generated/torch.ao.nn.quantized.functional.upsample.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>upsample<a class="headerlink" href="#upsample" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.nn.quantized.functional.upsample">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.quantized.functional.</span></span><span class="sig-name descname"><span class="pre">upsample</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale_factor</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'nearest'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">align_corners</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/functional.html#upsample"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/nn/quantized/functional.py#L675"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.functional.upsample" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.quantized.functional.</span></span><span class="sig-name descname"><span class="pre">upsample</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale_factor</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'nearest'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">align_corners</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/functional.html#upsample"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/nn/quantized/functional.py#L675"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.functional.upsample" title="Permalink to this definition">¶</a></dt>
 <dd><p>Upsamples the input to either the given <code class="xref py py-attr docutils literal notranslate"><span class="pre">size</span></code> or the given
 <code class="xref py py-attr docutils literal notranslate"><span class="pre">scale_factor</span></code></p>
 <div class="admonition warning">
diff --git a/main/generated/torch.ao.nn.quantized.functional.upsample_bilinear.html b/main/generated/torch.ao.nn.quantized.functional.upsample_bilinear.html
index c45ca9742e6..8cbaaf49833 100644
--- a/main/generated/torch.ao.nn.quantized.functional.upsample_bilinear.html
+++ b/main/generated/torch.ao.nn.quantized.functional.upsample_bilinear.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
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@@ -614,7 +614,7 @@
 <h1>upsample_bilinear<a class="headerlink" href="#upsample-bilinear" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.nn.quantized.functional.upsample_bilinear">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.quantized.functional.</span></span><span class="sig-name descname"><span class="pre">upsample_bilinear</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale_factor</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/functional.html#upsample_bilinear"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/nn/quantized/functional.py#L731"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.functional.upsample_bilinear" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.quantized.functional.</span></span><span class="sig-name descname"><span class="pre">upsample_bilinear</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale_factor</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/functional.html#upsample_bilinear"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/nn/quantized/functional.py#L731"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.functional.upsample_bilinear" title="Permalink to this definition">¶</a></dt>
 <dd><p>Upsamples the input, using bilinear upsampling.</p>
 <div class="admonition warning">
 <p class="admonition-title">Warning</p>
diff --git a/main/generated/torch.ao.nn.quantized.functional.upsample_nearest.html b/main/generated/torch.ao.nn.quantized.functional.upsample_nearest.html
index a6c2d980f94..05abed4ff24 100644
--- a/main/generated/torch.ao.nn.quantized.functional.upsample_nearest.html
+++ b/main/generated/torch.ao.nn.quantized.functional.upsample_nearest.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
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@@ -614,7 +614,7 @@
 <h1>upsample_nearest<a class="headerlink" href="#upsample-nearest" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.nn.quantized.functional.upsample_nearest">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.quantized.functional.</span></span><span class="sig-name descname"><span class="pre">upsample_nearest</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale_factor</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/functional.html#upsample_nearest"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/nn/quantized/functional.py#L756"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.functional.upsample_nearest" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.nn.quantized.functional.</span></span><span class="sig-name descname"><span class="pre">upsample_nearest</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale_factor</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/nn/quantized/functional.html#upsample_nearest"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/nn/quantized/functional.py#L756"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.nn.quantized.functional.upsample_nearest" title="Permalink to this definition">¶</a></dt>
 <dd><p>Upsamples the input, using nearest neighbours’ pixel values.</p>
 <div class="admonition warning">
 <p class="admonition-title">Warning</p>
diff --git a/main/generated/torch.ao.quantization.CUSTOM_KEY.html b/main/generated/torch.ao.quantization.CUSTOM_KEY.html
index f5c6a9251c3..38860c9f794 100644
--- a/main/generated/torch.ao.quantization.CUSTOM_KEY.html
+++ b/main/generated/torch.ao.quantization.CUSTOM_KEY.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.ao.quantization.DeQuantStub.html b/main/generated/torch.ao.quantization.DeQuantStub.html
index ab4d994f46a..54c0bef7d0d 100644
--- a/main/generated/torch.ao.quantization.DeQuantStub.html
+++ b/main/generated/torch.ao.quantization.DeQuantStub.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>DeQuantStub<a class="headerlink" href="#dequantstub" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.quantization.DeQuantStub">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.</span></span><span class="sig-name descname"><span class="pre">DeQuantStub</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">qconfig</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/stubs.html#DeQuantStub"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/stubs.py#L24"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.DeQuantStub" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.</span></span><span class="sig-name descname"><span class="pre">DeQuantStub</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">qconfig</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/stubs.html#DeQuantStub"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/quantization/stubs.py#L24"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.DeQuantStub" title="Permalink to this definition">¶</a></dt>
 <dd><p>Dequantize stub module, before calibration, this is same as identity,
 this will be swapped as <cite>nnq.DeQuantize</cite> in <cite>convert</cite>.</p>
 <dl class="field-list simple">
diff --git a/main/generated/torch.ao.quantization.NUMERIC_DEBUG_HANDLE_KEY.html b/main/generated/torch.ao.quantization.NUMERIC_DEBUG_HANDLE_KEY.html
index 98d6e0d3362..9a256d7cc14 100644
--- a/main/generated/torch.ao.quantization.NUMERIC_DEBUG_HANDLE_KEY.html
+++ b/main/generated/torch.ao.quantization.NUMERIC_DEBUG_HANDLE_KEY.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.ao.quantization.QuantStub.html b/main/generated/torch.ao.quantization.QuantStub.html
index 7de202bde04..f12946d031b 100644
--- a/main/generated/torch.ao.quantization.QuantStub.html
+++ b/main/generated/torch.ao.quantization.QuantStub.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>QuantStub<a class="headerlink" href="#quantstub" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.quantization.QuantStub">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.</span></span><span class="sig-name descname"><span class="pre">QuantStub</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">qconfig</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/stubs.html#QuantStub"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/stubs.py#L6"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.QuantStub" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.</span></span><span class="sig-name descname"><span class="pre">QuantStub</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">qconfig</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/stubs.html#QuantStub"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/quantization/stubs.py#L6"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.QuantStub" title="Permalink to this definition">¶</a></dt>
 <dd><p>Quantize stub module, before calibration, this is same as an observer,
 it will be swapped as <cite>nnq.Quantize</cite> in <cite>convert</cite>.</p>
 <dl class="field-list simple">
diff --git a/main/generated/torch.ao.quantization.QuantWrapper.html b/main/generated/torch.ao.quantization.QuantWrapper.html
index 48dd5681aea..59671f46f1a 100644
--- a/main/generated/torch.ao.quantization.QuantWrapper.html
+++ b/main/generated/torch.ao.quantization.QuantWrapper.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>QuantWrapper<a class="headerlink" href="#quantwrapper" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.quantization.QuantWrapper">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.</span></span><span class="sig-name descname"><span class="pre">QuantWrapper</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/stubs.html#QuantWrapper"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/stubs.py#L42"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.QuantWrapper" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.</span></span><span class="sig-name descname"><span class="pre">QuantWrapper</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/stubs.html#QuantWrapper"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/quantization/stubs.py#L42"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.QuantWrapper" title="Permalink to this definition">¶</a></dt>
 <dd><p>A wrapper class that wraps the input module, adds QuantStub and
 DeQuantStub and surround the call to module with call to quant and dequant
 modules.</p>
diff --git a/main/generated/torch.ao.quantization.add_quant_dequant.html b/main/generated/torch.ao.quantization.add_quant_dequant.html
index 0b84e94e06f..ed14b7af159 100644
--- a/main/generated/torch.ao.quantization.add_quant_dequant.html
+++ b/main/generated/torch.ao.quantization.add_quant_dequant.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>add_quant_dequant<a class="headerlink" href="#add-quant-dequant" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.quantization.add_quant_dequant">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.</span></span><span class="sig-name descname"><span class="pre">add_quant_dequant</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/quantize.html#add_quant_dequant"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/quantize.py#L308"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.add_quant_dequant" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.</span></span><span class="sig-name descname"><span class="pre">add_quant_dequant</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/quantize.html#add_quant_dequant"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/quantization/quantize.py#L308"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.add_quant_dequant" title="Permalink to this definition">¶</a></dt>
 <dd><p>Wrap the leaf child module in QuantWrapper if it has a valid qconfig
 Note that this function will modify the children of module inplace and it
 can return a new module which wraps the input module as well.</p>
diff --git a/main/generated/torch.ao.quantization.backend_config.BackendConfig.html b/main/generated/torch.ao.quantization.backend_config.BackendConfig.html
index 4bbf9e9ddcf..66fd73db98e 100644
--- a/main/generated/torch.ao.quantization.backend_config.BackendConfig.html
+++ b/main/generated/torch.ao.quantization.backend_config.BackendConfig.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>BackendConfig<a class="headerlink" href="#backendconfig" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.quantization.backend_config.BackendConfig">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.backend_config.</span></span><span class="sig-name descname"><span class="pre">BackendConfig</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/backend_config/backend_config.html#BackendConfig"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/backend_config/backend_config.py#L289"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.backend_config.BackendConfig" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.backend_config.</span></span><span class="sig-name descname"><span class="pre">BackendConfig</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/backend_config/backend_config.html#BackendConfig"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/quantization/backend_config/backend_config.py#L289"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.backend_config.BackendConfig" title="Permalink to this definition">¶</a></dt>
 <dd><p>Config that defines the set of patterns that can be quantized on a given backend, and how reference
 quantized models can be produced from these patterns.</p>
 <p>A pattern in this context refers to a module, a functional, an operator, or a directed acyclic graph
@@ -667,7 +667,7 @@ <h1>BackendConfig<a class="headerlink" href="#backendconfig" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.ao.quantization.backend_config.BackendConfig.from_dict">
-<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">backend_config_dict</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/backend_config/backend_config.html#BackendConfig.from_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/backend_config/backend_config.py#L404"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.backend_config.BackendConfig.from_dict" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">backend_config_dict</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/backend_config/backend_config.html#BackendConfig.from_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/quantization/backend_config/backend_config.py#L404"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.backend_config.BackendConfig.from_dict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Create a <code class="docutils literal notranslate"><span class="pre">BackendConfig</span></code> from a dictionary with the following items:</p>
 <blockquote>
 <div><p>“name”: the name of the target backend</p>
@@ -682,7 +682,7 @@ <h1>BackendConfig<a class="headerlink" href="#backendconfig" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.ao.quantization.backend_config.BackendConfig.set_backend_pattern_config">
-<span class="sig-name descname"><span class="pre">set_backend_pattern_config</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">config</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/backend_config/backend_config.html#BackendConfig.set_backend_pattern_config"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/backend_config/backend_config.py#L374"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.backend_config.BackendConfig.set_backend_pattern_config" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">set_backend_pattern_config</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">config</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/backend_config/backend_config.html#BackendConfig.set_backend_pattern_config"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/quantization/backend_config/backend_config.py#L374"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.backend_config.BackendConfig.set_backend_pattern_config" title="Permalink to this definition">¶</a></dt>
 <dd><p>Set the config for an pattern that can be run on the target backend.
 This overrides any existing config for the given pattern.</p>
 <dl class="field-list simple">
@@ -694,7 +694,7 @@ <h1>BackendConfig<a class="headerlink" href="#backendconfig" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.ao.quantization.backend_config.BackendConfig.set_backend_pattern_configs">
-<span class="sig-name descname"><span class="pre">set_backend_pattern_configs</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">configs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/backend_config/backend_config.html#BackendConfig.set_backend_pattern_configs"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/backend_config/backend_config.py#L386"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.backend_config.BackendConfig.set_backend_pattern_configs" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">set_backend_pattern_configs</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">configs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/backend_config/backend_config.html#BackendConfig.set_backend_pattern_configs"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/quantization/backend_config/backend_config.py#L386"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.backend_config.BackendConfig.set_backend_pattern_configs" title="Permalink to this definition">¶</a></dt>
 <dd><p>Set the configs for patterns that can be run on the target backend.
 This overrides any existing config for a given pattern if it was previously registered already.</p>
 <dl class="field-list simple">
@@ -706,7 +706,7 @@ <h1>BackendConfig<a class="headerlink" href="#backendconfig" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.ao.quantization.backend_config.BackendConfig.set_name">
-<span class="sig-name descname"><span class="pre">set_name</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/backend_config/backend_config.html#BackendConfig.set_name"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/backend_config/backend_config.py#L367"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.backend_config.BackendConfig.set_name" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">set_name</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/backend_config/backend_config.html#BackendConfig.set_name"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/quantization/backend_config/backend_config.py#L367"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.backend_config.BackendConfig.set_name" title="Permalink to this definition">¶</a></dt>
 <dd><p>Set the name of the target backend.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -717,7 +717,7 @@ <h1>BackendConfig<a class="headerlink" href="#backendconfig" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.ao.quantization.backend_config.BackendConfig.to_dict">
-<span class="sig-name descname"><span class="pre">to_dict</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/backend_config/backend_config.html#BackendConfig.to_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/backend_config/backend_config.py#L426"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.backend_config.BackendConfig.to_dict" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">to_dict</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/backend_config/backend_config.html#BackendConfig.to_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/quantization/backend_config/backend_config.py#L426"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.backend_config.BackendConfig.to_dict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Convert this <code class="docutils literal notranslate"><span class="pre">BackendConfig</span></code> to a dictionary with the items described in
 <a class="reference internal" href="#torch.ao.quantization.backend_config.BackendConfig.from_dict" title="torch.ao.quantization.backend_config.BackendConfig.from_dict"><code class="xref py py-func docutils literal notranslate"><span class="pre">from_dict()</span></code></a>.</p>
 <dl class="field-list simple">
diff --git a/main/generated/torch.ao.quantization.backend_config.BackendPatternConfig.html b/main/generated/torch.ao.quantization.backend_config.BackendPatternConfig.html
index 9185f56f71a..23bc08a7508 100644
--- a/main/generated/torch.ao.quantization.backend_config.BackendPatternConfig.html
+++ b/main/generated/torch.ao.quantization.backend_config.BackendPatternConfig.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,14 +614,14 @@
 <h1>BackendPatternConfig<a class="headerlink" href="#backendpatternconfig" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.quantization.backend_config.BackendPatternConfig">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.backend_config.</span></span><span class="sig-name descname"><span class="pre">BackendPatternConfig</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">pattern</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/backend_config/backend_config.html#BackendPatternConfig"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/backend_config/backend_config.py#L437"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.backend_config.BackendPatternConfig" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.backend_config.</span></span><span class="sig-name descname"><span class="pre">BackendPatternConfig</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">pattern</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/backend_config/backend_config.html#BackendPatternConfig"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/quantization/backend_config/backend_config.py#L437"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.backend_config.BackendPatternConfig" title="Permalink to this definition">¶</a></dt>
 <dd><p>Config object that specifies quantization behavior for a given operator pattern.
 For a detailed example usage, see <a class="reference internal" href="torch.ao.quantization.backend_config.BackendConfig.html#torch.ao.quantization.backend_config.BackendConfig" title="torch.ao.quantization.backend_config.BackendConfig"><code class="xref py py-class docutils literal notranslate"><span class="pre">BackendConfig</span></code></a>.</p>
 <dl class="field-list simple">
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.ao.quantization.backend_config.BackendPatternConfig.add_dtype_config">
-<span class="sig-name descname"><span class="pre">add_dtype_config</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dtype_config</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/backend_config/backend_config.html#BackendPatternConfig.add_dtype_config"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/backend_config/backend_config.py#L512"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.backend_config.BackendPatternConfig.add_dtype_config" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">add_dtype_config</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dtype_config</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/backend_config/backend_config.html#BackendPatternConfig.add_dtype_config"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/quantization/backend_config/backend_config.py#L512"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.backend_config.BackendPatternConfig.add_dtype_config" title="Permalink to this definition">¶</a></dt>
 <dd><p>Add a set of supported data types passed as arguments to quantize ops in the
 reference model spec.</p>
 <dl class="field-list simple">
@@ -633,7 +633,7 @@ <h1>BackendPatternConfig<a class="headerlink" href="#backendpatternconfig" title
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.ao.quantization.backend_config.BackendPatternConfig.from_dict">
-<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">backend_pattern_config_dict</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/backend_config/backend_config.html#BackendPatternConfig.from_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/backend_config/backend_config.py#L630"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.backend_config.BackendPatternConfig.from_dict" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">backend_pattern_config_dict</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/backend_config/backend_config.html#BackendPatternConfig.from_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/quantization/backend_config/backend_config.py#L630"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.backend_config.BackendPatternConfig.from_dict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Create a <code class="docutils literal notranslate"><span class="pre">BackendPatternConfig</span></code> from a dictionary with the following items:</p>
 <blockquote>
 <div><p>“pattern”: the pattern being configured
@@ -657,7 +657,7 @@ <h1>BackendPatternConfig<a class="headerlink" href="#backendpatternconfig" title
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.ao.quantization.backend_config.BackendPatternConfig.set_dtype_configs">
-<span class="sig-name descname"><span class="pre">set_dtype_configs</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dtype_configs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/backend_config/backend_config.html#BackendPatternConfig.set_dtype_configs"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/backend_config/backend_config.py#L520"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.backend_config.BackendPatternConfig.set_dtype_configs" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">set_dtype_configs</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dtype_configs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/backend_config/backend_config.html#BackendPatternConfig.set_dtype_configs"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/quantization/backend_config/backend_config.py#L520"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.backend_config.BackendPatternConfig.set_dtype_configs" title="Permalink to this definition">¶</a></dt>
 <dd><p>Set the supported data types passed as arguments to quantize ops in the
 reference model spec, overriding all previously registered data types.</p>
 <dl class="field-list simple">
@@ -669,7 +669,7 @@ <h1>BackendPatternConfig<a class="headerlink" href="#backendpatternconfig" title
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.ao.quantization.backend_config.BackendPatternConfig.set_fused_module">
-<span class="sig-name descname"><span class="pre">set_fused_module</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">fused_module</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/backend_config/backend_config.html#BackendPatternConfig.set_fused_module"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/backend_config/backend_config.py#L567"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.backend_config.BackendPatternConfig.set_fused_module" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">set_fused_module</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">fused_module</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/backend_config/backend_config.html#BackendPatternConfig.set_fused_module"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/quantization/backend_config/backend_config.py#L567"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.backend_config.BackendPatternConfig.set_fused_module" title="Permalink to this definition">¶</a></dt>
 <dd><p>Set the module that represents the fused implementation for this pattern.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -680,7 +680,7 @@ <h1>BackendPatternConfig<a class="headerlink" href="#backendpatternconfig" title
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.ao.quantization.backend_config.BackendPatternConfig.set_fuser_method">
-<span class="sig-name descname"><span class="pre">set_fuser_method</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">fuser_method</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/backend_config/backend_config.html#BackendPatternConfig.set_fuser_method"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/backend_config/backend_config.py#L576"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.backend_config.BackendPatternConfig.set_fuser_method" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">set_fuser_method</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">fuser_method</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/backend_config/backend_config.html#BackendPatternConfig.set_fuser_method"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/quantization/backend_config/backend_config.py#L576"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.backend_config.BackendPatternConfig.set_fuser_method" title="Permalink to this definition">¶</a></dt>
 <dd><p>Set the function that specifies how to fuse this BackendPatternConfig’s pattern.</p>
 <p>The first argument of this function should be <cite>is_qat</cite>, and the rest of the arguments
 should be the items in the tuple pattern. The return value of this function should be
@@ -702,7 +702,7 @@ <h1>BackendPatternConfig<a class="headerlink" href="#backendpatternconfig" title
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.ao.quantization.backend_config.BackendPatternConfig.set_observation_type">
-<span class="sig-name descname"><span class="pre">set_observation_type</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">observation_type</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/backend_config/backend_config.html#BackendPatternConfig.set_observation_type"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/backend_config/backend_config.py#L486"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.backend_config.BackendPatternConfig.set_observation_type" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">set_observation_type</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">observation_type</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/backend_config/backend_config.html#BackendPatternConfig.set_observation_type"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/quantization/backend_config/backend_config.py#L486"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.backend_config.BackendPatternConfig.set_observation_type" title="Permalink to this definition">¶</a></dt>
 <dd><p>Set how observers should be inserted in the graph for this pattern.</p>
 <p>Observation type here refers to how observers (or quant-dequant ops) will be placed
 in the graph. This is used to produce the desired reference patterns understood by
@@ -727,7 +727,7 @@ <h1>BackendPatternConfig<a class="headerlink" href="#backendpatternconfig" title
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.ao.quantization.backend_config.BackendPatternConfig.set_pattern">
-<span class="sig-name descname"><span class="pre">set_pattern</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">pattern</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/backend_config/backend_config.html#BackendPatternConfig.set_pattern"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/backend_config/backend_config.py#L471"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.backend_config.BackendPatternConfig.set_pattern" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">set_pattern</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">pattern</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/backend_config/backend_config.html#BackendPatternConfig.set_pattern"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/quantization/backend_config/backend_config.py#L471"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.backend_config.BackendPatternConfig.set_pattern" title="Permalink to this definition">¶</a></dt>
 <dd><p>Set the pattern to configure.</p>
 <p>The pattern can be a float module, functional operator, pytorch operator, or a tuple
 combination of the above. Tuple patterns are treated as sequential patterns, and
@@ -741,7 +741,7 @@ <h1>BackendPatternConfig<a class="headerlink" href="#backendpatternconfig" title
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.ao.quantization.backend_config.BackendPatternConfig.set_qat_module">
-<span class="sig-name descname"><span class="pre">set_qat_module</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">qat_module</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/backend_config/backend_config.html#BackendPatternConfig.set_qat_module"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/backend_config/backend_config.py#L548"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.backend_config.BackendPatternConfig.set_qat_module" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">set_qat_module</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">qat_module</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/backend_config/backend_config.html#BackendPatternConfig.set_qat_module"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/quantization/backend_config/backend_config.py#L548"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.backend_config.BackendPatternConfig.set_qat_module" title="Permalink to this definition">¶</a></dt>
 <dd><p>Set the module that represents the QAT implementation for this pattern.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -752,7 +752,7 @@ <h1>BackendPatternConfig<a class="headerlink" href="#backendpatternconfig" title
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.ao.quantization.backend_config.BackendPatternConfig.set_reference_quantized_module">
-<span class="sig-name descname"><span class="pre">set_reference_quantized_module</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">reference_quantized_module</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/backend_config/backend_config.html#BackendPatternConfig.set_reference_quantized_module"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/backend_config/backend_config.py#L555"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.backend_config.BackendPatternConfig.set_reference_quantized_module" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">set_reference_quantized_module</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">reference_quantized_module</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/backend_config/backend_config.html#BackendPatternConfig.set_reference_quantized_module"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/quantization/backend_config/backend_config.py#L555"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.backend_config.BackendPatternConfig.set_reference_quantized_module" title="Permalink to this definition">¶</a></dt>
 <dd><p>Set the module that represents the reference quantized implementation for
 this pattern’s root module.</p>
 <p>For more detail, see <a class="reference internal" href="#torch.ao.quantization.backend_config.BackendPatternConfig.set_root_module" title="torch.ao.quantization.backend_config.BackendPatternConfig.set_root_module"><code class="xref py py-func docutils literal notranslate"><span class="pre">set_root_module()</span></code></a>.</p>
@@ -765,7 +765,7 @@ <h1>BackendPatternConfig<a class="headerlink" href="#backendpatternconfig" title
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.ao.quantization.backend_config.BackendPatternConfig.set_root_module">
-<span class="sig-name descname"><span class="pre">set_root_module</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">root_module</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/backend_config/backend_config.html#BackendPatternConfig.set_root_module"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/backend_config/backend_config.py#L530"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.backend_config.BackendPatternConfig.set_root_module" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">set_root_module</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">root_module</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/backend_config/backend_config.html#BackendPatternConfig.set_root_module"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/quantization/backend_config/backend_config.py#L530"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.backend_config.BackendPatternConfig.set_root_module" title="Permalink to this definition">¶</a></dt>
 <dd><p>Set the module that represents the root for this pattern.</p>
 <p>When we construct the reference quantized model during the convert phase,
 the root modules (e.g. torch.nn.Linear for torch.ao.nn.intrinsic.LinearReLU)
@@ -784,7 +784,7 @@ <h1>BackendPatternConfig<a class="headerlink" href="#backendpatternconfig" title
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.ao.quantization.backend_config.BackendPatternConfig.to_dict">
-<span class="sig-name descname"><span class="pre">to_dict</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/backend_config/backend_config.html#BackendPatternConfig.to_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/backend_config/backend_config.py#L706"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.backend_config.BackendPatternConfig.to_dict" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">to_dict</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/backend_config/backend_config.html#BackendPatternConfig.to_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/quantization/backend_config/backend_config.py#L706"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.backend_config.BackendPatternConfig.to_dict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Convert this <code class="docutils literal notranslate"><span class="pre">BackendPatternConfig</span></code> to a dictionary with the items described in
 <a class="reference internal" href="#torch.ao.quantization.backend_config.BackendPatternConfig.from_dict" title="torch.ao.quantization.backend_config.BackendPatternConfig.from_dict"><code class="xref py py-func docutils literal notranslate"><span class="pre">from_dict()</span></code></a>.</p>
 <dl class="field-list simple">
diff --git a/main/generated/torch.ao.quantization.backend_config.DTypeConfig.html b/main/generated/torch.ao.quantization.backend_config.DTypeConfig.html
index eae9d53797a..46fa7a1050b 100644
--- a/main/generated/torch.ao.quantization.backend_config.DTypeConfig.html
+++ b/main/generated/torch.ao.quantization.backend_config.DTypeConfig.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>DTypeConfig<a class="headerlink" href="#dtypeconfig" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.quantization.backend_config.DTypeConfig">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.backend_config.</span></span><span class="sig-name descname"><span class="pre">DTypeConfig</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weight_dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias_dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">is_dynamic</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/backend_config/backend_config.html#DTypeConfig"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/backend_config/backend_config.py#L112"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.backend_config.DTypeConfig" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.backend_config.</span></span><span class="sig-name descname"><span class="pre">DTypeConfig</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weight_dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias_dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">is_dynamic</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/backend_config/backend_config.html#DTypeConfig"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/quantization/backend_config/backend_config.py#L112"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.backend_config.DTypeConfig" title="Permalink to this definition">¶</a></dt>
 <dd><p>Config object that specifies the supported data types passed as arguments to
 quantize ops in the reference model spec, for input and output activations,
 weights, and biases.</p>
@@ -680,7 +680,7 @@ <h1>DTypeConfig<a class="headerlink" href="#dtypeconfig" title="Permalink to thi
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.ao.quantization.backend_config.DTypeConfig.from_dict">
-<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dtype_config_dict</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/backend_config/backend_config.html#DTypeConfig.from_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/backend_config/backend_config.py#L231"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.backend_config.DTypeConfig.from_dict" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dtype_config_dict</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/backend_config/backend_config.html#DTypeConfig.from_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/quantization/backend_config/backend_config.py#L231"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.backend_config.DTypeConfig.from_dict" title="Permalink to this definition">¶</a></dt>
 <dd><dl class="simple">
 <dt>Create a <code class="docutils literal notranslate"><span class="pre">DTypeConfig</span></code> from a dictionary with the following items (all optional):</dt><dd><p>“input_dtype”: torch.dtype or <code class="docutils literal notranslate"><span class="pre">DTypeWithConstraints</span></code>
 “output_dtype”: torch.dtype or <code class="docutils literal notranslate"><span class="pre">DTypeWithConstraints</span></code>
@@ -698,7 +698,7 @@ <h1>DTypeConfig<a class="headerlink" href="#dtypeconfig" title="Permalink to thi
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.ao.quantization.backend_config.DTypeConfig.to_dict">
-<span class="sig-name descname"><span class="pre">to_dict</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/backend_config/backend_config.html#DTypeConfig.to_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/backend_config/backend_config.py#L266"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.backend_config.DTypeConfig.to_dict" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">to_dict</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/backend_config/backend_config.html#DTypeConfig.to_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/quantization/backend_config/backend_config.py#L266"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.backend_config.DTypeConfig.to_dict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Convert this <code class="docutils literal notranslate"><span class="pre">DTypeConfig</span></code> to a dictionary with the items described in
 <a class="reference internal" href="#torch.ao.quantization.backend_config.DTypeConfig.from_dict" title="torch.ao.quantization.backend_config.DTypeConfig.from_dict"><code class="xref py py-func docutils literal notranslate"><span class="pre">from_dict()</span></code></a>.</p>
 <dl class="field-list simple">
diff --git a/main/generated/torch.ao.quantization.backend_config.DTypeWithConstraints.html b/main/generated/torch.ao.quantization.backend_config.DTypeWithConstraints.html
index 629da8d50df..3b0145f504e 100644
--- a/main/generated/torch.ao.quantization.backend_config.DTypeWithConstraints.html
+++ b/main/generated/torch.ao.quantization.backend_config.DTypeWithConstraints.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>DTypeWithConstraints<a class="headerlink" href="#dtypewithconstraints" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.quantization.backend_config.DTypeWithConstraints">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.backend_config.</span></span><span class="sig-name descname"><span class="pre">DTypeWithConstraints</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">quant_min_lower_bound</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">quant_max_upper_bound</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale_min_lower_bound</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale_max_upper_bound</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale_exact_match</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">zero_point_exact_match</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/backend_config/backend_config.html#DTypeWithConstraints"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/backend_config/backend_config.py#L76"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.backend_config.DTypeWithConstraints" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.backend_config.</span></span><span class="sig-name descname"><span class="pre">DTypeWithConstraints</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">quant_min_lower_bound</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">quant_max_upper_bound</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale_min_lower_bound</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale_max_upper_bound</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale_exact_match</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">zero_point_exact_match</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/backend_config/backend_config.html#DTypeWithConstraints"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/quantization/backend_config/backend_config.py#L76"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.backend_config.DTypeWithConstraints" title="Permalink to this definition">¶</a></dt>
 <dd><p>Config for specifying additional constraints for a given dtype, such as quantization
 value ranges, scale value ranges, and fixed quantization params, to be used in
 <a class="reference internal" href="torch.ao.quantization.backend_config.DTypeConfig.html#torch.ao.quantization.backend_config.DTypeConfig" title="torch.ao.quantization.backend_config.DTypeConfig"><code class="xref py py-class docutils literal notranslate"><span class="pre">DTypeConfig</span></code></a>.</p>
diff --git a/main/generated/torch.ao.quantization.backend_config.ObservationType.html b/main/generated/torch.ao.quantization.backend_config.ObservationType.html
index 4145c3f4da3..608cfec415e 100644
--- a/main/generated/torch.ao.quantization.backend_config.ObservationType.html
+++ b/main/generated/torch.ao.quantization.backend_config.ObservationType.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>ObservationType<a class="headerlink" href="#observationtype" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.quantization.backend_config.ObservationType">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.backend_config.</span></span><span class="sig-name descname"><span class="pre">ObservationType</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/backend_config/backend_config.html#ObservationType"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/backend_config/backend_config.py#L53"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.backend_config.ObservationType" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.backend_config.</span></span><span class="sig-name descname"><span class="pre">ObservationType</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/backend_config/backend_config.html#ObservationType"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/quantization/backend_config/backend_config.py#L53"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.backend_config.ObservationType" title="Permalink to this definition">¶</a></dt>
 <dd><p>An enum that represents different ways of how an operator/operator pattern
 should be observed</p>
 <dl class="py attribute">
diff --git a/main/generated/torch.ao.quantization.compare_results.html b/main/generated/torch.ao.quantization.compare_results.html
index d8504726e0c..9af6591120d 100644
--- a/main/generated/torch.ao.quantization.compare_results.html
+++ b/main/generated/torch.ao.quantization.compare_results.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>compare_results<a class="headerlink" href="#compare-results" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.quantization.compare_results">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.</span></span><span class="sig-name descname"><span class="pre">compare_results</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">ref_results</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">actual_results</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/pt2e/_numeric_debugger.py#L297"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.compare_results" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.</span></span><span class="sig-name descname"><span class="pre">compare_results</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">ref_results</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">actual_results</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/quantization/pt2e/_numeric_debugger.py#L297"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.compare_results" title="Permalink to this definition">¶</a></dt>
 <dd><p>Given two dict mapping from <cite>debug_handle_id</cite> (int) to list of tensors
 return a map from <cite>debug_handle_id</cite> to <cite>NodeAccuracySummary</cite> that contains
 comparison information like SQNR, MSE etc.</p>
diff --git a/main/generated/torch.ao.quantization.convert.html b/main/generated/torch.ao.quantization.convert.html
index 1b1d7e0067b..3ac39ddb9b3 100644
--- a/main/generated/torch.ao.quantization.convert.html
+++ b/main/generated/torch.ao.quantization.convert.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>convert<a class="headerlink" href="#convert" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.quantization.convert">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.</span></span><span class="sig-name descname"><span class="pre">convert</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mapping</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">remove_qconfig</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">is_reference</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">convert_custom_config_dict</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_precomputed_fake_quant</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/quantize.html#convert"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/quantize.py#L616"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.convert" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.</span></span><span class="sig-name descname"><span class="pre">convert</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mapping</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">remove_qconfig</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">is_reference</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">convert_custom_config_dict</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_precomputed_fake_quant</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/quantize.html#convert"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/quantization/quantize.py#L616"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.convert" title="Permalink to this definition">¶</a></dt>
 <dd><p>Converts submodules in input module to a different module according to <cite>mapping</cite>
 by calling <cite>from_float</cite> method on the target module class. And remove qconfig at the
 end if remove_qconfig is set to True.</p>
diff --git a/main/generated/torch.ao.quantization.default_eval_fn.html b/main/generated/torch.ao.quantization.default_eval_fn.html
index 0f7b7f6b53c..fd5803df8b0 100644
--- a/main/generated/torch.ao.quantization.default_eval_fn.html
+++ b/main/generated/torch.ao.quantization.default_eval_fn.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>default_eval_fn<a class="headerlink" href="#default-eval-fn" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.quantization.default_eval_fn">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.</span></span><span class="sig-name descname"><span class="pre">default_eval_fn</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">calib_data</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization.html#default_eval_fn"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/__init__.py#L188"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.default_eval_fn" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.</span></span><span class="sig-name descname"><span class="pre">default_eval_fn</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">calib_data</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization.html#default_eval_fn"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/quantization/__init__.py#L188"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.default_eval_fn" title="Permalink to this definition">¶</a></dt>
 <dd><p>Define the default evaluation function.</p>
 <p>Default evaluation function takes a torch.utils.data.Dataset or a list of
 input Tensors and run the model on the dataset</p>
diff --git a/main/generated/torch.ao.quantization.extract_results_from_loggers.html b/main/generated/torch.ao.quantization.extract_results_from_loggers.html
index f8bd72011e9..ae783f3e4d9 100644
--- a/main/generated/torch.ao.quantization.extract_results_from_loggers.html
+++ b/main/generated/torch.ao.quantization.extract_results_from_loggers.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>extract_results_from_loggers<a class="headerlink" href="#extract-results-from-loggers" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.quantization.extract_results_from_loggers">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.</span></span><span class="sig-name descname"><span class="pre">extract_results_from_loggers</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/pt2e/_numeric_debugger.py#L272"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.extract_results_from_loggers" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.</span></span><span class="sig-name descname"><span class="pre">extract_results_from_loggers</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/quantization/pt2e/_numeric_debugger.py#L272"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.extract_results_from_loggers" title="Permalink to this definition">¶</a></dt>
 <dd><p>For a given model, extract the tensors stats and related information for each debug handle.
 The reason we have a list of object, instead of Tensor is because the output of node may not be
 a Tensor, it could be (nested) list, tuple or dict as well.</p>
diff --git a/main/generated/torch.ao.quantization.fake_quantize.FakeQuantize.html b/main/generated/torch.ao.quantization.fake_quantize.FakeQuantize.html
index dd790c05298..5386e729a02 100644
--- a/main/generated/torch.ao.quantization.fake_quantize.FakeQuantize.html
+++ b/main/generated/torch.ao.quantization.fake_quantize.FakeQuantize.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>FakeQuantize<a class="headerlink" href="#fakequantize" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.quantization.fake_quantize.FakeQuantize">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.fake_quantize.</span></span><span class="sig-name descname"><span class="pre">FakeQuantize</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">observer=&lt;class</span> <span class="pre">'torch.ao.quantization.observer.MovingAverageMinMaxObserver'&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">quant_min=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">quant_max=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">is_dynamic=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">**observer_kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/fake_quantize.html#FakeQuantize"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/fake_quantize.py#L130"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.fake_quantize.FakeQuantize" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.fake_quantize.</span></span><span class="sig-name descname"><span class="pre">FakeQuantize</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">observer=&lt;class</span> <span class="pre">'torch.ao.quantization.observer.MovingAverageMinMaxObserver'&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">quant_min=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">quant_max=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">is_dynamic=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">**observer_kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/fake_quantize.html#FakeQuantize"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/quantization/fake_quantize.py#L130"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.fake_quantize.FakeQuantize" title="Permalink to this definition">¶</a></dt>
 <dd><p>Simulate the quantize and dequantize operations in training time.</p>
 <p>The output of this module is given by:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">x_out</span> <span class="o">=</span> <span class="p">(</span>
diff --git a/main/generated/torch.ao.quantization.fake_quantize.FakeQuantizeBase.html b/main/generated/torch.ao.quantization.fake_quantize.FakeQuantizeBase.html
index f3a64a931dd..fdc3cd4f0f5 100644
--- a/main/generated/torch.ao.quantization.fake_quantize.FakeQuantizeBase.html
+++ b/main/generated/torch.ao.quantization.fake_quantize.FakeQuantizeBase.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>FakeQuantizeBase<a class="headerlink" href="#fakequantizebase" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.quantization.fake_quantize.FakeQuantizeBase">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.fake_quantize.</span></span><span class="sig-name descname"><span class="pre">FakeQuantizeBase</span></span><a class="reference internal" href="../_modules/torch/ao/quantization/fake_quantize.html#FakeQuantizeBase"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/fake_quantize.py#L72"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.fake_quantize.FakeQuantizeBase" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.fake_quantize.</span></span><span class="sig-name descname"><span class="pre">FakeQuantizeBase</span></span><a class="reference internal" href="../_modules/torch/ao/quantization/fake_quantize.html#FakeQuantizeBase"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/quantization/fake_quantize.py#L72"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.fake_quantize.FakeQuantizeBase" title="Permalink to this definition">¶</a></dt>
 <dd><p>Base fake quantize module.</p>
 <p>Base fake quantize module
 Any fake quantize implementation should derive from this class.</p>
diff --git a/main/generated/torch.ao.quantization.fake_quantize.FixedQParamsFakeQuantize.html b/main/generated/torch.ao.quantization.fake_quantize.FixedQParamsFakeQuantize.html
index b7aff96c8c9..6d62a050c44 100644
--- a/main/generated/torch.ao.quantization.fake_quantize.FixedQParamsFakeQuantize.html
+++ b/main/generated/torch.ao.quantization.fake_quantize.FixedQParamsFakeQuantize.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
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     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,14 +614,14 @@
 <h1>FixedQParamsFakeQuantize<a class="headerlink" href="#fixedqparamsfakequantize" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.quantization.fake_quantize.FixedQParamsFakeQuantize">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.fake_quantize.</span></span><span class="sig-name descname"><span class="pre">FixedQParamsFakeQuantize</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">observer</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/fake_quantize.html#FixedQParamsFakeQuantize"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/fake_quantize.py#L320"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.fake_quantize.FixedQParamsFakeQuantize" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.fake_quantize.</span></span><span class="sig-name descname"><span class="pre">FixedQParamsFakeQuantize</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">observer</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/fake_quantize.html#FixedQParamsFakeQuantize"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/quantization/fake_quantize.py#L320"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.fake_quantize.FixedQParamsFakeQuantize" title="Permalink to this definition">¶</a></dt>
 <dd><p>Simulate quantize and dequantize in training time.</p>
 <p>Simulate quantize and dequantize with fixed quantization
 parameters in training time. Only per tensor quantization
 is supported.</p>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.ao.quantization.fake_quantize.FixedQParamsFakeQuantize.extra_repr">
-<span class="sig-name descname"><span class="pre">extra_repr</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/fake_quantize.html#FixedQParamsFakeQuantize.extra_repr"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/fake_quantize.py#L347"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.fake_quantize.FixedQParamsFakeQuantize.extra_repr" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">extra_repr</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/fake_quantize.html#FixedQParamsFakeQuantize.extra_repr"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/quantization/fake_quantize.py#L347"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.fake_quantize.FixedQParamsFakeQuantize.extra_repr" title="Permalink to this definition">¶</a></dt>
 <dd><p>Define a string representation of the object’s attributes.</p>
 </dd></dl>
 
diff --git a/main/generated/torch.ao.quantization.fake_quantize.FusedMovingAvgObsFakeQuantize.html b/main/generated/torch.ao.quantization.fake_quantize.FusedMovingAvgObsFakeQuantize.html
index f24c40e7299..e1edb42a880 100644
--- a/main/generated/torch.ao.quantization.fake_quantize.FusedMovingAvgObsFakeQuantize.html
+++ b/main/generated/torch.ao.quantization.fake_quantize.FusedMovingAvgObsFakeQuantize.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
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+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>FusedMovingAvgObsFakeQuantize<a class="headerlink" href="#fusedmovingavgobsfakequantize" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.quantization.fake_quantize.FusedMovingAvgObsFakeQuantize">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.fake_quantize.</span></span><span class="sig-name descname"><span class="pre">FusedMovingAvgObsFakeQuantize</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">observer=&lt;class</span> <span class="pre">'torch.ao.quantization.observer.MovingAverageMinMaxObserver'&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">quant_min=0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">quant_max=255</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">**observer_kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/fake_quantize.html#FusedMovingAvgObsFakeQuantize"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/fake_quantize.py#L358"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.fake_quantize.FusedMovingAvgObsFakeQuantize" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.fake_quantize.</span></span><span class="sig-name descname"><span class="pre">FusedMovingAvgObsFakeQuantize</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">observer=&lt;class</span> <span class="pre">'torch.ao.quantization.observer.MovingAverageMinMaxObserver'&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">quant_min=0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">quant_max=255</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">**observer_kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/fake_quantize.html#FusedMovingAvgObsFakeQuantize"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/quantization/fake_quantize.py#L358"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.fake_quantize.FusedMovingAvgObsFakeQuantize" title="Permalink to this definition">¶</a></dt>
 <dd><p>Define a fused module to observe the tensor.</p>
 <p>Fused module that is used to observe the input tensor (compute min/max), compute
 scale/zero_point and fake_quantize the tensor.
diff --git a/main/generated/torch.ao.quantization.fake_quantize.default_fake_quant.html b/main/generated/torch.ao.quantization.fake_quantize.default_fake_quant.html
index 4ab62299a5b..c395bebc95e 100644
--- a/main/generated/torch.ao.quantization.fake_quantize.default_fake_quant.html
+++ b/main/generated/torch.ao.quantization.fake_quantize.default_fake_quant.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.ao.quantization.fake_quantize.default_fused_act_fake_quant.html b/main/generated/torch.ao.quantization.fake_quantize.default_fused_act_fake_quant.html
index 1e3ee3f74cc..15426ae3afd 100644
--- a/main/generated/torch.ao.quantization.fake_quantize.default_fused_act_fake_quant.html
+++ b/main/generated/torch.ao.quantization.fake_quantize.default_fused_act_fake_quant.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
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     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.ao.quantization.fake_quantize.default_fused_per_channel_wt_fake_quant.html b/main/generated/torch.ao.quantization.fake_quantize.default_fused_per_channel_wt_fake_quant.html
index 19e0fd91fd5..fcf1ff1835a 100644
--- a/main/generated/torch.ao.quantization.fake_quantize.default_fused_per_channel_wt_fake_quant.html
+++ b/main/generated/torch.ao.quantization.fake_quantize.default_fused_per_channel_wt_fake_quant.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.ao.quantization.fake_quantize.default_fused_wt_fake_quant.html b/main/generated/torch.ao.quantization.fake_quantize.default_fused_wt_fake_quant.html
index 616aa9db085..4724b8f48df 100644
--- a/main/generated/torch.ao.quantization.fake_quantize.default_fused_wt_fake_quant.html
+++ b/main/generated/torch.ao.quantization.fake_quantize.default_fused_wt_fake_quant.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.ao.quantization.fake_quantize.default_histogram_fake_quant.html b/main/generated/torch.ao.quantization.fake_quantize.default_histogram_fake_quant.html
index 29537e93649..9e60dacae51 100644
--- a/main/generated/torch.ao.quantization.fake_quantize.default_histogram_fake_quant.html
+++ b/main/generated/torch.ao.quantization.fake_quantize.default_histogram_fake_quant.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.ao.quantization.fake_quantize.default_per_channel_weight_fake_quant.html b/main/generated/torch.ao.quantization.fake_quantize.default_per_channel_weight_fake_quant.html
index 758dc0fa174..3da9929405b 100644
--- a/main/generated/torch.ao.quantization.fake_quantize.default_per_channel_weight_fake_quant.html
+++ b/main/generated/torch.ao.quantization.fake_quantize.default_per_channel_weight_fake_quant.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.ao.quantization.fake_quantize.default_weight_fake_quant.html b/main/generated/torch.ao.quantization.fake_quantize.default_weight_fake_quant.html
index 058abb52bfe..10ba6aa9a6e 100644
--- a/main/generated/torch.ao.quantization.fake_quantize.default_weight_fake_quant.html
+++ b/main/generated/torch.ao.quantization.fake_quantize.default_weight_fake_quant.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.ao.quantization.fake_quantize.disable_fake_quant.html b/main/generated/torch.ao.quantization.fake_quantize.disable_fake_quant.html
index 0fcb90ab657..6122147a617 100644
--- a/main/generated/torch.ao.quantization.fake_quantize.disable_fake_quant.html
+++ b/main/generated/torch.ao.quantization.fake_quantize.disable_fake_quant.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>disable_fake_quant<a class="headerlink" href="#disable-fake-quant" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.quantization.fake_quantize.disable_fake_quant">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.fake_quantize.</span></span><span class="sig-name descname"><span class="pre">disable_fake_quant</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mod</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/fake_quantize.html#disable_fake_quant"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/fake_quantize.py#L598"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.fake_quantize.disable_fake_quant" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.fake_quantize.</span></span><span class="sig-name descname"><span class="pre">disable_fake_quant</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mod</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/fake_quantize.html#disable_fake_quant"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/quantization/fake_quantize.py#L598"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.fake_quantize.disable_fake_quant" title="Permalink to this definition">¶</a></dt>
 <dd><p>Disable fake quantization for the module.</p>
 <p>Disable fake quantization for this module, if applicable. Example usage:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># model is any PyTorch model</span>
diff --git a/main/generated/torch.ao.quantization.fake_quantize.disable_observer.html b/main/generated/torch.ao.quantization.fake_quantize.disable_observer.html
index 9b74966d799..0d276084a6d 100644
--- a/main/generated/torch.ao.quantization.fake_quantize.disable_observer.html
+++ b/main/generated/torch.ao.quantization.fake_quantize.disable_observer.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>disable_observer<a class="headerlink" href="#disable-observer" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.quantization.fake_quantize.disable_observer">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.fake_quantize.</span></span><span class="sig-name descname"><span class="pre">disable_observer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mod</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/fake_quantize.html#disable_observer"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/fake_quantize.py#L624"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.fake_quantize.disable_observer" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.fake_quantize.</span></span><span class="sig-name descname"><span class="pre">disable_observer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mod</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/fake_quantize.html#disable_observer"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/quantization/fake_quantize.py#L624"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.fake_quantize.disable_observer" title="Permalink to this definition">¶</a></dt>
 <dd><p>Disable observation for this module.</p>
 <p>Disable observation for this module, if applicable. Example usage:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># model is any PyTorch model</span>
diff --git a/main/generated/torch.ao.quantization.fake_quantize.enable_fake_quant.html b/main/generated/torch.ao.quantization.fake_quantize.enable_fake_quant.html
index baa3982120c..792cad006e8 100644
--- a/main/generated/torch.ao.quantization.fake_quantize.enable_fake_quant.html
+++ b/main/generated/torch.ao.quantization.fake_quantize.enable_fake_quant.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>enable_fake_quant<a class="headerlink" href="#enable-fake-quant" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.quantization.fake_quantize.enable_fake_quant">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.fake_quantize.</span></span><span class="sig-name descname"><span class="pre">enable_fake_quant</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mod</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/fake_quantize.html#enable_fake_quant"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/fake_quantize.py#L611"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.fake_quantize.enable_fake_quant" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.fake_quantize.</span></span><span class="sig-name descname"><span class="pre">enable_fake_quant</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mod</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/fake_quantize.html#enable_fake_quant"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/quantization/fake_quantize.py#L611"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.fake_quantize.enable_fake_quant" title="Permalink to this definition">¶</a></dt>
 <dd><p>Enable fake quantization for the module.</p>
 <p>Enable fake quantization for this module, if applicable. Example usage:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># model is any PyTorch model</span>
diff --git a/main/generated/torch.ao.quantization.fake_quantize.enable_observer.html b/main/generated/torch.ao.quantization.fake_quantize.enable_observer.html
index 175480a3bb5..f9eed3b76f7 100644
--- a/main/generated/torch.ao.quantization.fake_quantize.enable_observer.html
+++ b/main/generated/torch.ao.quantization.fake_quantize.enable_observer.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>enable_observer<a class="headerlink" href="#enable-observer" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.quantization.fake_quantize.enable_observer">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.fake_quantize.</span></span><span class="sig-name descname"><span class="pre">enable_observer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mod</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/fake_quantize.html#enable_observer"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/fake_quantize.py#L637"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.fake_quantize.enable_observer" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.fake_quantize.</span></span><span class="sig-name descname"><span class="pre">enable_observer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mod</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/fake_quantize.html#enable_observer"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/quantization/fake_quantize.py#L637"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.fake_quantize.enable_observer" title="Permalink to this definition">¶</a></dt>
 <dd><p>Enable observation for this module.</p>
 <p>Enable observation for this module, if applicable. Example usage:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># model is any PyTorch model</span>
diff --git a/main/generated/torch.ao.quantization.fuse_modules.fuse_modules.html b/main/generated/torch.ao.quantization.fuse_modules.fuse_modules.html
index 8da2f860b9e..eaed9ec33bb 100644
--- a/main/generated/torch.ao.quantization.fuse_modules.fuse_modules.html
+++ b/main/generated/torch.ao.quantization.fuse_modules.fuse_modules.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>fuse_modules<a class="headerlink" href="#fuse-modules" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.quantization.fuse_modules.fuse_modules">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.fuse_modules.</span></span><span class="sig-name descname"><span class="pre">fuse_modules</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">modules_to_fuse</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inplace=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fuser_func=&lt;function</span> <span class="pre">fuse_known_modules&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fuse_custom_config_dict=None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/fuse_modules.html#fuse_modules"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/fuse_modules.py#L130"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.fuse_modules.fuse_modules" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.fuse_modules.</span></span><span class="sig-name descname"><span class="pre">fuse_modules</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">modules_to_fuse</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inplace=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fuser_func=&lt;function</span> <span class="pre">fuse_known_modules&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fuse_custom_config_dict=None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/fuse_modules.html#fuse_modules"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/quantization/fuse_modules.py#L130"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.fuse_modules.fuse_modules" title="Permalink to this definition">¶</a></dt>
 <dd><p>Fuse a list of modules into a single module.</p>
 <p>Fuses only the following sequence of modules:
 conv, bn
diff --git a/main/generated/torch.ao.quantization.fx.custom_config.ConvertCustomConfig.html b/main/generated/torch.ao.quantization.fx.custom_config.ConvertCustomConfig.html
index bd94ffbb929..7dfa73d0c18 100644
--- a/main/generated/torch.ao.quantization.fx.custom_config.ConvertCustomConfig.html
+++ b/main/generated/torch.ao.quantization.fx.custom_config.ConvertCustomConfig.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>ConvertCustomConfig<a class="headerlink" href="#convertcustomconfig" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.quantization.fx.custom_config.ConvertCustomConfig">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.fx.custom_config.</span></span><span class="sig-name descname"><span class="pre">ConvertCustomConfig</span></span><a class="reference internal" href="../_modules/torch/ao/quantization/fx/custom_config.html#ConvertCustomConfig"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/fx/custom_config.py#L374"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.fx.custom_config.ConvertCustomConfig" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.fx.custom_config.</span></span><span class="sig-name descname"><span class="pre">ConvertCustomConfig</span></span><a class="reference internal" href="../_modules/torch/ao/quantization/fx/custom_config.html#ConvertCustomConfig"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/quantization/fx/custom_config.py#L374"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.fx.custom_config.ConvertCustomConfig" title="Permalink to this definition">¶</a></dt>
 <dd><p>Custom configuration for <a class="reference internal" href="torch.ao.quantization.quantize_fx.convert_fx.html#torch.ao.quantization.quantize_fx.convert_fx" title="torch.ao.quantization.quantize_fx.convert_fx"><code class="xref py py-func docutils literal notranslate"><span class="pre">convert_fx()</span></code></a>.</p>
 <p>Example usage:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">convert_custom_config</span> <span class="o">=</span> <span class="n">ConvertCustomConfig</span><span class="p">()</span>             <span class="o">.</span><span class="n">set_observed_to_quantized_mapping</span><span class="p">(</span><span class="n">ObservedCustomModule</span><span class="p">,</span> <span class="n">QuantizedCustomModule</span><span class="p">)</span>             <span class="o">.</span><span class="n">set_preserved_attributes</span><span class="p">([</span><span class="s2">&quot;attr1&quot;</span><span class="p">,</span> <span class="s2">&quot;attr2&quot;</span><span class="p">])</span>
@@ -624,7 +624,7 @@ <h1>ConvertCustomConfig<a class="headerlink" href="#convertcustomconfig" title="
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.ao.quantization.fx.custom_config.ConvertCustomConfig.from_dict">
-<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">convert_custom_config_dict</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/fx/custom_config.html#ConvertCustomConfig.from_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/fx/custom_config.py#L419"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.fx.custom_config.ConvertCustomConfig.from_dict" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">convert_custom_config_dict</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/fx/custom_config.html#ConvertCustomConfig.from_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/quantization/fx/custom_config.py#L419"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.fx.custom_config.ConvertCustomConfig.from_dict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Create a <code class="docutils literal notranslate"><span class="pre">ConvertCustomConfig</span></code> from a dictionary with the following items:</p>
 <blockquote>
 <div><p>“observed_to_quantized_custom_module_class”: a nested dictionary mapping from quantization
@@ -646,7 +646,7 @@ <h1>ConvertCustomConfig<a class="headerlink" href="#convertcustomconfig" title="
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.ao.quantization.fx.custom_config.ConvertCustomConfig.set_observed_to_quantized_mapping">
-<span class="sig-name descname"><span class="pre">set_observed_to_quantized_mapping</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">observed_class</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">quantized_class</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">quant_type</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">QuantType.STATIC</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/fx/custom_config.html#ConvertCustomConfig.set_observed_to_quantized_mapping"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/fx/custom_config.py#L393"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.fx.custom_config.ConvertCustomConfig.set_observed_to_quantized_mapping" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">set_observed_to_quantized_mapping</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">observed_class</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">quantized_class</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">quant_type</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">QuantType.STATIC</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/fx/custom_config.html#ConvertCustomConfig.set_observed_to_quantized_mapping"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/quantization/fx/custom_config.py#L393"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.fx.custom_config.ConvertCustomConfig.set_observed_to_quantized_mapping" title="Permalink to this definition">¶</a></dt>
 <dd><p>Set the mapping from a custom observed module class to a custom quantized module class.</p>
 <p>The quantized module class must have a <code class="docutils literal notranslate"><span class="pre">from_observed</span></code> class method that converts the observed module class
 to the quantized module class.</p>
@@ -659,7 +659,7 @@ <h1>ConvertCustomConfig<a class="headerlink" href="#convertcustomconfig" title="
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.ao.quantization.fx.custom_config.ConvertCustomConfig.set_preserved_attributes">
-<span class="sig-name descname"><span class="pre">set_preserved_attributes</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">attributes</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/fx/custom_config.html#ConvertCustomConfig.set_preserved_attributes"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/fx/custom_config.py#L410"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.fx.custom_config.ConvertCustomConfig.set_preserved_attributes" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">set_preserved_attributes</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">attributes</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/fx/custom_config.html#ConvertCustomConfig.set_preserved_attributes"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/quantization/fx/custom_config.py#L410"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.fx.custom_config.ConvertCustomConfig.set_preserved_attributes" title="Permalink to this definition">¶</a></dt>
 <dd><p>Set the names of the attributes that will persist in the graph module even if they are not used in
 the model’s <code class="docutils literal notranslate"><span class="pre">forward</span></code> method.</p>
 <dl class="field-list simple">
@@ -671,7 +671,7 @@ <h1>ConvertCustomConfig<a class="headerlink" href="#convertcustomconfig" title="
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.ao.quantization.fx.custom_config.ConvertCustomConfig.to_dict">
-<span class="sig-name descname"><span class="pre">to_dict</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/fx/custom_config.html#ConvertCustomConfig.to_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/fx/custom_config.py#L451"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.fx.custom_config.ConvertCustomConfig.to_dict" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">to_dict</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/fx/custom_config.html#ConvertCustomConfig.to_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/quantization/fx/custom_config.py#L451"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.fx.custom_config.ConvertCustomConfig.to_dict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Convert this <code class="docutils literal notranslate"><span class="pre">ConvertCustomConfig</span></code> to a dictionary with the items described in
 <a class="reference internal" href="#torch.ao.quantization.fx.custom_config.ConvertCustomConfig.from_dict" title="torch.ao.quantization.fx.custom_config.ConvertCustomConfig.from_dict"><code class="xref py py-func docutils literal notranslate"><span class="pre">from_dict()</span></code></a>.</p>
 <dl class="field-list simple">
diff --git a/main/generated/torch.ao.quantization.fx.custom_config.FuseCustomConfig.html b/main/generated/torch.ao.quantization.fx.custom_config.FuseCustomConfig.html
index 41f0dd7920f..6a9c075b828 100644
--- a/main/generated/torch.ao.quantization.fx.custom_config.FuseCustomConfig.html
+++ b/main/generated/torch.ao.quantization.fx.custom_config.FuseCustomConfig.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>FuseCustomConfig<a class="headerlink" href="#fusecustomconfig" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.quantization.fx.custom_config.FuseCustomConfig">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.fx.custom_config.</span></span><span class="sig-name descname"><span class="pre">FuseCustomConfig</span></span><a class="reference internal" href="../_modules/torch/ao/quantization/fx/custom_config.html#FuseCustomConfig"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/fx/custom_config.py#L471"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.fx.custom_config.FuseCustomConfig" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.fx.custom_config.</span></span><span class="sig-name descname"><span class="pre">FuseCustomConfig</span></span><a class="reference internal" href="../_modules/torch/ao/quantization/fx/custom_config.html#FuseCustomConfig"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/quantization/fx/custom_config.py#L471"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.fx.custom_config.FuseCustomConfig" title="Permalink to this definition">¶</a></dt>
 <dd><p>Custom configuration for <a class="reference internal" href="torch.ao.quantization.quantize_fx.fuse_fx.html#torch.ao.quantization.quantize_fx.fuse_fx" title="torch.ao.quantization.quantize_fx.fuse_fx"><code class="xref py py-func docutils literal notranslate"><span class="pre">fuse_fx()</span></code></a>.</p>
 <p>Example usage:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">fuse_custom_config</span> <span class="o">=</span> <span class="n">FuseCustomConfig</span><span class="p">()</span><span class="o">.</span><span class="n">set_preserved_attributes</span><span class="p">([</span><span class="s2">&quot;attr1&quot;</span><span class="p">,</span> <span class="s2">&quot;attr2&quot;</span><span class="p">])</span>
@@ -624,7 +624,7 @@ <h1>FuseCustomConfig<a class="headerlink" href="#fusecustomconfig" title="Permal
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.ao.quantization.fx.custom_config.FuseCustomConfig.from_dict">
-<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">fuse_custom_config_dict</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/fx/custom_config.html#FuseCustomConfig.from_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/fx/custom_config.py#L496"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.fx.custom_config.FuseCustomConfig.from_dict" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">fuse_custom_config_dict</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/fx/custom_config.html#FuseCustomConfig.from_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/quantization/fx/custom_config.py#L496"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.fx.custom_config.FuseCustomConfig.from_dict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Create a <code class="docutils literal notranslate"><span class="pre">ConvertCustomConfig</span></code> from a dictionary with the following items:</p>
 <blockquote>
 <div><p>“preserved_attributes”: a list of attributes that persist even if they are not used in <code class="docutils literal notranslate"><span class="pre">forward</span></code></p>
@@ -639,7 +639,7 @@ <h1>FuseCustomConfig<a class="headerlink" href="#fusecustomconfig" title="Permal
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.ao.quantization.fx.custom_config.FuseCustomConfig.set_preserved_attributes">
-<span class="sig-name descname"><span class="pre">set_preserved_attributes</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">attributes</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/fx/custom_config.html#FuseCustomConfig.set_preserved_attributes"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/fx/custom_config.py#L487"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.fx.custom_config.FuseCustomConfig.set_preserved_attributes" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">set_preserved_attributes</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">attributes</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/fx/custom_config.html#FuseCustomConfig.set_preserved_attributes"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/quantization/fx/custom_config.py#L487"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.fx.custom_config.FuseCustomConfig.set_preserved_attributes" title="Permalink to this definition">¶</a></dt>
 <dd><p>Set the names of the attributes that will persist in the graph module even if they are not used in
 the model’s <code class="docutils literal notranslate"><span class="pre">forward</span></code> method.</p>
 <dl class="field-list simple">
@@ -651,7 +651,7 @@ <h1>FuseCustomConfig<a class="headerlink" href="#fusecustomconfig" title="Permal
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.ao.quantization.fx.custom_config.FuseCustomConfig.to_dict">
-<span class="sig-name descname"><span class="pre">to_dict</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/fx/custom_config.html#FuseCustomConfig.to_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/fx/custom_config.py#L511"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.fx.custom_config.FuseCustomConfig.to_dict" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">to_dict</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/fx/custom_config.html#FuseCustomConfig.to_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/quantization/fx/custom_config.py#L511"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.fx.custom_config.FuseCustomConfig.to_dict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Convert this <code class="docutils literal notranslate"><span class="pre">FuseCustomConfig</span></code> to a dictionary with the items described in
 <a class="reference internal" href="torch.ao.quantization.fx.custom_config.ConvertCustomConfig.html#torch.ao.quantization.fx.custom_config.ConvertCustomConfig.from_dict" title="torch.ao.quantization.fx.custom_config.ConvertCustomConfig.from_dict"><code class="xref py py-func docutils literal notranslate"><span class="pre">from_dict()</span></code></a>.</p>
 <dl class="field-list simple">
diff --git a/main/generated/torch.ao.quantization.fx.custom_config.PrepareCustomConfig.html b/main/generated/torch.ao.quantization.fx.custom_config.PrepareCustomConfig.html
index d333abab0aa..1600bd5ce21 100644
--- a/main/generated/torch.ao.quantization.fx.custom_config.PrepareCustomConfig.html
+++ b/main/generated/torch.ao.quantization.fx.custom_config.PrepareCustomConfig.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>PrepareCustomConfig<a class="headerlink" href="#preparecustomconfig" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.quantization.fx.custom_config.PrepareCustomConfig">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.fx.custom_config.</span></span><span class="sig-name descname"><span class="pre">PrepareCustomConfig</span></span><a class="reference internal" href="../_modules/torch/ao/quantization/fx/custom_config.html#PrepareCustomConfig"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/fx/custom_config.py#L46"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.fx.custom_config.PrepareCustomConfig" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.fx.custom_config.</span></span><span class="sig-name descname"><span class="pre">PrepareCustomConfig</span></span><a class="reference internal" href="../_modules/torch/ao/quantization/fx/custom_config.html#PrepareCustomConfig"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/quantization/fx/custom_config.py#L46"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.fx.custom_config.PrepareCustomConfig" title="Permalink to this definition">¶</a></dt>
 <dd><p>Custom configuration for <a class="reference internal" href="torch.ao.quantization.quantize_fx.prepare_fx.html#torch.ao.quantization.quantize_fx.prepare_fx" title="torch.ao.quantization.quantize_fx.prepare_fx"><code class="xref py py-func docutils literal notranslate"><span class="pre">prepare_fx()</span></code></a> and
 <a class="reference internal" href="torch.ao.quantization.quantize_fx.prepare_qat_fx.html#torch.ao.quantization.quantize_fx.prepare_qat_fx" title="torch.ao.quantization.quantize_fx.prepare_qat_fx"><code class="xref py py-func docutils literal notranslate"><span class="pre">prepare_qat_fx()</span></code></a>.</p>
 <p>Example usage:</p>
@@ -625,7 +625,7 @@ <h1>PrepareCustomConfig<a class="headerlink" href="#preparecustomconfig" title="
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.ao.quantization.fx.custom_config.PrepareCustomConfig.from_dict">
-<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">prepare_custom_config_dict</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/fx/custom_config.html#PrepareCustomConfig.from_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/fx/custom_config.py#L184"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.fx.custom_config.PrepareCustomConfig.from_dict" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">prepare_custom_config_dict</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/fx/custom_config.html#PrepareCustomConfig.from_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/quantization/fx/custom_config.py#L184"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.fx.custom_config.PrepareCustomConfig.from_dict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Create a <code class="docutils literal notranslate"><span class="pre">PrepareCustomConfig</span></code> from a dictionary with the following items:</p>
 <blockquote>
 <div><p>“standalone_module_name”: a list of (module_name, qconfig_mapping, example_inputs,
@@ -651,7 +651,7 @@ <h1>PrepareCustomConfig<a class="headerlink" href="#preparecustomconfig" title="
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.ao.quantization.fx.custom_config.PrepareCustomConfig.set_float_to_observed_mapping">
-<span class="sig-name descname"><span class="pre">set_float_to_observed_mapping</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">float_class</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">observed_class</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">quant_type</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">QuantType.STATIC</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/fx/custom_config.html#PrepareCustomConfig.set_float_to_observed_mapping"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/fx/custom_config.py#L120"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.fx.custom_config.PrepareCustomConfig.set_float_to_observed_mapping" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">set_float_to_observed_mapping</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">float_class</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">observed_class</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">quant_type</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">QuantType.STATIC</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/fx/custom_config.html#PrepareCustomConfig.set_float_to_observed_mapping"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/quantization/fx/custom_config.py#L120"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.fx.custom_config.PrepareCustomConfig.set_float_to_observed_mapping" title="Permalink to this definition">¶</a></dt>
 <dd><p>Set the mapping from a custom float module class to a custom observed module class.</p>
 <p>The observed module class must have a <code class="docutils literal notranslate"><span class="pre">from_float</span></code> class method that converts the float module class
 to the observed module class. This is currently only supported for static quantization.</p>
@@ -664,7 +664,7 @@ <h1>PrepareCustomConfig<a class="headerlink" href="#preparecustomconfig" title="
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.ao.quantization.fx.custom_config.PrepareCustomConfig.set_input_quantized_indexes">
-<span class="sig-name descname"><span class="pre">set_input_quantized_indexes</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">indexes</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/fx/custom_config.html#PrepareCustomConfig.set_input_quantized_indexes"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/fx/custom_config.py#L159"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.fx.custom_config.PrepareCustomConfig.set_input_quantized_indexes" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">set_input_quantized_indexes</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">indexes</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/fx/custom_config.html#PrepareCustomConfig.set_input_quantized_indexes"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/quantization/fx/custom_config.py#L159"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.fx.custom_config.PrepareCustomConfig.set_input_quantized_indexes" title="Permalink to this definition">¶</a></dt>
 <dd><p>Set the indexes of the inputs of the graph that should be quantized.
 Inputs are otherwise assumed to be in fp32 by default instead.</p>
 <dl class="field-list simple">
@@ -676,7 +676,7 @@ <h1>PrepareCustomConfig<a class="headerlink" href="#preparecustomconfig" title="
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.ao.quantization.fx.custom_config.PrepareCustomConfig.set_non_traceable_module_classes">
-<span class="sig-name descname"><span class="pre">set_non_traceable_module_classes</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module_classes</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/fx/custom_config.html#PrepareCustomConfig.set_non_traceable_module_classes"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/fx/custom_config.py#L150"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.fx.custom_config.PrepareCustomConfig.set_non_traceable_module_classes" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">set_non_traceable_module_classes</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module_classes</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/fx/custom_config.html#PrepareCustomConfig.set_non_traceable_module_classes"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/quantization/fx/custom_config.py#L150"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.fx.custom_config.PrepareCustomConfig.set_non_traceable_module_classes" title="Permalink to this definition">¶</a></dt>
 <dd><p>Set the modules that are not symbolically traceable, identified by class.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -687,7 +687,7 @@ <h1>PrepareCustomConfig<a class="headerlink" href="#preparecustomconfig" title="
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.ao.quantization.fx.custom_config.PrepareCustomConfig.set_non_traceable_module_names">
-<span class="sig-name descname"><span class="pre">set_non_traceable_module_names</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module_names</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/fx/custom_config.html#PrepareCustomConfig.set_non_traceable_module_names"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/fx/custom_config.py#L141"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.fx.custom_config.PrepareCustomConfig.set_non_traceable_module_names" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">set_non_traceable_module_names</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module_names</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/fx/custom_config.html#PrepareCustomConfig.set_non_traceable_module_names"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/quantization/fx/custom_config.py#L141"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.fx.custom_config.PrepareCustomConfig.set_non_traceable_module_names" title="Permalink to this definition">¶</a></dt>
 <dd><p>Set the modules that are not symbolically traceable, identified by name.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -698,7 +698,7 @@ <h1>PrepareCustomConfig<a class="headerlink" href="#preparecustomconfig" title="
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.ao.quantization.fx.custom_config.PrepareCustomConfig.set_output_quantized_indexes">
-<span class="sig-name descname"><span class="pre">set_output_quantized_indexes</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">indexes</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/fx/custom_config.html#PrepareCustomConfig.set_output_quantized_indexes"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/fx/custom_config.py#L167"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.fx.custom_config.PrepareCustomConfig.set_output_quantized_indexes" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">set_output_quantized_indexes</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">indexes</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/fx/custom_config.html#PrepareCustomConfig.set_output_quantized_indexes"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/quantization/fx/custom_config.py#L167"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.fx.custom_config.PrepareCustomConfig.set_output_quantized_indexes" title="Permalink to this definition">¶</a></dt>
 <dd><p>Set the indexes of the outputs of the graph that should be quantized.
 Outputs are otherwise assumed to be in fp32 by default instead.</p>
 <dl class="field-list simple">
@@ -710,7 +710,7 @@ <h1>PrepareCustomConfig<a class="headerlink" href="#preparecustomconfig" title="
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.ao.quantization.fx.custom_config.PrepareCustomConfig.set_preserved_attributes">
-<span class="sig-name descname"><span class="pre">set_preserved_attributes</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">attributes</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/fx/custom_config.html#PrepareCustomConfig.set_preserved_attributes"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/fx/custom_config.py#L175"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.fx.custom_config.PrepareCustomConfig.set_preserved_attributes" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">set_preserved_attributes</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">attributes</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/fx/custom_config.html#PrepareCustomConfig.set_preserved_attributes"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/quantization/fx/custom_config.py#L175"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.fx.custom_config.PrepareCustomConfig.set_preserved_attributes" title="Permalink to this definition">¶</a></dt>
 <dd><p>Set the names of the attributes that will persist in the graph module even if they are not used in
 the model’s <code class="docutils literal notranslate"><span class="pre">forward</span></code> method.</p>
 <dl class="field-list simple">
@@ -722,7 +722,7 @@ <h1>PrepareCustomConfig<a class="headerlink" href="#preparecustomconfig" title="
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.ao.quantization.fx.custom_config.PrepareCustomConfig.set_standalone_module_class">
-<span class="sig-name descname"><span class="pre">set_standalone_module_class</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module_class</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">qconfig_mapping</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">example_inputs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepare_custom_config</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">backend_config</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/fx/custom_config.html#PrepareCustomConfig.set_standalone_module_class"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/fx/custom_config.py#L100"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.fx.custom_config.PrepareCustomConfig.set_standalone_module_class" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">set_standalone_module_class</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module_class</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">qconfig_mapping</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">example_inputs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepare_custom_config</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">backend_config</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/fx/custom_config.html#PrepareCustomConfig.set_standalone_module_class"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/quantization/fx/custom_config.py#L100"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.fx.custom_config.PrepareCustomConfig.set_standalone_module_class" title="Permalink to this definition">¶</a></dt>
 <dd><p>Set the configuration for running a standalone module identified by <code class="docutils literal notranslate"><span class="pre">module_class</span></code>.</p>
 <p>If <code class="docutils literal notranslate"><span class="pre">qconfig_mapping</span></code> is None, the parent <code class="docutils literal notranslate"><span class="pre">qconfig_mapping</span></code> will be used instead.
 If <code class="docutils literal notranslate"><span class="pre">prepare_custom_config</span></code> is None, an empty <code class="docutils literal notranslate"><span class="pre">PrepareCustomConfig</span></code> will be used.
@@ -736,7 +736,7 @@ <h1>PrepareCustomConfig<a class="headerlink" href="#preparecustomconfig" title="
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.ao.quantization.fx.custom_config.PrepareCustomConfig.set_standalone_module_name">
-<span class="sig-name descname"><span class="pre">set_standalone_module_name</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module_name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">qconfig_mapping</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">example_inputs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepare_custom_config</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">backend_config</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/fx/custom_config.html#PrepareCustomConfig.set_standalone_module_name"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/fx/custom_config.py#L80"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.fx.custom_config.PrepareCustomConfig.set_standalone_module_name" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">set_standalone_module_name</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module_name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">qconfig_mapping</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">example_inputs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepare_custom_config</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">backend_config</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/fx/custom_config.html#PrepareCustomConfig.set_standalone_module_name"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/quantization/fx/custom_config.py#L80"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.fx.custom_config.PrepareCustomConfig.set_standalone_module_name" title="Permalink to this definition">¶</a></dt>
 <dd><p>Set the configuration for running a standalone module identified by <code class="docutils literal notranslate"><span class="pre">module_name</span></code>.</p>
 <p>If <code class="docutils literal notranslate"><span class="pre">qconfig_mapping</span></code> is None, the parent <code class="docutils literal notranslate"><span class="pre">qconfig_mapping</span></code> will be used instead.
 If <code class="docutils literal notranslate"><span class="pre">prepare_custom_config</span></code> is None, an empty <code class="docutils literal notranslate"><span class="pre">PrepareCustomConfig</span></code> will be used.
@@ -750,7 +750,7 @@ <h1>PrepareCustomConfig<a class="headerlink" href="#preparecustomconfig" title="
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.ao.quantization.fx.custom_config.PrepareCustomConfig.to_dict">
-<span class="sig-name descname"><span class="pre">to_dict</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/fx/custom_config.html#PrepareCustomConfig.to_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/fx/custom_config.py#L320"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.fx.custom_config.PrepareCustomConfig.to_dict" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">to_dict</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/fx/custom_config.html#PrepareCustomConfig.to_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/quantization/fx/custom_config.py#L320"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.fx.custom_config.PrepareCustomConfig.to_dict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Convert this <code class="docutils literal notranslate"><span class="pre">PrepareCustomConfig</span></code> to a dictionary with the items described in
 <a class="reference internal" href="#torch.ao.quantization.fx.custom_config.PrepareCustomConfig.from_dict" title="torch.ao.quantization.fx.custom_config.PrepareCustomConfig.from_dict"><code class="xref py py-func docutils literal notranslate"><span class="pre">from_dict()</span></code></a>.</p>
 <dl class="field-list simple">
diff --git a/main/generated/torch.ao.quantization.fx.custom_config.StandaloneModuleConfigEntry.html b/main/generated/torch.ao.quantization.fx.custom_config.StandaloneModuleConfigEntry.html
index 1db8f94a954..a6ef5e9386a 100644
--- a/main/generated/torch.ao.quantization.fx.custom_config.StandaloneModuleConfigEntry.html
+++ b/main/generated/torch.ao.quantization.fx.custom_config.StandaloneModuleConfigEntry.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>StandaloneModuleConfigEntry<a class="headerlink" href="#standalonemoduleconfigentry" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.quantization.fx.custom_config.StandaloneModuleConfigEntry">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.fx.custom_config.</span></span><span class="sig-name descname"><span class="pre">StandaloneModuleConfigEntry</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">qconfig_mapping</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="s"><span class="pre">'Optional[QConfigMapping]'</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">example_inputs</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="s"><span class="pre">'tuple[Any,</span> <span class="pre">...]'</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepare_custom_config</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="s"><span class="pre">'Optional[PrepareCustomConfig]'</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">backend_config</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="s"><span class="pre">'Optional[BackendConfig]'</span></span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/fx/custom_config.html#StandaloneModuleConfigEntry"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/fx/custom_config.py#L36"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.fx.custom_config.StandaloneModuleConfigEntry" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.fx.custom_config.</span></span><span class="sig-name descname"><span class="pre">StandaloneModuleConfigEntry</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">qconfig_mapping</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="s"><span class="pre">'Optional[QConfigMapping]'</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">example_inputs</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="s"><span class="pre">'tuple[Any,</span> <span class="pre">...]'</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepare_custom_config</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="s"><span class="pre">'Optional[PrepareCustomConfig]'</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">backend_config</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="s"><span class="pre">'Optional[BackendConfig]'</span></span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/fx/custom_config.html#StandaloneModuleConfigEntry"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/quantization/fx/custom_config.py#L36"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.fx.custom_config.StandaloneModuleConfigEntry" title="Permalink to this definition">¶</a></dt>
 <dd><dl class="field-list simple">
 </dl>
 </dd></dl>
diff --git a/main/generated/torch.ao.quantization.generate_numeric_debug_handle.html b/main/generated/torch.ao.quantization.generate_numeric_debug_handle.html
index 461900a7a3c..98435a26f8b 100644
--- a/main/generated/torch.ao.quantization.generate_numeric_debug_handle.html
+++ b/main/generated/torch.ao.quantization.generate_numeric_debug_handle.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>generate_numeric_debug_handle<a class="headerlink" href="#generate-numeric-debug-handle" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.quantization.generate_numeric_debug_handle">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.</span></span><span class="sig-name descname"><span class="pre">generate_numeric_debug_handle</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">ep</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/pt2e/_numeric_debugger.py#L21"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.generate_numeric_debug_handle" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.</span></span><span class="sig-name descname"><span class="pre">generate_numeric_debug_handle</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">ep</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/quantization/pt2e/_numeric_debugger.py#L21"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.generate_numeric_debug_handle" title="Permalink to this definition">¶</a></dt>
 <dd><p>Attach numeric_debug_handle_id for all nodes in the graph module of the given
 ExportedProgram, like conv2d, squeeze, conv1d, etc, except for placeholder.
 Notice that nodes like getattr are out of scope since they are not in the graph.</p>
diff --git a/main/generated/torch.ao.quantization.observer.AffineQuantizedObserverBase.html b/main/generated/torch.ao.quantization.observer.AffineQuantizedObserverBase.html
index aa22f205e50..a51fd5f6c0d 100644
--- a/main/generated/torch.ao.quantization.observer.AffineQuantizedObserverBase.html
+++ b/main/generated/torch.ao.quantization.observer.AffineQuantizedObserverBase.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>AffineQuantizedObserverBase<a class="headerlink" href="#affinequantizedobserverbase" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.quantization.observer.AffineQuantizedObserverBase">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.observer.</span></span><span class="sig-name descname"><span class="pre">AffineQuantizedObserverBase</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mapping_type</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target_dtype</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">granularity</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">quant_min</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">quant_max</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale_dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">zero_point_dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">preserve_zero</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">zero_point_domain</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">ZeroPointDomain.INT</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/observer.html#AffineQuantizedObserverBase"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/observer.py#L1797"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.observer.AffineQuantizedObserverBase" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.observer.</span></span><span class="sig-name descname"><span class="pre">AffineQuantizedObserverBase</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mapping_type</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target_dtype</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">granularity</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">quant_min</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">quant_max</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale_dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">zero_point_dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">preserve_zero</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">zero_point_domain</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">ZeroPointDomain.INT</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/observer.html#AffineQuantizedObserverBase"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/quantization/observer.py#L1797"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.observer.AffineQuantizedObserverBase" title="Permalink to this definition">¶</a></dt>
 <dd><p>Observer module for affine quantization (<a class="reference external" href="https://github.com/pytorch/ao/tree/main/torchao/quantization#affine-quantization">https://github.com/pytorch/ao/tree/main/torchao/quantization#affine-quantization</a>)</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -628,7 +628,7 @@ <h1>AffineQuantizedObserverBase<a class="headerlink" href="#affinequantizedobser
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.ao.quantization.observer.AffineQuantizedObserverBase.calculate_qparams">
-<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">calculate_qparams</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/observer.html#AffineQuantizedObserverBase.calculate_qparams"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/observer.py#L1847"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.observer.AffineQuantizedObserverBase.calculate_qparams" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">calculate_qparams</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/observer.html#AffineQuantizedObserverBase.calculate_qparams"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/quantization/observer.py#L1847"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.observer.AffineQuantizedObserverBase.calculate_qparams" title="Permalink to this definition">¶</a></dt>
 <dd><p>Calculate quantization parameter based on the stats attached to the observer module
 and returns a tuple of scale and zero_point Tensor</p>
 <dl class="field-list simple">
@@ -640,7 +640,7 @@ <h1>AffineQuantizedObserverBase<a class="headerlink" href="#affinequantizedobser
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.ao.quantization.observer.AffineQuantizedObserverBase.forward">
-<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/observer.html#AffineQuantizedObserverBase.forward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/observer.py#L1841"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.observer.AffineQuantizedObserverBase.forward" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/observer.html#AffineQuantizedObserverBase.forward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/quantization/observer.py#L1841"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.observer.AffineQuantizedObserverBase.forward" title="Permalink to this definition">¶</a></dt>
 <dd><p>forward function should take the input tensor
 and updates internal stats and return the original input Tensor</p>
 <dl class="field-list simple">
@@ -652,7 +652,7 @@ <h1>AffineQuantizedObserverBase<a class="headerlink" href="#affinequantizedobser
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.ao.quantization.observer.AffineQuantizedObserverBase.with_args">
-<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">with_args</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/observer.py#L100"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.observer.AffineQuantizedObserverBase.with_args" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">with_args</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/quantization/observer.py#L100"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.observer.AffineQuantizedObserverBase.with_args" title="Permalink to this definition">¶</a></dt>
 <dd><p>Wrapper that allows creation of class factories.</p>
 <p>This can be useful when there is a need to create classes with the same
 constructor arguments, but different instances. Can be used in conjunction with
diff --git a/main/generated/torch.ao.quantization.observer.Granularity.html b/main/generated/torch.ao.quantization.observer.Granularity.html
index 0ad5be78cac..30c5a399640 100644
--- a/main/generated/torch.ao.quantization.observer.Granularity.html
+++ b/main/generated/torch.ao.quantization.observer.Granularity.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>Granularity<a class="headerlink" href="#granularity" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.quantization.observer.Granularity">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.observer.</span></span><span class="sig-name descname"><span class="pre">Granularity</span></span><a class="reference internal" href="../_modules/torch/ao/quantization/observer.html#Granularity"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/observer.py#L1664"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.observer.Granularity" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.observer.</span></span><span class="sig-name descname"><span class="pre">Granularity</span></span><a class="reference internal" href="../_modules/torch/ao/quantization/observer.html#Granularity"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/quantization/observer.py#L1664"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.observer.Granularity" title="Permalink to this definition">¶</a></dt>
 <dd><p>Base class for representing the granularity of quantization.</p>
 <p>This class serves as a parent for specific granularity types used in
 quantization operations, such as per-tensor or per-axis quantization.</p>
diff --git a/main/generated/torch.ao.quantization.observer.HistogramObserver.html b/main/generated/torch.ao.quantization.observer.HistogramObserver.html
index cd0ff0c109e..569f6a157e6 100644
--- a/main/generated/torch.ao.quantization.observer.HistogramObserver.html
+++ b/main/generated/torch.ao.quantization.observer.HistogramObserver.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>HistogramObserver<a class="headerlink" href="#histogramobserver" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.quantization.observer.HistogramObserver">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.observer.</span></span><span class="sig-name descname"><span class="pre">HistogramObserver</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">bins</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2048</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.quint8</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">qscheme</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.per_tensor_affine</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduce_range</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">quant_min</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">quant_max</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">factory_kwargs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.1920928955078125e-07</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">is_dynamic</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/observer.html#HistogramObserver"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/observer.py#L974"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.observer.HistogramObserver" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.observer.</span></span><span class="sig-name descname"><span class="pre">HistogramObserver</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">bins</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2048</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.quint8</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">qscheme</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.per_tensor_affine</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduce_range</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">quant_min</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">quant_max</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">factory_kwargs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.1920928955078125e-07</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">is_dynamic</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/observer.html#HistogramObserver"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/quantization/observer.py#L974"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.observer.HistogramObserver" title="Permalink to this definition">¶</a></dt>
 <dd><p>The module records the running histogram of tensor values along with
 min/max values. <code class="docutils literal notranslate"><span class="pre">calculate_qparams</span></code> will calculate scale and zero_point.</p>
 <dl class="field-list simple">
diff --git a/main/generated/torch.ao.quantization.observer.MappingType.html b/main/generated/torch.ao.quantization.observer.MappingType.html
index f9043ddc8b8..3b743d78ff2 100644
--- a/main/generated/torch.ao.quantization.observer.MappingType.html
+++ b/main/generated/torch.ao.quantization.observer.MappingType.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>MappingType<a class="headerlink" href="#mappingtype" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.quantization.observer.MappingType">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.observer.</span></span><span class="sig-name descname"><span class="pre">MappingType</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/observer.html#MappingType"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/observer.py#L1611"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.observer.MappingType" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.observer.</span></span><span class="sig-name descname"><span class="pre">MappingType</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/observer.html#MappingType"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/quantization/observer.py#L1611"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.observer.MappingType" title="Permalink to this definition">¶</a></dt>
 <dd><p>How floating point number is mapped to integer number</p>
 <p>symmetric mapping means floating point range is symmetrically mapped to integer range
 let’s say we have floating point range (-3.5, 10.2) and integer range (-8, 7) (int4)
diff --git a/main/generated/torch.ao.quantization.observer.MinMaxObserver.html b/main/generated/torch.ao.quantization.observer.MinMaxObserver.html
index 1ec8d04d27e..8616ed240cc 100644
--- a/main/generated/torch.ao.quantization.observer.MinMaxObserver.html
+++ b/main/generated/torch.ao.quantization.observer.MinMaxObserver.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>MinMaxObserver<a class="headerlink" href="#minmaxobserver" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.quantization.observer.MinMaxObserver">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.observer.</span></span><span class="sig-name descname"><span class="pre">MinMaxObserver</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.quint8</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">qscheme</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.per_tensor_affine</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduce_range</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">quant_min</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">quant_max</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">factory_kwargs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.1920928955078125e-07</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">is_dynamic</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/observer.html#MinMaxObserver"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/observer.py#L431"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.observer.MinMaxObserver" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.observer.</span></span><span class="sig-name descname"><span class="pre">MinMaxObserver</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.quint8</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">qscheme</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.per_tensor_affine</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduce_range</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">quant_min</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">quant_max</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">factory_kwargs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.1920928955078125e-07</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">is_dynamic</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/observer.html#MinMaxObserver"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/quantization/observer.py#L431"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.observer.MinMaxObserver" title="Permalink to this definition">¶</a></dt>
 <dd><p>Observer module for computing the quantization parameters based on the
 running min and max values.</p>
 <p>This observer uses the tensor min/max statistics to compute the quantization
@@ -674,19 +674,19 @@ <h1>MinMaxObserver<a class="headerlink" href="#minmaxobserver" title="Permalink
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.ao.quantization.observer.MinMaxObserver.calculate_qparams">
-<span class="sig-name descname"><span class="pre">calculate_qparams</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/observer.html#MinMaxObserver.calculate_qparams"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/observer.py#L561"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.observer.MinMaxObserver.calculate_qparams" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">calculate_qparams</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/observer.html#MinMaxObserver.calculate_qparams"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/quantization/observer.py#L561"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.observer.MinMaxObserver.calculate_qparams" title="Permalink to this definition">¶</a></dt>
 <dd><p>Calculates the quantization parameters.</p>
 </dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.ao.quantization.observer.MinMaxObserver.forward">
-<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x_orig</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/observer.html#MinMaxObserver.forward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/observer.py#L548"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.observer.MinMaxObserver.forward" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x_orig</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/observer.html#MinMaxObserver.forward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/quantization/observer.py#L548"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.observer.MinMaxObserver.forward" title="Permalink to this definition">¶</a></dt>
 <dd><p>Records the running minimum and maximum of <code class="docutils literal notranslate"><span class="pre">x</span></code>.</p>
 </dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.ao.quantization.observer.MinMaxObserver.reset_min_max_vals">
-<span class="sig-name descname"><span class="pre">reset_min_max_vals</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/observer.html#MinMaxObserver.reset_min_max_vals"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/observer.py#L570"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.observer.MinMaxObserver.reset_min_max_vals" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">reset_min_max_vals</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/observer.html#MinMaxObserver.reset_min_max_vals"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/quantization/observer.py#L570"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.observer.MinMaxObserver.reset_min_max_vals" title="Permalink to this definition">¶</a></dt>
 <dd><p>Resets the min/max values.</p>
 </dd></dl>
 
diff --git a/main/generated/torch.ao.quantization.observer.MovingAverageMinMaxObserver.html b/main/generated/torch.ao.quantization.observer.MovingAverageMinMaxObserver.html
index a8b956bdab3..ce380f01c38 100644
--- a/main/generated/torch.ao.quantization.observer.MovingAverageMinMaxObserver.html
+++ b/main/generated/torch.ao.quantization.observer.MovingAverageMinMaxObserver.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>MovingAverageMinMaxObserver<a class="headerlink" href="#movingaverageminmaxobserver" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.quantization.observer.MovingAverageMinMaxObserver">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.observer.</span></span><span class="sig-name descname"><span class="pre">MovingAverageMinMaxObserver</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">averaging_constant</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.01</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.quint8</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">qscheme</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.per_tensor_affine</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduce_range</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">quant_min</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">quant_max</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.1920928955078125e-07</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">is_dynamic</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/observer.html#MovingAverageMinMaxObserver"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/observer.py#L577"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.observer.MovingAverageMinMaxObserver" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.observer.</span></span><span class="sig-name descname"><span class="pre">MovingAverageMinMaxObserver</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">averaging_constant</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.01</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.quint8</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">qscheme</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.per_tensor_affine</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduce_range</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">quant_min</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">quant_max</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.1920928955078125e-07</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">is_dynamic</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/observer.html#MovingAverageMinMaxObserver"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/quantization/observer.py#L577"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.observer.MovingAverageMinMaxObserver" title="Permalink to this definition">¶</a></dt>
 <dd><p>Observer module for computing the quantization parameters based on the
 moving average of the min and max values.</p>
 <p>This observer computes the quantization parameters based on the moving
diff --git a/main/generated/torch.ao.quantization.observer.MovingAveragePerChannelMinMaxObserver.html b/main/generated/torch.ao.quantization.observer.MovingAveragePerChannelMinMaxObserver.html
index 003b26f0954..77d07a77010 100644
--- a/main/generated/torch.ao.quantization.observer.MovingAveragePerChannelMinMaxObserver.html
+++ b/main/generated/torch.ao.quantization.observer.MovingAveragePerChannelMinMaxObserver.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>MovingAveragePerChannelMinMaxObserver<a class="headerlink" href="#movingaverageperchannelminmaxobserver" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.quantization.observer.MovingAveragePerChannelMinMaxObserver">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.observer.</span></span><span class="sig-name descname"><span class="pre">MovingAveragePerChannelMinMaxObserver</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">averaging_constant</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.01</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ch_axis</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.quint8</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">qscheme</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.per_channel_affine</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduce_range</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">quant_min</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">quant_max</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.1920928955078125e-07</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">is_dynamic</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/observer.html#MovingAveragePerChannelMinMaxObserver"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/observer.py#L884"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.observer.MovingAveragePerChannelMinMaxObserver" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.observer.</span></span><span class="sig-name descname"><span class="pre">MovingAveragePerChannelMinMaxObserver</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">averaging_constant</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.01</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ch_axis</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.quint8</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">qscheme</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.per_channel_affine</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduce_range</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">quant_min</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">quant_max</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.1920928955078125e-07</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">is_dynamic</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/observer.html#MovingAveragePerChannelMinMaxObserver"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/quantization/observer.py#L884"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.observer.MovingAveragePerChannelMinMaxObserver" title="Permalink to this definition">¶</a></dt>
 <dd><p>Observer module for computing the quantization parameters based on the
 running per channel min and max values.</p>
 <p>This observer uses the tensor min/max statistics to compute the per channel
diff --git a/main/generated/torch.ao.quantization.observer.NoopObserver.html b/main/generated/torch.ao.quantization.observer.NoopObserver.html
index 1e2f3bd8f6c..7b9ed199a92 100644
--- a/main/generated/torch.ao.quantization.observer.NoopObserver.html
+++ b/main/generated/torch.ao.quantization.observer.NoopObserver.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>NoopObserver<a class="headerlink" href="#noopobserver" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.quantization.observer.NoopObserver">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.observer.</span></span><span class="sig-name descname"><span class="pre">NoopObserver</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.float16</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">custom_op_name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/observer.html#NoopObserver"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/observer.py#L1546"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.observer.NoopObserver" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.observer.</span></span><span class="sig-name descname"><span class="pre">NoopObserver</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.float16</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">custom_op_name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/observer.html#NoopObserver"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/quantization/observer.py#L1546"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.observer.NoopObserver" title="Permalink to this definition">¶</a></dt>
 <dd><p>Observer that doesn’t do anything and just passes its configuration to the
 quantized module’s <code class="docutils literal notranslate"><span class="pre">.from_float()</span></code>.</p>
 <p>Primarily used for quantization to float16 which doesn’t require determining
diff --git a/main/generated/torch.ao.quantization.observer.ObserverBase.html b/main/generated/torch.ao.quantization.observer.ObserverBase.html
index a69c39ce442..f3162395579 100644
--- a/main/generated/torch.ao.quantization.observer.ObserverBase.html
+++ b/main/generated/torch.ao.quantization.observer.ObserverBase.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>ObserverBase<a class="headerlink" href="#observerbase" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.quantization.observer.ObserverBase">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.observer.</span></span><span class="sig-name descname"><span class="pre">ObserverBase</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dtype</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">is_dynamic</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/observer.html#ObserverBase"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/observer.py#L148"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.observer.ObserverBase" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.observer.</span></span><span class="sig-name descname"><span class="pre">ObserverBase</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dtype</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">is_dynamic</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/observer.html#ObserverBase"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/quantization/observer.py#L148"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.observer.ObserverBase" title="Permalink to this definition">¶</a></dt>
 <dd><p>Base observer Module.
 Any observer implementation should derive from this class.</p>
 <p>Concrete observers should follow the same API. In forward, they will update
@@ -633,7 +633,7 @@ <h1>ObserverBase<a class="headerlink" href="#observerbase" title="Permalink to t
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.ao.quantization.observer.ObserverBase.with_args">
-<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">with_args</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/observer.py#L100"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.observer.ObserverBase.with_args" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">with_args</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/quantization/observer.py#L100"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.observer.ObserverBase.with_args" title="Permalink to this definition">¶</a></dt>
 <dd><p>Wrapper that allows creation of class factories.</p>
 <p>This can be useful when there is a need to create classes with the same
 constructor arguments, but different instances. Can be used in conjunction with
@@ -651,7 +651,7 @@ <h1>ObserverBase<a class="headerlink" href="#observerbase" title="Permalink to t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.ao.quantization.observer.ObserverBase.with_callable_args">
-<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">with_callable_args</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/observer.py#L121"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.observer.ObserverBase.with_callable_args" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">with_callable_args</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/quantization/observer.py#L121"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.observer.ObserverBase.with_callable_args" title="Permalink to this definition">¶</a></dt>
 <dd><p>Wrapper that allows creation of class factories args that need to be
 called at construction time.</p>
 <p>This can be useful when there is a need to create classes with the same
diff --git a/main/generated/torch.ao.quantization.observer.PerAxis.html b/main/generated/torch.ao.quantization.observer.PerAxis.html
index fd144c47896..3f6fd69379d 100644
--- a/main/generated/torch.ao.quantization.observer.PerAxis.html
+++ b/main/generated/torch.ao.quantization.observer.PerAxis.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>PerAxis<a class="headerlink" href="#peraxis" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.quantization.observer.PerAxis">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.observer.</span></span><span class="sig-name descname"><span class="pre">PerAxis</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">axis</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/observer.html#PerAxis"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/observer.py#L1699"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.observer.PerAxis" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.observer.</span></span><span class="sig-name descname"><span class="pre">PerAxis</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">axis</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/observer.html#PerAxis"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/quantization/observer.py#L1699"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.observer.PerAxis" title="Permalink to this definition">¶</a></dt>
 <dd><p>Represents per-axis granularity in quantization.</p>
 <p>This granularity type calculates different quantization parameters
 along a specified axis of the tensor.</p>
diff --git a/main/generated/torch.ao.quantization.observer.PerBlock.html b/main/generated/torch.ao.quantization.observer.PerBlock.html
index 13ab8935a0a..8d6b30adc0f 100644
--- a/main/generated/torch.ao.quantization.observer.PerBlock.html
+++ b/main/generated/torch.ao.quantization.observer.PerBlock.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>PerBlock<a class="headerlink" href="#perblock" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.quantization.observer.PerBlock">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.observer.</span></span><span class="sig-name descname"><span class="pre">PerBlock</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">block_size</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/observer.html#PerBlock"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/observer.py#L1674"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.observer.PerBlock" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.observer.</span></span><span class="sig-name descname"><span class="pre">PerBlock</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">block_size</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/observer.html#PerBlock"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/quantization/observer.py#L1674"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.observer.PerBlock" title="Permalink to this definition">¶</a></dt>
 <dd><p>Represents per-block granularity in quantization. See
 <code class="xref py py-func docutils literal notranslate"><span class="pre">quantize_affine()</span></code> for docs for
 <cite>block_size</cite></p>
diff --git a/main/generated/torch.ao.quantization.observer.PerChannelMinMaxObserver.html b/main/generated/torch.ao.quantization.observer.PerChannelMinMaxObserver.html
index 9cbce330fcc..5552bccf93a 100644
--- a/main/generated/torch.ao.quantization.observer.PerChannelMinMaxObserver.html
+++ b/main/generated/torch.ao.quantization.observer.PerChannelMinMaxObserver.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>PerChannelMinMaxObserver<a class="headerlink" href="#perchannelminmaxobserver" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.quantization.observer.PerChannelMinMaxObserver">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.observer.</span></span><span class="sig-name descname"><span class="pre">PerChannelMinMaxObserver</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">ch_axis</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.quint8</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">qscheme</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.per_channel_affine</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduce_range</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">quant_min</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">quant_max</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">factory_kwargs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.1920928955078125e-07</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">is_dynamic</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/observer.html#PerChannelMinMaxObserver"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/observer.py#L676"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.observer.PerChannelMinMaxObserver" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.observer.</span></span><span class="sig-name descname"><span class="pre">PerChannelMinMaxObserver</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">ch_axis</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.quint8</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">qscheme</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.per_channel_affine</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduce_range</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">quant_min</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">quant_max</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">factory_kwargs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.1920928955078125e-07</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">is_dynamic</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/observer.html#PerChannelMinMaxObserver"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/quantization/observer.py#L676"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.observer.PerChannelMinMaxObserver" title="Permalink to this definition">¶</a></dt>
 <dd><p>Observer module for computing the quantization parameters based on the
 running per channel min and max values.</p>
 <p>This observer uses the tensor min/max statistics to compute the per channel
@@ -646,7 +646,7 @@ <h1>PerChannelMinMaxObserver<a class="headerlink" href="#perchannelminmaxobserve
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.ao.quantization.observer.PerChannelMinMaxObserver.reset_min_max_vals">
-<span class="sig-name descname"><span class="pre">reset_min_max_vals</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/observer.html#PerChannelMinMaxObserver.reset_min_max_vals"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/observer.py#L870"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.observer.PerChannelMinMaxObserver.reset_min_max_vals" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">reset_min_max_vals</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/observer.html#PerChannelMinMaxObserver.reset_min_max_vals"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/quantization/observer.py#L870"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.observer.PerChannelMinMaxObserver.reset_min_max_vals" title="Permalink to this definition">¶</a></dt>
 <dd><p>Resets the min/max values.</p>
 </dd></dl>
 
diff --git a/main/generated/torch.ao.quantization.observer.PerGroup.html b/main/generated/torch.ao.quantization.observer.PerGroup.html
index 5ffa94a6123..247aa6142b5 100644
--- a/main/generated/torch.ao.quantization.observer.PerGroup.html
+++ b/main/generated/torch.ao.quantization.observer.PerGroup.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>PerGroup<a class="headerlink" href="#pergroup" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.quantization.observer.PerGroup">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.observer.</span></span><span class="sig-name descname"><span class="pre">PerGroup</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">group_size</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/observer.html#PerGroup"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/observer.py#L1718"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.observer.PerGroup" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.observer.</span></span><span class="sig-name descname"><span class="pre">PerGroup</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">group_size</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/observer.html#PerGroup"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/quantization/observer.py#L1718"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.observer.PerGroup" title="Permalink to this definition">¶</a></dt>
 <dd><p>Represents per-channel group granularity in quantization.</p>
 <p>This granularity type calculates different quantization parameters
 for each group of &lt;group_size&gt; elements.</p>
diff --git a/main/generated/torch.ao.quantization.observer.PerRow.html b/main/generated/torch.ao.quantization.observer.PerRow.html
index 081460d9770..7776067eadd 100644
--- a/main/generated/torch.ao.quantization.observer.PerRow.html
+++ b/main/generated/torch.ao.quantization.observer.PerRow.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>PerRow<a class="headerlink" href="#perrow" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.quantization.observer.PerRow">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.observer.</span></span><span class="sig-name descname"><span class="pre">PerRow</span></span><a class="reference internal" href="../_modules/torch/ao/quantization/observer.html#PerRow"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/observer.py#L1739"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.observer.PerRow" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.observer.</span></span><span class="sig-name descname"><span class="pre">PerRow</span></span><a class="reference internal" href="../_modules/torch/ao/quantization/observer.html#PerRow"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/quantization/observer.py#L1739"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.observer.PerRow" title="Permalink to this definition">¶</a></dt>
 <dd><p>Represents row-wise granularity in quantization.</p>
 <p>This is a special case of per-axis quantization and is unique to Float8 matmuls
 where the input is quantized with a block_size of (1, …, input.shape[-1]). And the weight
diff --git a/main/generated/torch.ao.quantization.observer.PerTensor.html b/main/generated/torch.ao.quantization.observer.PerTensor.html
index c39a62c4055..af858f88504 100644
--- a/main/generated/torch.ao.quantization.observer.PerTensor.html
+++ b/main/generated/torch.ao.quantization.observer.PerTensor.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>PerTensor<a class="headerlink" href="#pertensor" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.quantization.observer.PerTensor">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.observer.</span></span><span class="sig-name descname"><span class="pre">PerTensor</span></span><a class="reference internal" href="../_modules/torch/ao/quantization/observer.html#PerTensor"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/observer.py#L1688"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.observer.PerTensor" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.observer.</span></span><span class="sig-name descname"><span class="pre">PerTensor</span></span><a class="reference internal" href="../_modules/torch/ao/quantization/observer.html#PerTensor"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/quantization/observer.py#L1688"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.observer.PerTensor" title="Permalink to this definition">¶</a></dt>
 <dd><p>Represents per-tensor granularity in quantization.</p>
 <p>This granularity type calculates the quantization parameters
 based off the entire tensor.</p>
diff --git a/main/generated/torch.ao.quantization.observer.PerToken.html b/main/generated/torch.ao.quantization.observer.PerToken.html
index ef308bbad8a..30194045209 100644
--- a/main/generated/torch.ao.quantization.observer.PerToken.html
+++ b/main/generated/torch.ao.quantization.observer.PerToken.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>PerToken<a class="headerlink" href="#pertoken" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.quantization.observer.PerToken">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.observer.</span></span><span class="sig-name descname"><span class="pre">PerToken</span></span><a class="reference internal" href="../_modules/torch/ao/quantization/observer.html#PerToken"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/observer.py#L1749"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.observer.PerToken" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.observer.</span></span><span class="sig-name descname"><span class="pre">PerToken</span></span><a class="reference internal" href="../_modules/torch/ao/quantization/observer.html#PerToken"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/quantization/observer.py#L1749"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.observer.PerToken" title="Permalink to this definition">¶</a></dt>
 <dd><p>Represents per-token granularity in quantization.</p>
 <p>This granularity type calculates a different set of quantization parameters
 for each token, which is represented as the last dimension of the tensor.</p>
diff --git a/main/generated/torch.ao.quantization.observer.PlaceholderObserver.html b/main/generated/torch.ao.quantization.observer.PlaceholderObserver.html
index dd68296b757..896a48a8d07 100644
--- a/main/generated/torch.ao.quantization.observer.PlaceholderObserver.html
+++ b/main/generated/torch.ao.quantization.observer.PlaceholderObserver.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>PlaceholderObserver<a class="headerlink" href="#placeholderobserver" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.quantization.observer.PlaceholderObserver">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.observer.</span></span><span class="sig-name descname"><span class="pre">PlaceholderObserver</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.float32</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">custom_op_name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">compute_dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">quant_min</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">quant_max</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">qscheme</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">is_dynamic</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/observer.html#PlaceholderObserver"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/observer.py#L1445"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.observer.PlaceholderObserver" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.observer.</span></span><span class="sig-name descname"><span class="pre">PlaceholderObserver</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.float32</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">custom_op_name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">compute_dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">quant_min</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">quant_max</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">qscheme</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">is_dynamic</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/observer.html#PlaceholderObserver"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/quantization/observer.py#L1445"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.observer.PlaceholderObserver" title="Permalink to this definition">¶</a></dt>
 <dd><p>Observer that doesn’t do anything and just passes its configuration to the
 quantized module’s <code class="docutils literal notranslate"><span class="pre">.from_float()</span></code>.</p>
 <p>Can be used for quantization to float16 which doesn’t require determining
diff --git a/main/generated/torch.ao.quantization.observer.RecordingObserver.html b/main/generated/torch.ao.quantization.observer.RecordingObserver.html
index f25a8b12a3f..facc71125a6 100644
--- a/main/generated/torch.ao.quantization.observer.RecordingObserver.html
+++ b/main/generated/torch.ao.quantization.observer.RecordingObserver.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>RecordingObserver<a class="headerlink" href="#recordingobserver" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.quantization.observer.RecordingObserver">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.observer.</span></span><span class="sig-name descname"><span class="pre">RecordingObserver</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.quint8</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/observer.html#RecordingObserver"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/observer.py#L1516"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.observer.RecordingObserver" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.observer.</span></span><span class="sig-name descname"><span class="pre">RecordingObserver</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.quint8</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/observer.html#RecordingObserver"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/quantization/observer.py#L1516"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.observer.RecordingObserver" title="Permalink to this definition">¶</a></dt>
 <dd><p>The module is mainly for debug and records the tensor values during runtime.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.ao.quantization.observer.TorchAODType.html b/main/generated/torch.ao.quantization.observer.TorchAODType.html
index 5a2e9bb6385..a424828e176 100644
--- a/main/generated/torch.ao.quantization.observer.TorchAODType.html
+++ b/main/generated/torch.ao.quantization.observer.TorchAODType.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>TorchAODType<a class="headerlink" href="#torchaodtype" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.quantization.observer.TorchAODType">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.observer.</span></span><span class="sig-name descname"><span class="pre">TorchAODType</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/observer.html#TorchAODType"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/observer.py#L1648"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.observer.TorchAODType" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.observer.</span></span><span class="sig-name descname"><span class="pre">TorchAODType</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/observer.html#TorchAODType"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/quantization/observer.py#L1648"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.observer.TorchAODType" title="Permalink to this definition">¶</a></dt>
 <dd><p>Placeholder for dtypes that do not exist in PyTorch core yet.</p>
 </dd></dl>
 
diff --git a/main/generated/torch.ao.quantization.observer.ZeroPointDomain.html b/main/generated/torch.ao.quantization.observer.ZeroPointDomain.html
index a88c0eedd7b..8fc5281d3ef 100644
--- a/main/generated/torch.ao.quantization.observer.ZeroPointDomain.html
+++ b/main/generated/torch.ao.quantization.observer.ZeroPointDomain.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>ZeroPointDomain<a class="headerlink" href="#zeropointdomain" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.quantization.observer.ZeroPointDomain">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.observer.</span></span><span class="sig-name descname"><span class="pre">ZeroPointDomain</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/observer.html#ZeroPointDomain"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/observer.py#L1635"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.observer.ZeroPointDomain" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.observer.</span></span><span class="sig-name descname"><span class="pre">ZeroPointDomain</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/observer.html#ZeroPointDomain"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/quantization/observer.py#L1635"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.observer.ZeroPointDomain" title="Permalink to this definition">¶</a></dt>
 <dd><p>Enum that indicate whether zero_point is in integer domain or floating point domain</p>
 <p>integer domain: quantized_val = (float_val / scale) (integer) + zero_point (integer)
 float domain: quantized_val = (float_val - (zero_point (float) - scale * mid_point)) / scale
diff --git a/main/generated/torch.ao.quantization.observer.default_debug_observer.html b/main/generated/torch.ao.quantization.observer.default_debug_observer.html
index bd29cabb67c..04097185748 100644
--- a/main/generated/torch.ao.quantization.observer.default_debug_observer.html
+++ b/main/generated/torch.ao.quantization.observer.default_debug_observer.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>default_debug_observer<a class="headerlink" href="#default-debug-observer" title="Permalink to this heading">¶</a></h1>
 <dl class="py attribute">
 <dt class="sig sig-object py" id="torch.ao.quantization.observer.default_debug_observer">
-<span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.observer.</span></span><span class="sig-name descname"><span class="pre">default_debug_observer</span></span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/observer.py#L1516"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.observer.default_debug_observer" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.observer.</span></span><span class="sig-name descname"><span class="pre">default_debug_observer</span></span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/quantization/observer.py#L1516"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.observer.default_debug_observer" title="Permalink to this definition">¶</a></dt>
 <dd><p>Default debug-only observer.</p>
 </dd></dl>
 
diff --git a/main/generated/torch.ao.quantization.observer.default_dynamic_quant_observer.html b/main/generated/torch.ao.quantization.observer.default_dynamic_quant_observer.html
index 8208bf64bec..3a7691310d5 100644
--- a/main/generated/torch.ao.quantization.observer.default_dynamic_quant_observer.html
+++ b/main/generated/torch.ao.quantization.observer.default_dynamic_quant_observer.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.ao.quantization.observer.default_float_qparams_observer.html b/main/generated/torch.ao.quantization.observer.default_float_qparams_observer.html
index 0dbc4be1e72..2f2f0a38c49 100644
--- a/main/generated/torch.ao.quantization.observer.default_float_qparams_observer.html
+++ b/main/generated/torch.ao.quantization.observer.default_float_qparams_observer.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.ao.quantization.observer.default_histogram_observer.html b/main/generated/torch.ao.quantization.observer.default_histogram_observer.html
index bc3c31d96cf..79cd0ae537c 100644
--- a/main/generated/torch.ao.quantization.observer.default_histogram_observer.html
+++ b/main/generated/torch.ao.quantization.observer.default_histogram_observer.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.ao.quantization.observer.default_observer.html b/main/generated/torch.ao.quantization.observer.default_observer.html
index d2f4dad45d6..6ec247d4d89 100644
--- a/main/generated/torch.ao.quantization.observer.default_observer.html
+++ b/main/generated/torch.ao.quantization.observer.default_observer.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.ao.quantization.observer.default_per_channel_weight_observer.html b/main/generated/torch.ao.quantization.observer.default_per_channel_weight_observer.html
index a5fadd6f1e9..890fd1daff9 100644
--- a/main/generated/torch.ao.quantization.observer.default_per_channel_weight_observer.html
+++ b/main/generated/torch.ao.quantization.observer.default_per_channel_weight_observer.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.ao.quantization.observer.default_placeholder_observer.html b/main/generated/torch.ao.quantization.observer.default_placeholder_observer.html
index 2c3d072c3f8..89b8bd2d8fe 100644
--- a/main/generated/torch.ao.quantization.observer.default_placeholder_observer.html
+++ b/main/generated/torch.ao.quantization.observer.default_placeholder_observer.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>default_placeholder_observer<a class="headerlink" href="#default-placeholder-observer" title="Permalink to this heading">¶</a></h1>
 <dl class="py attribute">
 <dt class="sig sig-object py" id="torch.ao.quantization.observer.default_placeholder_observer">
-<span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.observer.</span></span><span class="sig-name descname"><span class="pre">default_placeholder_observer</span></span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/observer.py#L1445"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.observer.default_placeholder_observer" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.observer.</span></span><span class="sig-name descname"><span class="pre">default_placeholder_observer</span></span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/quantization/observer.py#L1445"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.observer.default_placeholder_observer" title="Permalink to this definition">¶</a></dt>
 <dd><p>Default placeholder observer, usually used for quantization to torch.float16.</p>
 </dd></dl>
 
diff --git a/main/generated/torch.ao.quantization.observer.default_weight_observer.html b/main/generated/torch.ao.quantization.observer.default_weight_observer.html
index 7e4ed3b9c1d..08ea4be2194 100644
--- a/main/generated/torch.ao.quantization.observer.default_weight_observer.html
+++ b/main/generated/torch.ao.quantization.observer.default_weight_observer.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.ao.quantization.observer.get_block_size.html b/main/generated/torch.ao.quantization.observer.get_block_size.html
index 41f8a96fd43..1d522d5abfd 100644
--- a/main/generated/torch.ao.quantization.observer.get_block_size.html
+++ b/main/generated/torch.ao.quantization.observer.get_block_size.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>get_block_size<a class="headerlink" href="#get-block-size" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.quantization.observer.get_block_size">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.observer.</span></span><span class="sig-name descname"><span class="pre">get_block_size</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_shape</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">granularity</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/observer.html#get_block_size"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/observer.py#L1765"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.observer.get_block_size" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.observer.</span></span><span class="sig-name descname"><span class="pre">get_block_size</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_shape</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">granularity</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/observer.html#get_block_size"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/quantization/observer.py#L1765"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.observer.get_block_size" title="Permalink to this definition">¶</a></dt>
 <dd><p>Get the block size based on the input shape and granularity type.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.ao.quantization.observer.get_observer_state_dict.html b/main/generated/torch.ao.quantization.observer.get_observer_state_dict.html
index 6d471ddc857..a5b1c1fcb57 100644
--- a/main/generated/torch.ao.quantization.observer.get_observer_state_dict.html
+++ b/main/generated/torch.ao.quantization.observer.get_observer_state_dict.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>get_observer_state_dict<a class="headerlink" href="#get-observer-state-dict" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.quantization.observer.get_observer_state_dict">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.observer.</span></span><span class="sig-name descname"><span class="pre">get_observer_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mod</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/observer.html#get_observer_state_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/observer.py#L1888"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.observer.get_observer_state_dict" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.observer.</span></span><span class="sig-name descname"><span class="pre">get_observer_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mod</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/observer.html#get_observer_state_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/quantization/observer.py#L1888"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.observer.get_observer_state_dict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns the state dict corresponding to the observer stats.
 Traverse the model state_dict and extract out the stats.</p>
 </dd></dl>
diff --git a/main/generated/torch.ao.quantization.observer.load_observer_state_dict.html b/main/generated/torch.ao.quantization.observer.load_observer_state_dict.html
index 1d220ff1259..e63d3ed708d 100644
--- a/main/generated/torch.ao.quantization.observer.load_observer_state_dict.html
+++ b/main/generated/torch.ao.quantization.observer.load_observer_state_dict.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>load_observer_state_dict<a class="headerlink" href="#load-observer-state-dict" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.quantization.observer.load_observer_state_dict">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.observer.</span></span><span class="sig-name descname"><span class="pre">load_observer_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mod</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">obs_dict</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/observer.html#load_observer_state_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/observer.py#L1907"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.observer.load_observer_state_dict" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.observer.</span></span><span class="sig-name descname"><span class="pre">load_observer_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mod</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">obs_dict</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/observer.html#load_observer_state_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/quantization/observer.py#L1907"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.observer.load_observer_state_dict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Given input model and a state_dict containing model observer stats,
 load the stats back into the model. The observer state_dict can be saved
 using torch.ao.quantization.get_observer_state_dict</p>
diff --git a/main/generated/torch.ao.quantization.prepare.html b/main/generated/torch.ao.quantization.prepare.html
index f56e2b4406e..9aa9cfd5a38 100644
--- a/main/generated/torch.ao.quantization.prepare.html
+++ b/main/generated/torch.ao.quantization.prepare.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>prepare<a class="headerlink" href="#prepare" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.quantization.prepare">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.</span></span><span class="sig-name descname"><span class="pre">prepare</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">allow_list</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">observer_non_leaf_module_list</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepare_custom_config_dict</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/quantize.html#prepare"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/quantize.py#L335"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.prepare" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.</span></span><span class="sig-name descname"><span class="pre">prepare</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">allow_list</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">observer_non_leaf_module_list</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepare_custom_config_dict</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/quantize.html#prepare"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/quantization/quantize.py#L335"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.prepare" title="Permalink to this definition">¶</a></dt>
 <dd><p>Prepares a copy of the model for quantization calibration or quantization-aware training.</p>
 <p>Quantization configuration should be assigned preemptively
 to individual submodules in <cite>.qconfig</cite> attribute.</p>
diff --git a/main/generated/torch.ao.quantization.prepare_for_propagation_comparison.html b/main/generated/torch.ao.quantization.prepare_for_propagation_comparison.html
index 3cde417b8fa..d3b0c011b1d 100644
--- a/main/generated/torch.ao.quantization.prepare_for_propagation_comparison.html
+++ b/main/generated/torch.ao.quantization.prepare_for_propagation_comparison.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>prepare_for_propagation_comparison<a class="headerlink" href="#prepare-for-propagation-comparison" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.quantization.prepare_for_propagation_comparison">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.</span></span><span class="sig-name descname"><span class="pre">prepare_for_propagation_comparison</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/pt2e/_numeric_debugger.py#L183"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.prepare_for_propagation_comparison" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.</span></span><span class="sig-name descname"><span class="pre">prepare_for_propagation_comparison</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/quantization/pt2e/_numeric_debugger.py#L183"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.prepare_for_propagation_comparison" title="Permalink to this definition">¶</a></dt>
 <dd><p>Add output loggers to node that has numeric_debug_handle</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.ao.quantization.prepare_qat.html b/main/generated/torch.ao.quantization.prepare_qat.html
index c8ddb39c3f3..24071b54014 100644
--- a/main/generated/torch.ao.quantization.prepare_qat.html
+++ b/main/generated/torch.ao.quantization.prepare_qat.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>prepare_qat<a class="headerlink" href="#prepare-qat" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.quantization.prepare_qat">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.</span></span><span class="sig-name descname"><span class="pre">prepare_qat</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mapping</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/quantize.html#prepare_qat"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/quantize.py#L564"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.prepare_qat" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.</span></span><span class="sig-name descname"><span class="pre">prepare_qat</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mapping</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/quantize.html#prepare_qat"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/quantization/quantize.py#L564"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.prepare_qat" title="Permalink to this definition">¶</a></dt>
 <dd><p>Prepares a copy of the model for quantization calibration or
 quantization-aware training and converts it to quantized version.</p>
 <p>Quantization configuration should be assigned preemptively
diff --git a/main/generated/torch.ao.quantization.propagate_qconfig_.html b/main/generated/torch.ao.quantization.propagate_qconfig_.html
index e8458bd6d14..0bcfbcfeb56 100644
--- a/main/generated/torch.ao.quantization.propagate_qconfig_.html
+++ b/main/generated/torch.ao.quantization.propagate_qconfig_.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
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     </div>
     <div id="searchBox">
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@@ -614,7 +614,7 @@
 <h1><a class="reference internal" href="#propagate-qconfig">propagate_qconfig</a><a class="headerlink" href="#propagate-qconfig" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.quantization.propagate_qconfig_">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.</span></span><span class="sig-name descname"><span class="pre">propagate_qconfig_</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">qconfig_dict</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepare_custom_config_dict</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/quantize.html#propagate_qconfig_"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/quantize.py#L120"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.propagate_qconfig_" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.</span></span><span class="sig-name descname"><span class="pre">propagate_qconfig_</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">qconfig_dict</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepare_custom_config_dict</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/quantize.html#propagate_qconfig_"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/quantization/quantize.py#L120"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.propagate_qconfig_" title="Permalink to this definition">¶</a></dt>
 <dd><p>Propagate qconfig through the module hierarchy and assign <cite>qconfig</cite>
 attribute on each leaf module</p>
 <dl class="field-list simple">
diff --git a/main/generated/torch.ao.quantization.pt2e.export_utils.model_is_exported.html b/main/generated/torch.ao.quantization.pt2e.export_utils.model_is_exported.html
index e3a08af03f7..345b93dbe5b 100644
--- a/main/generated/torch.ao.quantization.pt2e.export_utils.model_is_exported.html
+++ b/main/generated/torch.ao.quantization.pt2e.export_utils.model_is_exported.html
@@ -300,7 +300,7 @@
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     <div class="version">
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     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>model_is_exported<a class="headerlink" href="#model-is-exported" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.quantization.pt2e.export_utils.model_is_exported">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.pt2e.export_utils.</span></span><span class="sig-name descname"><span class="pre">model_is_exported</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">m</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/pt2e/export_utils.html#model_is_exported"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/pt2e/export_utils.py#L30"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.pt2e.export_utils.model_is_exported" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.pt2e.export_utils.</span></span><span class="sig-name descname"><span class="pre">model_is_exported</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">m</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/pt2e/export_utils.html#model_is_exported"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/quantization/pt2e/export_utils.py#L30"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.pt2e.export_utils.model_is_exported" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return True if the <cite>torch.nn.Module</cite> was exported, False otherwise
 (e.g. if the model was FX symbolically traced or not traced at all).</p>
 <dl class="field-list simple">
diff --git a/main/generated/torch.ao.quantization.qconfig.QConfig.html b/main/generated/torch.ao.quantization.qconfig.QConfig.html
index 5b5e2f80115..23ae850913a 100644
--- a/main/generated/torch.ao.quantization.qconfig.QConfig.html
+++ b/main/generated/torch.ao.quantization.qconfig.QConfig.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
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+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
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     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>QConfig<a class="headerlink" href="#qconfig" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.quantization.qconfig.QConfig">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.qconfig.</span></span><span class="sig-name descname"><span class="pre">QConfig</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">activation</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weight</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/qconfig.html#QConfig"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/qconfig.py#L85"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.qconfig.QConfig" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.qconfig.</span></span><span class="sig-name descname"><span class="pre">QConfig</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">activation</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weight</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/qconfig.html#QConfig"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/quantization/qconfig.py#L85"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.qconfig.QConfig" title="Permalink to this definition">¶</a></dt>
 <dd><p>Describes how to quantize a layer or a part of the network by providing
 settings (observer classes) for activations and weights respectively.</p>
 <p>Note that QConfig needs to contain observer <strong>classes</strong> (like MinMaxObserver) or a callable that returns
diff --git a/main/generated/torch.ao.quantization.qconfig.default_activation_only_qconfig.html b/main/generated/torch.ao.quantization.qconfig.default_activation_only_qconfig.html
index 4da7374bf75..fed3d2deab3 100644
--- a/main/generated/torch.ao.quantization.qconfig.default_activation_only_qconfig.html
+++ b/main/generated/torch.ao.quantization.qconfig.default_activation_only_qconfig.html
@@ -300,7 +300,7 @@
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     <div class="version">
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     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.ao.quantization.qconfig.default_debug_qconfig.html b/main/generated/torch.ao.quantization.qconfig.default_debug_qconfig.html
index 2ada34deea6..3f372bdaed0 100644
--- a/main/generated/torch.ao.quantization.qconfig.default_debug_qconfig.html
+++ b/main/generated/torch.ao.quantization.qconfig.default_debug_qconfig.html
@@ -300,7 +300,7 @@
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     <div class="version">
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     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.ao.quantization.qconfig.default_dynamic_qconfig.html b/main/generated/torch.ao.quantization.qconfig.default_dynamic_qconfig.html
index c8fa87c7b27..5847001f2bf 100644
--- a/main/generated/torch.ao.quantization.qconfig.default_dynamic_qconfig.html
+++ b/main/generated/torch.ao.quantization.qconfig.default_dynamic_qconfig.html
@@ -300,7 +300,7 @@
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diff --git a/main/generated/torch.ao.quantization.qconfig.default_per_channel_qconfig.html b/main/generated/torch.ao.quantization.qconfig.default_per_channel_qconfig.html
index 5ba9d66134b..5db4c15f113 100644
--- a/main/generated/torch.ao.quantization.qconfig.default_per_channel_qconfig.html
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@@ -300,7 +300,7 @@
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     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.ao.quantization.qconfig.default_qat_qconfig.html b/main/generated/torch.ao.quantization.qconfig.default_qat_qconfig.html
index c225d2de9c1..a6aaa8a7b09 100644
--- a/main/generated/torch.ao.quantization.qconfig.default_qat_qconfig.html
+++ b/main/generated/torch.ao.quantization.qconfig.default_qat_qconfig.html
@@ -300,7 +300,7 @@
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     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
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     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.ao.quantization.qconfig.default_qat_qconfig_v2.html b/main/generated/torch.ao.quantization.qconfig.default_qat_qconfig_v2.html
index 874a370267f..7e0d79be2bd 100644
--- a/main/generated/torch.ao.quantization.qconfig.default_qat_qconfig_v2.html
+++ b/main/generated/torch.ao.quantization.qconfig.default_qat_qconfig_v2.html
@@ -300,7 +300,7 @@
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     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
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     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.ao.quantization.qconfig.default_qconfig.html b/main/generated/torch.ao.quantization.qconfig.default_qconfig.html
index bfa23e7aef7..fdca1f372a3 100644
--- a/main/generated/torch.ao.quantization.qconfig.default_qconfig.html
+++ b/main/generated/torch.ao.quantization.qconfig.default_qconfig.html
@@ -300,7 +300,7 @@
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     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
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     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.ao.quantization.qconfig.default_weight_only_qconfig.html b/main/generated/torch.ao.quantization.qconfig.default_weight_only_qconfig.html
index 6570df84b67..812c0510c47 100644
--- a/main/generated/torch.ao.quantization.qconfig.default_weight_only_qconfig.html
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     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.ao.quantization.qconfig.float16_dynamic_qconfig.html b/main/generated/torch.ao.quantization.qconfig.float16_dynamic_qconfig.html
index b07fad24aff..86a7c91a929 100644
--- a/main/generated/torch.ao.quantization.qconfig.float16_dynamic_qconfig.html
+++ b/main/generated/torch.ao.quantization.qconfig.float16_dynamic_qconfig.html
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     <div class="version">
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diff --git a/main/generated/torch.ao.quantization.qconfig.float16_static_qconfig.html b/main/generated/torch.ao.quantization.qconfig.float16_static_qconfig.html
index e9d2073f26f..2b25d274c12 100644
--- a/main/generated/torch.ao.quantization.qconfig.float16_static_qconfig.html
+++ b/main/generated/torch.ao.quantization.qconfig.float16_static_qconfig.html
@@ -300,7 +300,7 @@
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     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
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diff --git a/main/generated/torch.ao.quantization.qconfig.float_qparams_weight_only_qconfig.html b/main/generated/torch.ao.quantization.qconfig.float_qparams_weight_only_qconfig.html
index fc7310e7141..3ef4d12cbc4 100644
--- a/main/generated/torch.ao.quantization.qconfig.float_qparams_weight_only_qconfig.html
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@@ -300,7 +300,7 @@
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     <div class="version">
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diff --git a/main/generated/torch.ao.quantization.qconfig.per_channel_dynamic_qconfig.html b/main/generated/torch.ao.quantization.qconfig.per_channel_dynamic_qconfig.html
index b6223ece837..403eea57bac 100644
--- a/main/generated/torch.ao.quantization.qconfig.per_channel_dynamic_qconfig.html
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@@ -300,7 +300,7 @@
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     <div class="version">
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diff --git a/main/generated/torch.ao.quantization.qconfig_mapping.QConfigMapping.html b/main/generated/torch.ao.quantization.qconfig_mapping.QConfigMapping.html
index 7452c4e45e9..da7a7b8717d 100644
--- a/main/generated/torch.ao.quantization.qconfig_mapping.QConfigMapping.html
+++ b/main/generated/torch.ao.quantization.qconfig_mapping.QConfigMapping.html
@@ -300,7 +300,7 @@
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     <div class="version">
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@@ -614,7 +614,7 @@
 <h1>QConfigMapping<a class="headerlink" href="#qconfigmapping" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.quantization.qconfig_mapping.QConfigMapping">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.qconfig_mapping.</span></span><span class="sig-name descname"><span class="pre">QConfigMapping</span></span><a class="reference internal" href="../_modules/torch/ao/quantization/qconfig_mapping.html#QConfigMapping"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/qconfig_mapping.py#L201"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.qconfig_mapping.QConfigMapping" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.qconfig_mapping.</span></span><span class="sig-name descname"><span class="pre">QConfigMapping</span></span><a class="reference internal" href="../_modules/torch/ao/quantization/qconfig_mapping.html#QConfigMapping"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/quantization/qconfig_mapping.py#L201"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.qconfig_mapping.QConfigMapping" title="Permalink to this definition">¶</a></dt>
 <dd><p>Mapping from model ops to <code class="xref py py-class docutils literal notranslate"><span class="pre">torch.ao.quantization.QConfig</span></code> s.</p>
 <p>The user can specify QConfigs using the following methods (in increasing match priority):</p>
 <blockquote>
@@ -641,7 +641,7 @@ <h1>QConfigMapping<a class="headerlink" href="#qconfigmapping" title="Permalink
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.ao.quantization.qconfig_mapping.QConfigMapping.from_dict">
-<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">qconfig_dict</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/qconfig_mapping.html#QConfigMapping.from_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/qconfig_mapping.py#L347"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.qconfig_mapping.QConfigMapping.from_dict" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">qconfig_dict</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/qconfig_mapping.html#QConfigMapping.from_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/quantization/qconfig_mapping.py#L347"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.qconfig_mapping.QConfigMapping.from_dict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Create a <code class="docutils literal notranslate"><span class="pre">QConfigMapping</span></code> from a dictionary with the following keys (all optional):</p>
 <blockquote>
 <div><p>“” (for global QConfig)</p>
@@ -660,7 +660,7 @@ <h1>QConfigMapping<a class="headerlink" href="#qconfigmapping" title="Permalink
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.ao.quantization.qconfig_mapping.QConfigMapping.set_global">
-<span class="sig-name descname"><span class="pre">set_global</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">global_qconfig</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/qconfig_mapping.html#QConfigMapping.set_global"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/qconfig_mapping.py#L244"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.qconfig_mapping.QConfigMapping.set_global" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">set_global</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">global_qconfig</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/qconfig_mapping.html#QConfigMapping.set_global"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/quantization/qconfig_mapping.py#L244"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.qconfig_mapping.QConfigMapping.set_global" title="Permalink to this definition">¶</a></dt>
 <dd><p>Set the global (default) QConfig.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -671,7 +671,7 @@ <h1>QConfigMapping<a class="headerlink" href="#qconfigmapping" title="Permalink
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.ao.quantization.qconfig_mapping.QConfigMapping.set_module_name">
-<span class="sig-name descname"><span class="pre">set_module_name</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module_name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">qconfig</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/qconfig_mapping.html#QConfigMapping.set_module_name"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/qconfig_mapping.py#L284"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.qconfig_mapping.QConfigMapping.set_module_name" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">set_module_name</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module_name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">qconfig</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/qconfig_mapping.html#QConfigMapping.set_module_name"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/quantization/qconfig_mapping.py#L284"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.qconfig_mapping.QConfigMapping.set_module_name" title="Permalink to this definition">¶</a></dt>
 <dd><p>Set the QConfig for modules matching the given module name.
 If the QConfig for an existing module name was already set, the new QConfig will override the old one.</p>
 <dl class="field-list simple">
@@ -683,7 +683,7 @@ <h1>QConfigMapping<a class="headerlink" href="#qconfigmapping" title="Permalink
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.ao.quantization.qconfig_mapping.QConfigMapping.set_module_name_object_type_order">
-<span class="sig-name descname"><span class="pre">set_module_name_object_type_order</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module_name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">object_type</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">index</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">qconfig</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/qconfig_mapping.html#QConfigMapping.set_module_name_object_type_order"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/qconfig_mapping.py#L292"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.qconfig_mapping.QConfigMapping.set_module_name_object_type_order" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">set_module_name_object_type_order</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module_name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">object_type</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">index</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">qconfig</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/qconfig_mapping.html#QConfigMapping.set_module_name_object_type_order"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/quantization/qconfig_mapping.py#L292"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.qconfig_mapping.QConfigMapping.set_module_name_object_type_order" title="Permalink to this definition">¶</a></dt>
 <dd><p>Set the QConfig for modules matching a combination of the given module name, object type,
 and the index at which the module appears.</p>
 <p>If the QConfig for an existing (module name, object type, index)  was already set, the new QConfig
@@ -697,7 +697,7 @@ <h1>QConfigMapping<a class="headerlink" href="#qconfigmapping" title="Permalink
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.ao.quantization.qconfig_mapping.QConfigMapping.set_module_name_regex">
-<span class="sig-name descname"><span class="pre">set_module_name_regex</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module_name_regex</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">qconfig</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/qconfig_mapping.html#QConfigMapping.set_module_name_regex"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/qconfig_mapping.py#L261"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.qconfig_mapping.QConfigMapping.set_module_name_regex" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">set_module_name_regex</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module_name_regex</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">qconfig</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/qconfig_mapping.html#QConfigMapping.set_module_name_regex"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/quantization/qconfig_mapping.py#L261"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.qconfig_mapping.QConfigMapping.set_module_name_regex" title="Permalink to this definition">¶</a></dt>
 <dd><p>Set the QConfig for modules matching the given regex string.</p>
 <p>Regexes will be matched in the order in which they are registered through this method.
 Thus, the caller should register more specific patterns first, e.g.:</p>
@@ -720,7 +720,7 @@ <h1>QConfigMapping<a class="headerlink" href="#qconfigmapping" title="Permalink
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.ao.quantization.qconfig_mapping.QConfigMapping.set_object_type">
-<span class="sig-name descname"><span class="pre">set_object_type</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">object_type</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">qconfig</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/qconfig_mapping.html#QConfigMapping.set_object_type"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/qconfig_mapping.py#L251"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.qconfig_mapping.QConfigMapping.set_object_type" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">set_object_type</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">object_type</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">qconfig</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/qconfig_mapping.html#QConfigMapping.set_object_type"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/quantization/qconfig_mapping.py#L251"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.qconfig_mapping.QConfigMapping.set_object_type" title="Permalink to this definition">¶</a></dt>
 <dd><p>Set the QConfig for a given module type, function, or method name.
 If the QConfig for an existing object type was already set, the new QConfig will override the old one.</p>
 <dl class="field-list simple">
@@ -732,7 +732,7 @@ <h1>QConfigMapping<a class="headerlink" href="#qconfigmapping" title="Permalink
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.ao.quantization.qconfig_mapping.QConfigMapping.to_dict">
-<span class="sig-name descname"><span class="pre">to_dict</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/qconfig_mapping.html#QConfigMapping.to_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/qconfig_mapping.py#L320"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.qconfig_mapping.QConfigMapping.to_dict" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">to_dict</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/qconfig_mapping.html#QConfigMapping.to_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/quantization/qconfig_mapping.py#L320"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.qconfig_mapping.QConfigMapping.to_dict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Convert this <code class="docutils literal notranslate"><span class="pre">QConfigMapping</span></code> to a dictionary with the following keys:</p>
 <blockquote>
 <div><p>“” (for global QConfig)</p>
diff --git a/main/generated/torch.ao.quantization.qconfig_mapping.get_default_qat_qconfig_mapping.html b/main/generated/torch.ao.quantization.qconfig_mapping.get_default_qat_qconfig_mapping.html
index edbbc6332b3..0d7ebf5b3f8 100644
--- a/main/generated/torch.ao.quantization.qconfig_mapping.get_default_qat_qconfig_mapping.html
+++ b/main/generated/torch.ao.quantization.qconfig_mapping.get_default_qat_qconfig_mapping.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>get_default_qat_qconfig_mapping<a class="headerlink" href="#get-default-qat-qconfig-mapping" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.quantization.qconfig_mapping.get_default_qat_qconfig_mapping">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.qconfig_mapping.</span></span><span class="sig-name descname"><span class="pre">get_default_qat_qconfig_mapping</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">backend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'x86'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">version</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/qconfig_mapping.html#get_default_qat_qconfig_mapping"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/qconfig_mapping.py#L139"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.qconfig_mapping.get_default_qat_qconfig_mapping" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.qconfig_mapping.</span></span><span class="sig-name descname"><span class="pre">get_default_qat_qconfig_mapping</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">backend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'x86'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">version</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/qconfig_mapping.html#get_default_qat_qconfig_mapping"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/quantization/qconfig_mapping.py#L139"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.qconfig_mapping.get_default_qat_qconfig_mapping" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the default QConfigMapping for quantization aware training.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.ao.quantization.qconfig_mapping.get_default_qconfig_mapping.html b/main/generated/torch.ao.quantization.qconfig_mapping.get_default_qconfig_mapping.html
index 432b5e9a455..02eadce9a44 100644
--- a/main/generated/torch.ao.quantization.qconfig_mapping.get_default_qconfig_mapping.html
+++ b/main/generated/torch.ao.quantization.qconfig_mapping.get_default_qconfig_mapping.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>get_default_qconfig_mapping<a class="headerlink" href="#get-default-qconfig-mapping" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.quantization.qconfig_mapping.get_default_qconfig_mapping">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.qconfig_mapping.</span></span><span class="sig-name descname"><span class="pre">get_default_qconfig_mapping</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">backend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'x86'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">version</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/qconfig_mapping.html#get_default_qconfig_mapping"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/qconfig_mapping.py#L126"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.qconfig_mapping.get_default_qconfig_mapping" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.qconfig_mapping.</span></span><span class="sig-name descname"><span class="pre">get_default_qconfig_mapping</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">backend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'x86'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">version</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/qconfig_mapping.html#get_default_qconfig_mapping"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/quantization/qconfig_mapping.py#L126"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.qconfig_mapping.get_default_qconfig_mapping" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the default QConfigMapping for post training quantization.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.ao.quantization.quantize.html b/main/generated/torch.ao.quantization.quantize.html
index 8183168b420..a39db31cb26 100644
--- a/main/generated/torch.ao.quantization.quantize.html
+++ b/main/generated/torch.ao.quantization.quantize.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>quantize<a class="headerlink" href="#quantize" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.quantization.quantize">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.</span></span><span class="sig-name descname"><span class="pre">quantize</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">run_fn</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">run_args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mapping</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/quantize.html#quantize"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/quantize.py#L445"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.quantize" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.</span></span><span class="sig-name descname"><span class="pre">quantize</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">run_fn</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">run_args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mapping</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/quantize.html#quantize"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/quantization/quantize.py#L445"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.quantize" title="Permalink to this definition">¶</a></dt>
 <dd><p>Quantize the input float model with post training static quantization.</p>
 <p>First it will prepare the model for calibration, then it calls
 <cite>run_fn</cite> which will run the calibration step, after that we will
diff --git a/main/generated/torch.ao.quantization.quantize_dynamic.html b/main/generated/torch.ao.quantization.quantize_dynamic.html
index 329736f3891..da686171ac2 100644
--- a/main/generated/torch.ao.quantization.quantize_dynamic.html
+++ b/main/generated/torch.ao.quantization.quantize_dynamic.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
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@@ -614,7 +614,7 @@
 <h1>quantize_dynamic<a class="headerlink" href="#quantize-dynamic" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.quantization.quantize_dynamic">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.</span></span><span class="sig-name descname"><span class="pre">quantize_dynamic</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">qconfig_spec</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.qint8</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mapping</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/quantize.html#quantize_dynamic"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/quantize.py#L474"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.quantize_dynamic" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.</span></span><span class="sig-name descname"><span class="pre">quantize_dynamic</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">qconfig_spec</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.qint8</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mapping</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/quantize.html#quantize_dynamic"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/quantization/quantize.py#L474"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.quantize_dynamic" title="Permalink to this definition">¶</a></dt>
 <dd><p>Converts a float model to dynamic (i.e. weights-only) quantized model.</p>
 <p>Replaces specified modules with dynamic weight-only quantized versions and output the quantized model.</p>
 <p>For simplest usage provide <cite>dtype</cite> argument that can be float16 or qint8. Weight-only quantization
diff --git a/main/generated/torch.ao.quantization.quantize_fx.convert_fx.html b/main/generated/torch.ao.quantization.quantize_fx.convert_fx.html
index 79c305f7ef1..a1d21ff2ecf 100644
--- a/main/generated/torch.ao.quantization.quantize_fx.convert_fx.html
+++ b/main/generated/torch.ao.quantization.quantize_fx.convert_fx.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>convert_fx<a class="headerlink" href="#convert-fx" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.quantization.quantize_fx.convert_fx">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.quantize_fx.</span></span><span class="sig-name descname"><span class="pre">convert_fx</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">graph_module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">convert_custom_config</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_remove_qconfig</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">qconfig_mapping</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">backend_config</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">keep_original_weights</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/quantize_fx.html#convert_fx"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/quantize_fx.py#L557"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.quantize_fx.convert_fx" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.quantize_fx.</span></span><span class="sig-name descname"><span class="pre">convert_fx</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">graph_module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">convert_custom_config</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_remove_qconfig</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">qconfig_mapping</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">backend_config</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">keep_original_weights</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/quantize_fx.html#convert_fx"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/quantization/quantize_fx.py#L557"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.quantize_fx.convert_fx" title="Permalink to this definition">¶</a></dt>
 <dd><p>Convert a calibrated or trained model to a quantized model</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.ao.quantization.quantize_fx.fuse_fx.html b/main/generated/torch.ao.quantization.quantize_fx.fuse_fx.html
index 6bd0a83ebb5..b9989687d86 100644
--- a/main/generated/torch.ao.quantization.quantize_fx.fuse_fx.html
+++ b/main/generated/torch.ao.quantization.quantize_fx.fuse_fx.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>fuse_fx<a class="headerlink" href="#fuse-fx" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.quantization.quantize_fx.fuse_fx">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.quantize_fx.</span></span><span class="sig-name descname"><span class="pre">fuse_fx</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fuse_custom_config</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">backend_config</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/quantize_fx.html#fuse_fx"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/quantize_fx.py#L204"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.quantize_fx.fuse_fx" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.quantize_fx.</span></span><span class="sig-name descname"><span class="pre">fuse_fx</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fuse_custom_config</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">backend_config</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/quantize_fx.html#fuse_fx"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/quantization/quantize_fx.py#L204"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.quantize_fx.fuse_fx" title="Permalink to this definition">¶</a></dt>
 <dd><p>Fuse modules like conv+bn, conv+bn+relu etc, model must be in eval mode.
 Fusion rules are defined in torch.ao.quantization.fx.fusion_pattern.py</p>
 <dl class="field-list simple">
diff --git a/main/generated/torch.ao.quantization.quantize_fx.prepare_fx.html b/main/generated/torch.ao.quantization.quantize_fx.prepare_fx.html
index a475c1f8bd0..79f08df0524 100644
--- a/main/generated/torch.ao.quantization.quantize_fx.prepare_fx.html
+++ b/main/generated/torch.ao.quantization.quantize_fx.prepare_fx.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>prepare_fx<a class="headerlink" href="#prepare-fx" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.quantization.quantize_fx.prepare_fx">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.quantize_fx.</span></span><span class="sig-name descname"><span class="pre">prepare_fx</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">qconfig_mapping</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">example_inputs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepare_custom_config</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_equalization_config</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">backend_config</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/quantize_fx.html#prepare_fx"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/quantize_fx.py#L252"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.quantize_fx.prepare_fx" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.quantize_fx.</span></span><span class="sig-name descname"><span class="pre">prepare_fx</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">qconfig_mapping</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">example_inputs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepare_custom_config</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_equalization_config</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">backend_config</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/quantize_fx.html#prepare_fx"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/quantization/quantize_fx.py#L252"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.quantize_fx.prepare_fx" title="Permalink to this definition">¶</a></dt>
 <dd><p>Prepare a model for post training quantization</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.ao.quantization.quantize_fx.prepare_qat_fx.html b/main/generated/torch.ao.quantization.quantize_fx.prepare_qat_fx.html
index 009bdc03348..a91f78802ca 100644
--- a/main/generated/torch.ao.quantization.quantize_fx.prepare_qat_fx.html
+++ b/main/generated/torch.ao.quantization.quantize_fx.prepare_qat_fx.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
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     <div id="searchBox">
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@@ -614,7 +614,7 @@
 <h1>prepare_qat_fx<a class="headerlink" href="#prepare-qat-fx" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.quantization.quantize_fx.prepare_qat_fx">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.quantize_fx.</span></span><span class="sig-name descname"><span class="pre">prepare_qat_fx</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">qconfig_mapping</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">example_inputs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepare_custom_config</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">backend_config</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/quantize_fx.html#prepare_qat_fx"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/quantize_fx.py#L403"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.quantize_fx.prepare_qat_fx" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.quantize_fx.</span></span><span class="sig-name descname"><span class="pre">prepare_qat_fx</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">qconfig_mapping</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">example_inputs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepare_custom_config</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">backend_config</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/quantize_fx.html#prepare_qat_fx"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/quantization/quantize_fx.py#L403"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.quantize_fx.prepare_qat_fx" title="Permalink to this definition">¶</a></dt>
 <dd><p>Prepare a model for quantization aware training</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.ao.quantization.quantize_qat.html b/main/generated/torch.ao.quantization.quantize_qat.html
index 83c9fd1ac39..7f7ddef16ee 100644
--- a/main/generated/torch.ao.quantization.quantize_qat.html
+++ b/main/generated/torch.ao.quantization.quantize_qat.html
@@ -300,7 +300,7 @@
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     <div class="version">
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     </div>
     <div id="searchBox">
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@@ -614,7 +614,7 @@
 <h1>quantize_qat<a class="headerlink" href="#quantize-qat" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.quantization.quantize_qat">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.</span></span><span class="sig-name descname"><span class="pre">quantize_qat</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">run_fn</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">run_args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/quantize.html#quantize_qat"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/quantize.py#L593"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.quantize_qat" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.</span></span><span class="sig-name descname"><span class="pre">quantize_qat</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">run_fn</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">run_args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/quantize.html#quantize_qat"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/quantization/quantize.py#L593"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.quantize_qat" title="Permalink to this definition">¶</a></dt>
 <dd><p>Do quantization aware training and output a quantized model</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.ao.quantization.swap_module.html b/main/generated/torch.ao.quantization.swap_module.html
index e26ba157245..d0d6592990b 100644
--- a/main/generated/torch.ao.quantization.swap_module.html
+++ b/main/generated/torch.ao.quantization.swap_module.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
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@@ -614,7 +614,7 @@
 <h1>swap_module<a class="headerlink" href="#swap-module" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.quantization.swap_module">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.</span></span><span class="sig-name descname"><span class="pre">swap_module</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mod</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mapping</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">custom_module_class_mapping</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_precomputed_fake_quant</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/quantize.html#swap_module"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/quantization/quantize.py#L730"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.swap_module" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.quantization.</span></span><span class="sig-name descname"><span class="pre">swap_module</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mod</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mapping</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">custom_module_class_mapping</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_precomputed_fake_quant</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/ao/quantization/quantize.html#swap_module"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/quantization/quantize.py#L730"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.quantization.swap_module" title="Permalink to this definition">¶</a></dt>
 <dd><p>Swaps the module if it has a quantized counterpart and it has an
 <cite>observer</cite> attached.</p>
 <dl class="field-list simple">
diff --git a/main/generated/torch.arange.html b/main/generated/torch.arange.html
index 06001de65f0..0c1e3e20f97 100644
--- a/main/generated/torch.arange.html
+++ b/main/generated/torch.arange.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.arccos.html b/main/generated/torch.arccos.html
index ebe791f1379..bd2ce7c16d1 100644
--- a/main/generated/torch.arccos.html
+++ b/main/generated/torch.arccos.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.arccosh.html b/main/generated/torch.arccosh.html
index c4e90226f9c..b3eb8184f44 100644
--- a/main/generated/torch.arccosh.html
+++ b/main/generated/torch.arccosh.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
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diff --git a/main/generated/torch.arcsin.html b/main/generated/torch.arcsin.html
index 40a9ba7cfc7..fb4078321e7 100644
--- a/main/generated/torch.arcsin.html
+++ b/main/generated/torch.arcsin.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.arcsinh.html b/main/generated/torch.arcsinh.html
index 947966f993f..cbe5234256c 100644
--- a/main/generated/torch.arcsinh.html
+++ b/main/generated/torch.arcsinh.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
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     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.arctan.html b/main/generated/torch.arctan.html
index fef463de43d..674a613090e 100644
--- a/main/generated/torch.arctan.html
+++ b/main/generated/torch.arctan.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
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     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.arctan2.html b/main/generated/torch.arctan2.html
index 89a718996ed..542f5c29c3d 100644
--- a/main/generated/torch.arctan2.html
+++ b/main/generated/torch.arctan2.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.arctanh.html b/main/generated/torch.arctanh.html
index 4c5c501afe1..10d99bdc205 100644
--- a/main/generated/torch.arctanh.html
+++ b/main/generated/torch.arctanh.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
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diff --git a/main/generated/torch.are_deterministic_algorithms_enabled.html b/main/generated/torch.are_deterministic_algorithms_enabled.html
index 4722cabfa96..a53bf30c453 100644
--- a/main/generated/torch.are_deterministic_algorithms_enabled.html
+++ b/main/generated/torch.are_deterministic_algorithms_enabled.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
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     </div>
     <div id="searchBox">
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@@ -612,7 +612,7 @@
 <h1>torch.are_deterministic_algorithms_enabled<a class="headerlink" href="#torch-are-deterministic-algorithms-enabled" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.are_deterministic_algorithms_enabled">
-<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">are_deterministic_algorithms_enabled</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch.html#are_deterministic_algorithms_enabled"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/__init__.py#L1430"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.are_deterministic_algorithms_enabled" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">are_deterministic_algorithms_enabled</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch.html#are_deterministic_algorithms_enabled"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/__init__.py#L1430"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.are_deterministic_algorithms_enabled" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns True if the global deterministic flag is turned on. Refer to
 <a class="reference internal" href="torch.use_deterministic_algorithms.html#torch.use_deterministic_algorithms" title="torch.use_deterministic_algorithms"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.use_deterministic_algorithms()</span></code></a> documentation for more details.</p>
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diff --git a/main/generated/torch.as_tensor.html b/main/generated/torch.as_tensor.html
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 <dt class="sig sig-object py" id="torch.atleast_1d">
-<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">atleast_1d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">tensors</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/functional.html#atleast_1d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/functional.py#L1493"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.atleast_1d" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">atleast_1d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">tensors</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/functional.html#atleast_1d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/functional.py#L1493"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.atleast_1d" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns a 1-dimensional view of each input tensor with zero dimensions.
 Input tensors with one or more dimensions are returned as-is.</p>
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diff --git a/main/generated/torch.atleast_2d.html b/main/generated/torch.atleast_2d.html
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 <h1>torch.atleast_2d<a class="headerlink" href="#torch-atleast-2d" title="Permalink to this heading">¶</a></h1>
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 <dt class="sig sig-object py" id="torch.atleast_2d">
-<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">atleast_2d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">tensors</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/functional.html#atleast_2d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/functional.py#L1529"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.atleast_2d" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">atleast_2d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">tensors</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/functional.html#atleast_2d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/functional.py#L1529"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.atleast_2d" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns a 2-dimensional view of each input tensor with zero dimensions.
 Input tensors with two or more dimensions are returned as-is.</p>
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diff --git a/main/generated/torch.atleast_3d.html b/main/generated/torch.atleast_3d.html
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@@ -612,7 +612,7 @@
 <h1>torch.atleast_3d<a class="headerlink" href="#torch-atleast-3d" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.atleast_3d">
-<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">atleast_3d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">tensors</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/functional.html#atleast_3d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/functional.py#L1567"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.atleast_3d" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">atleast_3d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">tensors</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/functional.html#atleast_3d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/functional.py#L1567"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.atleast_3d" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns a 3-dimensional view of each input tensor with zero dimensions.
 Input tensors with three or more dimensions are returned as-is.</p>
 <dl class="field-list simple">
diff --git a/main/generated/torch.autograd.Function.backward.html b/main/generated/torch.autograd.Function.backward.html
index f13270bc8e6..9021e07d7d3 100644
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 <h1>torch.autograd.Function.backward<a class="headerlink" href="#torch-autograd-function-backward" title="Permalink to this heading">¶</a></h1>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.autograd.Function.backward">
-<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">Function.</span></span><span class="sig-name descname"><span class="pre">backward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">ctx</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">grad_outputs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/autograd/function.py#L411"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.Function.backward" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">Function.</span></span><span class="sig-name descname"><span class="pre">backward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">ctx</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">grad_outputs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/autograd/function.py#L411"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.Function.backward" title="Permalink to this definition">¶</a></dt>
 <dd><p>Define a formula for differentiating the operation with backward mode automatic differentiation.</p>
 <p>This function is to be overridden by all subclasses.
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diff --git a/main/generated/torch.autograd.Function.forward.html b/main/generated/torch.autograd.Function.forward.html
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 <h1>torch.autograd.Function.forward<a class="headerlink" href="#torch-autograd-function-forward" title="Permalink to this heading">¶</a></h1>
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 <dt class="sig sig-object py" id="torch.autograd.Function.forward">
-<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">Function.</span></span><span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/autograd/function.py#L348"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.Function.forward" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">Function.</span></span><span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/autograd/function.py#L348"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.Function.forward" title="Permalink to this definition">¶</a></dt>
 <dd><p>Define the forward of the custom autograd Function.</p>
 <p>This function is to be overridden by all subclasses.
 There are two ways to define forward:</p>
diff --git a/main/generated/torch.autograd.Function.jvp.html b/main/generated/torch.autograd.Function.jvp.html
index 6403a7116ce..50d0ddb76d0 100644
--- a/main/generated/torch.autograd.Function.jvp.html
+++ b/main/generated/torch.autograd.Function.jvp.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.autograd.Function.jvp<a class="headerlink" href="#torch-autograd-function-jvp" title="Permalink to this heading">¶</a></h1>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.autograd.Function.jvp">
-<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">Function.</span></span><span class="sig-name descname"><span class="pre">jvp</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">ctx</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">grad_inputs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/autograd/function.py#L443"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.Function.jvp" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">Function.</span></span><span class="sig-name descname"><span class="pre">jvp</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">ctx</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">grad_inputs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/autograd/function.py#L443"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.Function.jvp" title="Permalink to this definition">¶</a></dt>
 <dd><p>Define a formula for differentiating the operation with forward mode automatic differentiation.</p>
 <p>This function is to be overridden by all subclasses.
 It must accept a context <code class="xref py py-attr docutils literal notranslate"><span class="pre">ctx</span></code> as the first argument, followed by
diff --git a/main/generated/torch.autograd.Function.vmap.html b/main/generated/torch.autograd.Function.vmap.html
index c299cdcc496..f6a444bdc63 100644
--- a/main/generated/torch.autograd.Function.vmap.html
+++ b/main/generated/torch.autograd.Function.vmap.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.autograd.Function.vmap<a class="headerlink" href="#torch-autograd-function-vmap" title="Permalink to this heading">¶</a></h1>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.autograd.Function.vmap">
-<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">Function.</span></span><span class="sig-name descname"><span class="pre">vmap</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">info</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">in_dims</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd/function.html#Function.vmap"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/autograd/function.py#L529"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.Function.vmap" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">Function.</span></span><span class="sig-name descname"><span class="pre">vmap</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">info</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">in_dims</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd/function.html#Function.vmap"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/autograd/function.py#L529"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.Function.vmap" title="Permalink to this definition">¶</a></dt>
 <dd><p>Define the behavior for this autograd.Function underneath <a class="reference internal" href="torch.vmap.html#torch.vmap" title="torch.vmap"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.vmap()</span></code></a>.</p>
 <p>For a <a class="reference internal" href="../autograd.html#torch.autograd.Function" title="torch.autograd.Function"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.autograd.Function()</span></code></a> to support
 <a class="reference internal" href="torch.vmap.html#torch.vmap" title="torch.vmap"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.vmap()</span></code></a>, you must either override this static method, or set
diff --git a/main/generated/torch.autograd.backward.html b/main/generated/torch.autograd.backward.html
index b7741c3d139..9324307f3fd 100644
--- a/main/generated/torch.autograd.backward.html
+++ b/main/generated/torch.autograd.backward.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.autograd.backward<a class="headerlink" href="#torch-autograd-backward" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.autograd.backward">
-<span class="sig-prename descclassname"><span class="pre">torch.autograd.</span></span><span class="sig-name descname"><span class="pre">backward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tensors</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">grad_tensors</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">retain_graph</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">create_graph</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">grad_variables</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inputs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd.html#backward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/autograd/__init__.py#L243"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.backward" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.autograd.</span></span><span class="sig-name descname"><span class="pre">backward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tensors</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">grad_tensors</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">retain_graph</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">create_graph</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">grad_variables</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inputs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd.html#backward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/autograd/__init__.py#L243"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.backward" title="Permalink to this definition">¶</a></dt>
 <dd><p>Compute the sum of gradients of given tensors with respect to graph leaves.</p>
 <p>The graph is differentiated using the chain rule. If any of <code class="docutils literal notranslate"><span class="pre">tensors</span></code>
 are non-scalar (i.e. their data has more than one element) and require
diff --git a/main/generated/torch.autograd.forward_ad.UnpackedDualTensor.html b/main/generated/torch.autograd.forward_ad.UnpackedDualTensor.html
index e524d1637ed..a1c64f33c56 100644
--- a/main/generated/torch.autograd.forward_ad.UnpackedDualTensor.html
+++ b/main/generated/torch.autograd.forward_ad.UnpackedDualTensor.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>UnpackedDualTensor<a class="headerlink" href="#unpackeddualtensor" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.autograd.forward_ad.UnpackedDualTensor">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.autograd.forward_ad.</span></span><span class="sig-name descname"><span class="pre">UnpackedDualTensor</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">primal</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tangent</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd/forward_ad.html#UnpackedDualTensor"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/autograd/forward_ad.py#L135"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.forward_ad.UnpackedDualTensor" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.autograd.forward_ad.</span></span><span class="sig-name descname"><span class="pre">UnpackedDualTensor</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">primal</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tangent</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd/forward_ad.html#UnpackedDualTensor"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/autograd/forward_ad.py#L135"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.forward_ad.UnpackedDualTensor" title="Permalink to this definition">¶</a></dt>
 <dd><p>Namedtuple returned by <a class="reference internal" href="torch.autograd.forward_ad.unpack_dual.html#torch.autograd.forward_ad.unpack_dual" title="torch.autograd.forward_ad.unpack_dual"><code class="xref py py-func docutils literal notranslate"><span class="pre">unpack_dual()</span></code></a> containing the primal and tangent components of the dual tensor.</p>
 <p>See <a class="reference internal" href="torch.autograd.forward_ad.unpack_dual.html#torch.autograd.forward_ad.unpack_dual" title="torch.autograd.forward_ad.unpack_dual"><code class="xref py py-func docutils literal notranslate"><span class="pre">unpack_dual()</span></code></a> for more details.</p>
 <dl class="py method">
diff --git a/main/generated/torch.autograd.forward_ad.dual_level.html b/main/generated/torch.autograd.forward_ad.dual_level.html
index 70bfa2ef736..be3e8e315d5 100644
--- a/main/generated/torch.autograd.forward_ad.dual_level.html
+++ b/main/generated/torch.autograd.forward_ad.dual_level.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
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@@ -612,7 +612,7 @@
 <h1>dual_level<a class="headerlink" href="#dual-level" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.autograd.forward_ad.dual_level">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.autograd.forward_ad.</span></span><span class="sig-name descname"><span class="pre">dual_level</span></span><a class="reference internal" href="../_modules/torch/autograd/forward_ad.html#dual_level"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/autograd/forward_ad.py#L175"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.forward_ad.dual_level" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.autograd.forward_ad.</span></span><span class="sig-name descname"><span class="pre">dual_level</span></span><a class="reference internal" href="../_modules/torch/autograd/forward_ad.html#dual_level"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/autograd/forward_ad.py#L175"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.forward_ad.dual_level" title="Permalink to this definition">¶</a></dt>
 <dd><p>Context-manager for forward AD, where all forward AD computation must occur within the <code class="docutils literal notranslate"><span class="pre">dual_level</span></code> context.</p>
 <div class="admonition note">
 <p class="admonition-title">Note</p>
diff --git a/main/generated/torch.autograd.forward_ad.enter_dual_level.html b/main/generated/torch.autograd.forward_ad.enter_dual_level.html
index 6e81db86ea0..e636f1d9f15 100644
--- a/main/generated/torch.autograd.forward_ad.enter_dual_level.html
+++ b/main/generated/torch.autograd.forward_ad.enter_dual_level.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.autograd.forward_ad.enter_dual_level<a class="headerlink" href="#torch-autograd-forward-ad-enter-dual-level" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.autograd.forward_ad.enter_dual_level">
-<span class="sig-prename descclassname"><span class="pre">torch.autograd.forward_ad.</span></span><span class="sig-name descname"><span class="pre">enter_dual_level</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd/forward_ad.html#enter_dual_level"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/autograd/forward_ad.py#L24"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.forward_ad.enter_dual_level" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.autograd.forward_ad.</span></span><span class="sig-name descname"><span class="pre">enter_dual_level</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd/forward_ad.html#enter_dual_level"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/autograd/forward_ad.py#L24"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.forward_ad.enter_dual_level" title="Permalink to this definition">¶</a></dt>
 <dd><p>Enter a new forward grad level.</p>
 <p>This level can be used to make and unpack dual Tensors to compute
 forward gradients.</p>
diff --git a/main/generated/torch.autograd.forward_ad.exit_dual_level.html b/main/generated/torch.autograd.forward_ad.exit_dual_level.html
index eb9deb770d7..392d858c06c 100644
--- a/main/generated/torch.autograd.forward_ad.exit_dual_level.html
+++ b/main/generated/torch.autograd.forward_ad.exit_dual_level.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.autograd.forward_ad.exit_dual_level<a class="headerlink" href="#torch-autograd-forward-ad-exit-dual-level" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.autograd.forward_ad.exit_dual_level">
-<span class="sig-prename descclassname"><span class="pre">torch.autograd.forward_ad.</span></span><span class="sig-name descname"><span class="pre">exit_dual_level</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">level</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd/forward_ad.html#exit_dual_level"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/autograd/forward_ad.py#L44"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.forward_ad.exit_dual_level" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.autograd.forward_ad.</span></span><span class="sig-name descname"><span class="pre">exit_dual_level</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">level</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd/forward_ad.html#exit_dual_level"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/autograd/forward_ad.py#L44"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.forward_ad.exit_dual_level" title="Permalink to this definition">¶</a></dt>
 <dd><p>Exit a forward grad level.</p>
 <p>This function deletes all the gradients associated with this
 level. Only deleting the latest entered level is allowed.</p>
diff --git a/main/generated/torch.autograd.forward_ad.make_dual.html b/main/generated/torch.autograd.forward_ad.make_dual.html
index 7b0155b3717..fe5b52ab095 100644
--- a/main/generated/torch.autograd.forward_ad.make_dual.html
+++ b/main/generated/torch.autograd.forward_ad.make_dual.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.autograd.forward_ad.make_dual<a class="headerlink" href="#torch-autograd-forward-ad-make-dual" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.autograd.forward_ad.make_dual">
-<span class="sig-prename descclassname"><span class="pre">torch.autograd.forward_ad.</span></span><span class="sig-name descname"><span class="pre">make_dual</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tangent</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">level</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd/forward_ad.html#make_dual"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/autograd/forward_ad.py#L70"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.forward_ad.make_dual" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.autograd.forward_ad.</span></span><span class="sig-name descname"><span class="pre">make_dual</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tangent</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">level</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd/forward_ad.html#make_dual"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/autograd/forward_ad.py#L70"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.forward_ad.make_dual" title="Permalink to this definition">¶</a></dt>
 <dd><p>Associate a tensor value with its tangent to create a “dual tensor” for forward AD gradient computation.</p>
 <p>The result is a new tensor aliased to <code class="xref py py-attr docutils literal notranslate"><span class="pre">tensor</span></code> with <code class="xref py py-attr docutils literal notranslate"><span class="pre">tangent</span></code> embedded
 as an attribute as-is if it has the same storage layout or copied otherwise.
diff --git a/main/generated/torch.autograd.forward_ad.unpack_dual.html b/main/generated/torch.autograd.forward_ad.unpack_dual.html
index 817299c3819..335e3265cba 100644
--- a/main/generated/torch.autograd.forward_ad.unpack_dual.html
+++ b/main/generated/torch.autograd.forward_ad.unpack_dual.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.autograd.forward_ad.unpack_dual<a class="headerlink" href="#torch-autograd-forward-ad-unpack-dual" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.autograd.forward_ad.unpack_dual">
-<span class="sig-prename descclassname"><span class="pre">torch.autograd.forward_ad.</span></span><span class="sig-name descname"><span class="pre">unpack_dual</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tensor</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">level</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd/forward_ad.html#unpack_dual"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/autograd/forward_ad.py#L143"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.forward_ad.unpack_dual" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.autograd.forward_ad.</span></span><span class="sig-name descname"><span class="pre">unpack_dual</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tensor</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">level</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd/forward_ad.html#unpack_dual"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/autograd/forward_ad.py#L143"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.forward_ad.unpack_dual" title="Permalink to this definition">¶</a></dt>
 <dd><p>Unpack a “dual tensor” to get both its Tensor value and its forward AD gradient.</p>
 <p>The result is a namedtuple <code class="docutils literal notranslate"><span class="pre">(primal,</span> <span class="pre">tangent)</span></code> where <code class="docutils literal notranslate"><span class="pre">primal</span></code> is a view of
 <code class="xref py py-attr docutils literal notranslate"><span class="pre">tensor</span></code>’s primal and <code class="docutils literal notranslate"><span class="pre">tangent</span></code> is <code class="xref py py-attr docutils literal notranslate"><span class="pre">tensor</span></code>’s tangent as-is.
diff --git a/main/generated/torch.autograd.function.BackwardCFunction.html b/main/generated/torch.autograd.function.BackwardCFunction.html
index 3e22854ddf2..a750e0eeacf 100644
--- a/main/generated/torch.autograd.function.BackwardCFunction.html
+++ b/main/generated/torch.autograd.function.BackwardCFunction.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,23 +612,23 @@
 <h1>BackwardCFunction<a class="headerlink" href="#backwardcfunction" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.autograd.function.BackwardCFunction">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.autograd.function.</span></span><span class="sig-name descname"><span class="pre">BackwardCFunction</span></span><a class="reference internal" href="../_modules/torch/autograd/function.html#BackwardCFunction"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/autograd/function.py#L287"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.function.BackwardCFunction" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.autograd.function.</span></span><span class="sig-name descname"><span class="pre">BackwardCFunction</span></span><a class="reference internal" href="../_modules/torch/autograd/function.html#BackwardCFunction"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/autograd/function.py#L287"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.function.BackwardCFunction" title="Permalink to this definition">¶</a></dt>
 <dd><p>This class is used for internal autograd work. Do not use.</p>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.autograd.function.BackwardCFunction.apply">
-<span class="sig-name descname"><span class="pre">apply</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd/function.html#BackwardCFunction.apply"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/autograd/function.py#L292"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.function.BackwardCFunction.apply" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">apply</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd/function.html#BackwardCFunction.apply"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/autograd/function.py#L292"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.function.BackwardCFunction.apply" title="Permalink to this definition">¶</a></dt>
 <dd><p>Apply method used when executing this Node during the backward</p>
 </dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.autograd.function.BackwardCFunction.apply_jvp">
-<span class="sig-name descname"><span class="pre">apply_jvp</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd/function.html#BackwardCFunction.apply_jvp"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/autograd/function.py#L309"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.function.BackwardCFunction.apply_jvp" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">apply_jvp</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd/function.html#BackwardCFunction.apply_jvp"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/autograd/function.py#L309"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.function.BackwardCFunction.apply_jvp" title="Permalink to this definition">¶</a></dt>
 <dd><p>Apply method used when executing forward mode AD during the forward</p>
 </dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.autograd.function.BackwardCFunction.mark_dirty">
-<span class="sig-name descname"><span class="pre">mark_dirty</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/autograd/function.py#L148"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.function.BackwardCFunction.mark_dirty" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">mark_dirty</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/autograd/function.py#L148"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.function.BackwardCFunction.mark_dirty" title="Permalink to this definition">¶</a></dt>
 <dd><p>Mark given tensors as modified in an in-place operation.</p>
 <p>This should be called at most once, in either the <code class="xref py py-func docutils literal notranslate"><span class="pre">setup_context()</span></code>
 or <code class="xref py py-func docutils literal notranslate"><span class="pre">forward()</span></code> methods, and all arguments should be inputs.</p>
@@ -666,7 +666,7 @@ <h1>BackwardCFunction<a class="headerlink" href="#backwardcfunction" title="Perm
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.autograd.function.BackwardCFunction.mark_non_differentiable">
-<span class="sig-name descname"><span class="pre">mark_non_differentiable</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/autograd/function.py#L194"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.function.BackwardCFunction.mark_non_differentiable" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">mark_non_differentiable</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/autograd/function.py#L194"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.function.BackwardCFunction.mark_non_differentiable" title="Permalink to this definition">¶</a></dt>
 <dd><p>Mark outputs as non-differentiable.</p>
 <p>This should be called at most once, in either the <code class="xref py py-func docutils literal notranslate"><span class="pre">setup_context()</span></code>
 or <code class="xref py py-func docutils literal notranslate"><span class="pre">forward()</span></code> methods, and all arguments should be tensor outputs.</p>
@@ -701,7 +701,7 @@ <h1>BackwardCFunction<a class="headerlink" href="#backwardcfunction" title="Perm
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.autograd.function.BackwardCFunction.save_for_backward">
-<span class="sig-name descname"><span class="pre">save_for_backward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">tensors</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/autograd/function.py#L35"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.function.BackwardCFunction.save_for_backward" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">save_for_backward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">tensors</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/autograd/function.py#L35"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.function.BackwardCFunction.save_for_backward" title="Permalink to this definition">¶</a></dt>
 <dd><p>Save given tensors for a future call to <code class="xref py py-func docutils literal notranslate"><span class="pre">backward()</span></code>.</p>
 <p><code class="docutils literal notranslate"><span class="pre">save_for_backward</span></code> should be called at most once, in either the
 <code class="xref py py-func docutils literal notranslate"><span class="pre">setup_context()</span></code> or <code class="xref py py-func docutils literal notranslate"><span class="pre">forward()</span></code> methods, and only with tensors.</p>
@@ -758,7 +758,7 @@ <h1>BackwardCFunction<a class="headerlink" href="#backwardcfunction" title="Perm
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.autograd.function.BackwardCFunction.save_for_forward">
-<span class="sig-name descname"><span class="pre">save_for_forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">tensors</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/autograd/function.py#L94"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.function.BackwardCFunction.save_for_forward" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">save_for_forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">tensors</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/autograd/function.py#L94"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.function.BackwardCFunction.save_for_forward" title="Permalink to this definition">¶</a></dt>
 <dd><p>Save given tensors for a future call to <code class="xref py py-func docutils literal notranslate"><span class="pre">jvp()</span></code>.</p>
 <p><code class="docutils literal notranslate"><span class="pre">save_for_forward</span></code> should be called at most once, in either the
 <code class="xref py py-func docutils literal notranslate"><span class="pre">setup_context()</span></code> or <code class="xref py py-func docutils literal notranslate"><span class="pre">forward()</span></code> methods, and all arguments
@@ -806,7 +806,7 @@ <h1>BackwardCFunction<a class="headerlink" href="#backwardcfunction" title="Perm
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.autograd.function.BackwardCFunction.set_materialize_grads">
-<span class="sig-name descname"><span class="pre">set_materialize_grads</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/autograd/function.py#L226"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.function.BackwardCFunction.set_materialize_grads" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">set_materialize_grads</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/autograd/function.py#L226"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.function.BackwardCFunction.set_materialize_grads" title="Permalink to this definition">¶</a></dt>
 <dd><p>Set whether to materialize grad tensors. Default is <code class="docutils literal notranslate"><span class="pre">True</span></code>.</p>
 <p>This should be called only from either the <code class="xref py py-func docutils literal notranslate"><span class="pre">setup_context()</span></code> or
 <code class="xref py py-func docutils literal notranslate"><span class="pre">forward()</span></code> methods.</p>
diff --git a/main/generated/torch.autograd.function.FunctionCtx.mark_dirty.html b/main/generated/torch.autograd.function.FunctionCtx.mark_dirty.html
index f41c8c8d8df..00ee3aaa386 100644
--- a/main/generated/torch.autograd.function.FunctionCtx.mark_dirty.html
+++ b/main/generated/torch.autograd.function.FunctionCtx.mark_dirty.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.autograd.function.FunctionCtx.mark_dirty<a class="headerlink" href="#torch-autograd-function-functionctx-mark-dirty" title="Permalink to this heading">¶</a></h1>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.autograd.function.FunctionCtx.mark_dirty">
-<span class="sig-prename descclassname"><span class="pre">FunctionCtx.</span></span><span class="sig-name descname"><span class="pre">mark_dirty</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd/function.html#FunctionCtx.mark_dirty"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/autograd/function.py#L148"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.function.FunctionCtx.mark_dirty" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">FunctionCtx.</span></span><span class="sig-name descname"><span class="pre">mark_dirty</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd/function.html#FunctionCtx.mark_dirty"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/autograd/function.py#L148"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.function.FunctionCtx.mark_dirty" title="Permalink to this definition">¶</a></dt>
 <dd><p>Mark given tensors as modified in an in-place operation.</p>
 <p>This should be called at most once, in either the <code class="xref py py-func docutils literal notranslate"><span class="pre">setup_context()</span></code>
 or <code class="xref py py-func docutils literal notranslate"><span class="pre">forward()</span></code> methods, and all arguments should be inputs.</p>
diff --git a/main/generated/torch.autograd.function.FunctionCtx.mark_non_differentiable.html b/main/generated/torch.autograd.function.FunctionCtx.mark_non_differentiable.html
index 0a573d93309..108a29baa2d 100644
--- a/main/generated/torch.autograd.function.FunctionCtx.mark_non_differentiable.html
+++ b/main/generated/torch.autograd.function.FunctionCtx.mark_non_differentiable.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.autograd.function.FunctionCtx.mark_non_differentiable<a class="headerlink" href="#torch-autograd-function-functionctx-mark-non-differentiable" title="Permalink to this heading">¶</a></h1>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.autograd.function.FunctionCtx.mark_non_differentiable">
-<span class="sig-prename descclassname"><span class="pre">FunctionCtx.</span></span><span class="sig-name descname"><span class="pre">mark_non_differentiable</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd/function.html#FunctionCtx.mark_non_differentiable"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/autograd/function.py#L194"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.function.FunctionCtx.mark_non_differentiable" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">FunctionCtx.</span></span><span class="sig-name descname"><span class="pre">mark_non_differentiable</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd/function.html#FunctionCtx.mark_non_differentiable"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/autograd/function.py#L194"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.function.FunctionCtx.mark_non_differentiable" title="Permalink to this definition">¶</a></dt>
 <dd><p>Mark outputs as non-differentiable.</p>
 <p>This should be called at most once, in either the <code class="xref py py-func docutils literal notranslate"><span class="pre">setup_context()</span></code>
 or <code class="xref py py-func docutils literal notranslate"><span class="pre">forward()</span></code> methods, and all arguments should be tensor outputs.</p>
diff --git a/main/generated/torch.autograd.function.FunctionCtx.save_for_backward.html b/main/generated/torch.autograd.function.FunctionCtx.save_for_backward.html
index 09b74db078f..946cae354f2 100644
--- a/main/generated/torch.autograd.function.FunctionCtx.save_for_backward.html
+++ b/main/generated/torch.autograd.function.FunctionCtx.save_for_backward.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.autograd.function.FunctionCtx.save_for_backward<a class="headerlink" href="#torch-autograd-function-functionctx-save-for-backward" title="Permalink to this heading">¶</a></h1>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.autograd.function.FunctionCtx.save_for_backward">
-<span class="sig-prename descclassname"><span class="pre">FunctionCtx.</span></span><span class="sig-name descname"><span class="pre">save_for_backward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">tensors</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd/function.html#FunctionCtx.save_for_backward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/autograd/function.py#L35"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.function.FunctionCtx.save_for_backward" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">FunctionCtx.</span></span><span class="sig-name descname"><span class="pre">save_for_backward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">tensors</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd/function.html#FunctionCtx.save_for_backward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/autograd/function.py#L35"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.function.FunctionCtx.save_for_backward" title="Permalink to this definition">¶</a></dt>
 <dd><p>Save given tensors for a future call to <code class="xref py py-func docutils literal notranslate"><span class="pre">backward()</span></code>.</p>
 <p><code class="docutils literal notranslate"><span class="pre">save_for_backward</span></code> should be called at most once, in either the
 <code class="xref py py-func docutils literal notranslate"><span class="pre">setup_context()</span></code> or <code class="xref py py-func docutils literal notranslate"><span class="pre">forward()</span></code> methods, and only with tensors.</p>
diff --git a/main/generated/torch.autograd.function.FunctionCtx.set_materialize_grads.html b/main/generated/torch.autograd.function.FunctionCtx.set_materialize_grads.html
index b951c25a7e0..11b4f5d948d 100644
--- a/main/generated/torch.autograd.function.FunctionCtx.set_materialize_grads.html
+++ b/main/generated/torch.autograd.function.FunctionCtx.set_materialize_grads.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.autograd.function.FunctionCtx.set_materialize_grads<a class="headerlink" href="#torch-autograd-function-functionctx-set-materialize-grads" title="Permalink to this heading">¶</a></h1>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.autograd.function.FunctionCtx.set_materialize_grads">
-<span class="sig-prename descclassname"><span class="pre">FunctionCtx.</span></span><span class="sig-name descname"><span class="pre">set_materialize_grads</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd/function.html#FunctionCtx.set_materialize_grads"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/autograd/function.py#L226"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.function.FunctionCtx.set_materialize_grads" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">FunctionCtx.</span></span><span class="sig-name descname"><span class="pre">set_materialize_grads</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd/function.html#FunctionCtx.set_materialize_grads"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/autograd/function.py#L226"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.function.FunctionCtx.set_materialize_grads" title="Permalink to this definition">¶</a></dt>
 <dd><p>Set whether to materialize grad tensors. Default is <code class="docutils literal notranslate"><span class="pre">True</span></code>.</p>
 <p>This should be called only from either the <code class="xref py py-func docutils literal notranslate"><span class="pre">setup_context()</span></code> or
 <code class="xref py py-func docutils literal notranslate"><span class="pre">forward()</span></code> methods.</p>
diff --git a/main/generated/torch.autograd.function.InplaceFunction.html b/main/generated/torch.autograd.function.InplaceFunction.html
index 778695ad910..45a1c025a22 100644
--- a/main/generated/torch.autograd.function.InplaceFunction.html
+++ b/main/generated/torch.autograd.function.InplaceFunction.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,12 +612,12 @@
 <h1>InplaceFunction<a class="headerlink" href="#inplacefunction" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.autograd.function.InplaceFunction">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.autograd.function.</span></span><span class="sig-name descname"><span class="pre">InplaceFunction</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd/function.html#InplaceFunction"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/autograd/function.py#L646"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.function.InplaceFunction" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.autograd.function.</span></span><span class="sig-name descname"><span class="pre">InplaceFunction</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd/function.html#InplaceFunction"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/autograd/function.py#L646"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.function.InplaceFunction" title="Permalink to this definition">¶</a></dt>
 <dd><p>This class is here only for backward compatibility reasons.
 Use <a class="reference internal" href="../autograd.html#torch.autograd.Function" title="torch.autograd.function.Function"><code class="xref py py-class docutils literal notranslate"><span class="pre">Function</span></code></a> instead of this for any new use case.</p>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.autograd.function.InplaceFunction.backward">
-<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">backward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">ctx</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">grad_outputs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/autograd/function.py#L411"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.function.InplaceFunction.backward" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">backward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">ctx</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">grad_outputs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/autograd/function.py#L411"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.function.InplaceFunction.backward" title="Permalink to this definition">¶</a></dt>
 <dd><p>Define a formula for differentiating the operation with backward mode automatic differentiation.</p>
 <p>This function is to be overridden by all subclasses.
 (Defining this function is equivalent to defining the <code class="docutils literal notranslate"><span class="pre">vjp</span></code> function.)</p>
@@ -644,7 +644,7 @@ <h1>InplaceFunction<a class="headerlink" href="#inplacefunction" title="Permalin
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.autograd.function.InplaceFunction.forward">
-<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/autograd/function.py#L348"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.function.InplaceFunction.forward" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/autograd/function.py#L348"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.function.InplaceFunction.forward" title="Permalink to this definition">¶</a></dt>
 <dd><p>Define the forward of the custom autograd Function.</p>
 <p>This function is to be overridden by all subclasses.
 There are two ways to define forward:</p>
@@ -693,7 +693,7 @@ <h1>InplaceFunction<a class="headerlink" href="#inplacefunction" title="Permalin
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.autograd.function.InplaceFunction.jvp">
-<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">jvp</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">ctx</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">grad_inputs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/autograd/function.py#L443"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.function.InplaceFunction.jvp" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">jvp</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">ctx</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">grad_inputs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/autograd/function.py#L443"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.function.InplaceFunction.jvp" title="Permalink to this definition">¶</a></dt>
 <dd><p>Define a formula for differentiating the operation with forward mode automatic differentiation.</p>
 <p>This function is to be overridden by all subclasses.
 It must accept a context <code class="xref py py-attr docutils literal notranslate"><span class="pre">ctx</span></code> as the first argument, followed by
@@ -716,7 +716,7 @@ <h1>InplaceFunction<a class="headerlink" href="#inplacefunction" title="Permalin
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.autograd.function.InplaceFunction.mark_dirty">
-<span class="sig-name descname"><span class="pre">mark_dirty</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/autograd/function.py#L148"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.function.InplaceFunction.mark_dirty" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">mark_dirty</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/autograd/function.py#L148"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.function.InplaceFunction.mark_dirty" title="Permalink to this definition">¶</a></dt>
 <dd><p>Mark given tensors as modified in an in-place operation.</p>
 <p>This should be called at most once, in either the <a class="reference internal" href="#torch.autograd.function.InplaceFunction.setup_context" title="torch.autograd.function.InplaceFunction.setup_context"><code class="xref py py-func docutils literal notranslate"><span class="pre">setup_context()</span></code></a>
 or <a class="reference internal" href="#torch.autograd.function.InplaceFunction.forward" title="torch.autograd.function.InplaceFunction.forward"><code class="xref py py-func docutils literal notranslate"><span class="pre">forward()</span></code></a> methods, and all arguments should be inputs.</p>
@@ -754,7 +754,7 @@ <h1>InplaceFunction<a class="headerlink" href="#inplacefunction" title="Permalin
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.autograd.function.InplaceFunction.mark_non_differentiable">
-<span class="sig-name descname"><span class="pre">mark_non_differentiable</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/autograd/function.py#L194"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.function.InplaceFunction.mark_non_differentiable" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">mark_non_differentiable</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/autograd/function.py#L194"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.function.InplaceFunction.mark_non_differentiable" title="Permalink to this definition">¶</a></dt>
 <dd><p>Mark outputs as non-differentiable.</p>
 <p>This should be called at most once, in either the <a class="reference internal" href="#torch.autograd.function.InplaceFunction.setup_context" title="torch.autograd.function.InplaceFunction.setup_context"><code class="xref py py-func docutils literal notranslate"><span class="pre">setup_context()</span></code></a>
 or <a class="reference internal" href="#torch.autograd.function.InplaceFunction.forward" title="torch.autograd.function.InplaceFunction.forward"><code class="xref py py-func docutils literal notranslate"><span class="pre">forward()</span></code></a> methods, and all arguments should be tensor outputs.</p>
@@ -789,7 +789,7 @@ <h1>InplaceFunction<a class="headerlink" href="#inplacefunction" title="Permalin
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.autograd.function.InplaceFunction.save_for_backward">
-<span class="sig-name descname"><span class="pre">save_for_backward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">tensors</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/autograd/function.py#L35"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.function.InplaceFunction.save_for_backward" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">save_for_backward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">tensors</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/autograd/function.py#L35"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.function.InplaceFunction.save_for_backward" title="Permalink to this definition">¶</a></dt>
 <dd><p>Save given tensors for a future call to <code class="xref py py-func docutils literal notranslate"><span class="pre">backward()</span></code>.</p>
 <p><code class="docutils literal notranslate"><span class="pre">save_for_backward</span></code> should be called at most once, in either the
 <a class="reference internal" href="#torch.autograd.function.InplaceFunction.setup_context" title="torch.autograd.function.InplaceFunction.setup_context"><code class="xref py py-func docutils literal notranslate"><span class="pre">setup_context()</span></code></a> or <a class="reference internal" href="#torch.autograd.function.InplaceFunction.forward" title="torch.autograd.function.InplaceFunction.forward"><code class="xref py py-func docutils literal notranslate"><span class="pre">forward()</span></code></a> methods, and only with tensors.</p>
@@ -846,7 +846,7 @@ <h1>InplaceFunction<a class="headerlink" href="#inplacefunction" title="Permalin
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.autograd.function.InplaceFunction.save_for_forward">
-<span class="sig-name descname"><span class="pre">save_for_forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">tensors</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/autograd/function.py#L94"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.function.InplaceFunction.save_for_forward" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">save_for_forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">tensors</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/autograd/function.py#L94"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.function.InplaceFunction.save_for_forward" title="Permalink to this definition">¶</a></dt>
 <dd><p>Save given tensors for a future call to <code class="xref py py-func docutils literal notranslate"><span class="pre">jvp()</span></code>.</p>
 <p><code class="docutils literal notranslate"><span class="pre">save_for_forward</span></code> should be called at most once, in either the
 <a class="reference internal" href="#torch.autograd.function.InplaceFunction.setup_context" title="torch.autograd.function.InplaceFunction.setup_context"><code class="xref py py-func docutils literal notranslate"><span class="pre">setup_context()</span></code></a> or <a class="reference internal" href="#torch.autograd.function.InplaceFunction.forward" title="torch.autograd.function.InplaceFunction.forward"><code class="xref py py-func docutils literal notranslate"><span class="pre">forward()</span></code></a> methods, and all arguments
@@ -894,7 +894,7 @@ <h1>InplaceFunction<a class="headerlink" href="#inplacefunction" title="Permalin
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.autograd.function.InplaceFunction.set_materialize_grads">
-<span class="sig-name descname"><span class="pre">set_materialize_grads</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/autograd/function.py#L226"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.function.InplaceFunction.set_materialize_grads" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">set_materialize_grads</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/autograd/function.py#L226"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.function.InplaceFunction.set_materialize_grads" title="Permalink to this definition">¶</a></dt>
 <dd><p>Set whether to materialize grad tensors. Default is <code class="docutils literal notranslate"><span class="pre">True</span></code>.</p>
 <p>This should be called only from either the <a class="reference internal" href="#torch.autograd.function.InplaceFunction.setup_context" title="torch.autograd.function.InplaceFunction.setup_context"><code class="xref py py-func docutils literal notranslate"><span class="pre">setup_context()</span></code></a> or
 <a class="reference internal" href="#torch.autograd.function.InplaceFunction.forward" title="torch.autograd.function.InplaceFunction.forward"><code class="xref py py-func docutils literal notranslate"><span class="pre">forward()</span></code></a> methods.</p>
@@ -942,7 +942,7 @@ <h1>InplaceFunction<a class="headerlink" href="#inplacefunction" title="Permalin
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.autograd.function.InplaceFunction.setup_context">
-<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">setup_context</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">ctx</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inputs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/autograd/function.py#L394"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.function.InplaceFunction.setup_context" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">setup_context</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">ctx</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inputs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/autograd/function.py#L394"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.function.InplaceFunction.setup_context" title="Permalink to this definition">¶</a></dt>
 <dd><p>There are two ways to define the forward pass of an autograd.Function.</p>
 <p>Either:</p>
 <ol class="arabic simple">
@@ -963,7 +963,7 @@ <h1>InplaceFunction<a class="headerlink" href="#inplacefunction" title="Permalin
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.autograd.function.InplaceFunction.vjp">
-<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">vjp</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">ctx</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">grad_outputs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/autograd/function.py#L411"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.function.InplaceFunction.vjp" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">vjp</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">ctx</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">grad_outputs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/autograd/function.py#L411"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.function.InplaceFunction.vjp" title="Permalink to this definition">¶</a></dt>
 <dd><p>Define a formula for differentiating the operation with backward mode automatic differentiation.</p>
 <p>This function is to be overridden by all subclasses.
 (Defining this function is equivalent to defining the <code class="docutils literal notranslate"><span class="pre">vjp</span></code> function.)</p>
@@ -990,7 +990,7 @@ <h1>InplaceFunction<a class="headerlink" href="#inplacefunction" title="Permalin
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.autograd.function.InplaceFunction.vmap">
-<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">vmap</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">info</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">in_dims</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/autograd/function.py#L529"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.function.InplaceFunction.vmap" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">vmap</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">info</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">in_dims</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/autograd/function.py#L529"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.function.InplaceFunction.vmap" title="Permalink to this definition">¶</a></dt>
 <dd><p>Define the behavior for this autograd.Function underneath <a class="reference internal" href="torch.vmap.html#torch.vmap" title="torch.vmap"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.vmap()</span></code></a>.</p>
 <p>For a <a class="reference internal" href="../autograd.html#torch.autograd.Function" title="torch.autograd.Function"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.autograd.Function()</span></code></a> to support
 <a class="reference internal" href="torch.vmap.html#torch.vmap" title="torch.vmap"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.vmap()</span></code></a>, you must either override this static method, or set
diff --git a/main/generated/torch.autograd.function.NestedIOFunction.html b/main/generated/torch.autograd.function.NestedIOFunction.html
index 31cec81d970..a81d51e2b04 100644
--- a/main/generated/torch.autograd.function.NestedIOFunction.html
+++ b/main/generated/torch.autograd.function.NestedIOFunction.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,12 +612,12 @@
 <h1>NestedIOFunction<a class="headerlink" href="#nestediofunction" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.autograd.function.NestedIOFunction">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.autograd.function.</span></span><span class="sig-name descname"><span class="pre">NestedIOFunction</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd/function.html#NestedIOFunction"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/autograd/function.py#L766"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.function.NestedIOFunction" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.autograd.function.</span></span><span class="sig-name descname"><span class="pre">NestedIOFunction</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd/function.html#NestedIOFunction"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/autograd/function.py#L766"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.function.NestedIOFunction" title="Permalink to this definition">¶</a></dt>
 <dd><p>This class is here only for backward compatibility reasons.
 Use <a class="reference internal" href="../autograd.html#torch.autograd.Function" title="torch.autograd.function.Function"><code class="xref py py-class docutils literal notranslate"><span class="pre">Function</span></code></a> instead of this for any new use case.</p>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.autograd.function.NestedIOFunction.backward">
-<span class="sig-name descname"><span class="pre">backward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">gradients</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd/function.html#NestedIOFunction.backward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/autograd/function.py#L789"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.function.NestedIOFunction.backward" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">backward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">gradients</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd/function.html#NestedIOFunction.backward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/autograd/function.py#L789"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.function.NestedIOFunction.backward" title="Permalink to this definition">¶</a></dt>
 <dd><p>Shared backward utility.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -628,7 +628,7 @@ <h1>NestedIOFunction<a class="headerlink" href="#nestediofunction" title="Permal
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.autograd.function.NestedIOFunction.backward_extended">
-<span class="sig-name descname"><span class="pre">backward_extended</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">grad_output</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd/function.html#NestedIOFunction.backward_extended"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/autograd/function.py#L842"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.function.NestedIOFunction.backward_extended" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">backward_extended</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">grad_output</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd/function.html#NestedIOFunction.backward_extended"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/autograd/function.py#L842"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.function.NestedIOFunction.backward_extended" title="Permalink to this definition">¶</a></dt>
 <dd><p>User defined backward.</p>
 <dl class="field-list simple">
 </dl>
@@ -636,7 +636,7 @@ <h1>NestedIOFunction<a class="headerlink" href="#nestediofunction" title="Permal
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.autograd.function.NestedIOFunction.forward">
-<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd/function.html#NestedIOFunction.forward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/autograd/function.py#L799"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.function.NestedIOFunction.forward" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd/function.html#NestedIOFunction.forward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/autograd/function.py#L799"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.function.NestedIOFunction.forward" title="Permalink to this definition">¶</a></dt>
 <dd><p>Shared forward utility.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -647,7 +647,7 @@ <h1>NestedIOFunction<a class="headerlink" href="#nestediofunction" title="Permal
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.autograd.function.NestedIOFunction.forward_extended">
-<span class="sig-name descname"><span class="pre">forward_extended</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">input</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd/function.html#NestedIOFunction.forward_extended"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/autograd/function.py#L836"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.function.NestedIOFunction.forward_extended" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">forward_extended</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">input</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd/function.html#NestedIOFunction.forward_extended"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/autograd/function.py#L836"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.function.NestedIOFunction.forward_extended" title="Permalink to this definition">¶</a></dt>
 <dd><p>User defined forward.</p>
 <dl class="field-list simple">
 </dl>
@@ -655,7 +655,7 @@ <h1>NestedIOFunction<a class="headerlink" href="#nestediofunction" title="Permal
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.autograd.function.NestedIOFunction.jvp">
-<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">jvp</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">ctx</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">grad_inputs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/autograd/function.py#L443"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.function.NestedIOFunction.jvp" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">jvp</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">ctx</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">grad_inputs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/autograd/function.py#L443"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.function.NestedIOFunction.jvp" title="Permalink to this definition">¶</a></dt>
 <dd><p>Define a formula for differentiating the operation with forward mode automatic differentiation.</p>
 <p>This function is to be overridden by all subclasses.
 It must accept a context <code class="xref py py-attr docutils literal notranslate"><span class="pre">ctx</span></code> as the first argument, followed by
@@ -678,7 +678,7 @@ <h1>NestedIOFunction<a class="headerlink" href="#nestediofunction" title="Permal
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.autograd.function.NestedIOFunction.mark_dirty">
-<span class="sig-name descname"><span class="pre">mark_dirty</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd/function.html#NestedIOFunction.mark_dirty"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/autograd/function.py#L824"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.function.NestedIOFunction.mark_dirty" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">mark_dirty</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd/function.html#NestedIOFunction.mark_dirty"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/autograd/function.py#L824"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.function.NestedIOFunction.mark_dirty" title="Permalink to this definition">¶</a></dt>
 <dd><p>See <code class="xref py py-meth docutils literal notranslate"><span class="pre">Function.mark_dirty()</span></code>.</p>
 <dl class="field-list simple">
 </dl>
@@ -686,7 +686,7 @@ <h1>NestedIOFunction<a class="headerlink" href="#nestediofunction" title="Permal
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.autograd.function.NestedIOFunction.mark_non_differentiable">
-<span class="sig-name descname"><span class="pre">mark_non_differentiable</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd/function.html#NestedIOFunction.mark_non_differentiable"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/autograd/function.py#L830"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.function.NestedIOFunction.mark_non_differentiable" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">mark_non_differentiable</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd/function.html#NestedIOFunction.mark_non_differentiable"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/autograd/function.py#L830"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.function.NestedIOFunction.mark_non_differentiable" title="Permalink to this definition">¶</a></dt>
 <dd><p>See <code class="xref py py-meth docutils literal notranslate"><span class="pre">Function.mark_non_differentiable()</span></code>.</p>
 <dl class="field-list simple">
 </dl>
@@ -694,7 +694,7 @@ <h1>NestedIOFunction<a class="headerlink" href="#nestediofunction" title="Permal
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.autograd.function.NestedIOFunction.save_for_backward">
-<span class="sig-name descname"><span class="pre">save_for_backward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd/function.html#NestedIOFunction.save_for_backward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/autograd/function.py#L809"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.function.NestedIOFunction.save_for_backward" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">save_for_backward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd/function.html#NestedIOFunction.save_for_backward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/autograd/function.py#L809"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.function.NestedIOFunction.save_for_backward" title="Permalink to this definition">¶</a></dt>
 <dd><p>See <code class="xref py py-meth docutils literal notranslate"><span class="pre">Function.save_for_backward()</span></code>.</p>
 <dl class="field-list simple">
 </dl>
@@ -702,7 +702,7 @@ <h1>NestedIOFunction<a class="headerlink" href="#nestediofunction" title="Permal
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.autograd.function.NestedIOFunction.save_for_forward">
-<span class="sig-name descname"><span class="pre">save_for_forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">tensors</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/autograd/function.py#L94"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.function.NestedIOFunction.save_for_forward" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">save_for_forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">tensors</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/autograd/function.py#L94"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.function.NestedIOFunction.save_for_forward" title="Permalink to this definition">¶</a></dt>
 <dd><p>Save given tensors for a future call to <code class="xref py py-func docutils literal notranslate"><span class="pre">jvp()</span></code>.</p>
 <p><code class="docutils literal notranslate"><span class="pre">save_for_forward</span></code> should be called at most once, in either the
 <a class="reference internal" href="#torch.autograd.function.NestedIOFunction.setup_context" title="torch.autograd.function.NestedIOFunction.setup_context"><code class="xref py py-func docutils literal notranslate"><span class="pre">setup_context()</span></code></a> or <a class="reference internal" href="#torch.autograd.function.NestedIOFunction.forward" title="torch.autograd.function.NestedIOFunction.forward"><code class="xref py py-func docutils literal notranslate"><span class="pre">forward()</span></code></a> methods, and all arguments
@@ -756,7 +756,7 @@ <h1>NestedIOFunction<a class="headerlink" href="#nestediofunction" title="Permal
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.autograd.function.NestedIOFunction.set_materialize_grads">
-<span class="sig-name descname"><span class="pre">set_materialize_grads</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/autograd/function.py#L226"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.function.NestedIOFunction.set_materialize_grads" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">set_materialize_grads</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/autograd/function.py#L226"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.function.NestedIOFunction.set_materialize_grads" title="Permalink to this definition">¶</a></dt>
 <dd><p>Set whether to materialize grad tensors. Default is <code class="docutils literal notranslate"><span class="pre">True</span></code>.</p>
 <p>This should be called only from either the <a class="reference internal" href="#torch.autograd.function.NestedIOFunction.setup_context" title="torch.autograd.function.NestedIOFunction.setup_context"><code class="xref py py-func docutils literal notranslate"><span class="pre">setup_context()</span></code></a> or
 <a class="reference internal" href="#torch.autograd.function.NestedIOFunction.forward" title="torch.autograd.function.NestedIOFunction.forward"><code class="xref py py-func docutils literal notranslate"><span class="pre">forward()</span></code></a> methods.</p>
@@ -804,7 +804,7 @@ <h1>NestedIOFunction<a class="headerlink" href="#nestediofunction" title="Permal
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.autograd.function.NestedIOFunction.setup_context">
-<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">setup_context</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">ctx</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inputs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/autograd/function.py#L394"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.function.NestedIOFunction.setup_context" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">setup_context</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">ctx</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inputs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/autograd/function.py#L394"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.function.NestedIOFunction.setup_context" title="Permalink to this definition">¶</a></dt>
 <dd><p>There are two ways to define the forward pass of an autograd.Function.</p>
 <p>Either:</p>
 <ol class="arabic simple">
@@ -825,7 +825,7 @@ <h1>NestedIOFunction<a class="headerlink" href="#nestediofunction" title="Permal
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.autograd.function.NestedIOFunction.vjp">
-<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">vjp</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">ctx</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">grad_outputs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/autograd/function.py#L411"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.function.NestedIOFunction.vjp" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">vjp</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">ctx</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">grad_outputs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/autograd/function.py#L411"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.function.NestedIOFunction.vjp" title="Permalink to this definition">¶</a></dt>
 <dd><p>Define a formula for differentiating the operation with backward mode automatic differentiation.</p>
 <p>This function is to be overridden by all subclasses.
 (Defining this function is equivalent to defining the <code class="docutils literal notranslate"><span class="pre">vjp</span></code> function.)</p>
@@ -852,7 +852,7 @@ <h1>NestedIOFunction<a class="headerlink" href="#nestediofunction" title="Permal
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.autograd.function.NestedIOFunction.vmap">
-<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">vmap</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">info</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">in_dims</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/autograd/function.py#L529"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.function.NestedIOFunction.vmap" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">vmap</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">info</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">in_dims</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/autograd/function.py#L529"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.function.NestedIOFunction.vmap" title="Permalink to this definition">¶</a></dt>
 <dd><p>Define the behavior for this autograd.Function underneath <a class="reference internal" href="torch.vmap.html#torch.vmap" title="torch.vmap"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.vmap()</span></code></a>.</p>
 <p>For a <a class="reference internal" href="../autograd.html#torch.autograd.Function" title="torch.autograd.Function"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.autograd.Function()</span></code></a> to support
 <a class="reference internal" href="torch.vmap.html#torch.vmap" title="torch.vmap"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.vmap()</span></code></a>, you must either override this static method, or set
diff --git a/main/generated/torch.autograd.function.once_differentiable.html b/main/generated/torch.autograd.function.once_differentiable.html
index 54a8036a4f0..a06fd7b08ab 100644
--- a/main/generated/torch.autograd.function.once_differentiable.html
+++ b/main/generated/torch.autograd.function.once_differentiable.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.autograd.function.once_differentiable<a class="headerlink" href="#torch-autograd-function-once-differentiable" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.autograd.function.once_differentiable">
-<span class="sig-prename descclassname"><span class="pre">torch.autograd.function.</span></span><span class="sig-name descname"><span class="pre">once_differentiable</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">fn</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd/function.html#once_differentiable"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/autograd/function.py#L599"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.function.once_differentiable" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.autograd.function.</span></span><span class="sig-name descname"><span class="pre">once_differentiable</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">fn</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd/function.html#once_differentiable"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/autograd/function.py#L599"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.function.once_differentiable" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 </div>
diff --git a/main/generated/torch.autograd.functional.hessian.html b/main/generated/torch.autograd.functional.hessian.html
index 70c2f28e09c..eac2004a025 100644
--- a/main/generated/torch.autograd.functional.hessian.html
+++ b/main/generated/torch.autograd.functional.hessian.html
@@ -300,7 +300,7 @@
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     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.autograd.functional.hessian<a class="headerlink" href="#torch-autograd-functional-hessian" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.autograd.functional.hessian">
-<span class="sig-prename descclassname"><span class="pre">torch.autograd.functional.</span></span><span class="sig-name descname"><span class="pre">hessian</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">func</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inputs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">create_graph</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">strict</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">vectorize</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">outer_jacobian_strategy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'reverse-mode'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd/functional.html#hessian"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/autograd/functional.py#L831"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.functional.hessian" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.autograd.functional.</span></span><span class="sig-name descname"><span class="pre">hessian</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">func</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inputs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">create_graph</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">strict</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">vectorize</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">outer_jacobian_strategy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'reverse-mode'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd/functional.html#hessian"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/autograd/functional.py#L831"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.functional.hessian" title="Permalink to this definition">¶</a></dt>
 <dd><p>Compute the Hessian of a given scalar function.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.autograd.functional.hvp.html b/main/generated/torch.autograd.functional.hvp.html
index c070b6a7fe1..d5987001b67 100644
--- a/main/generated/torch.autograd.functional.hvp.html
+++ b/main/generated/torch.autograd.functional.hvp.html
@@ -300,7 +300,7 @@
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     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.autograd.functional.hvp<a class="headerlink" href="#torch-autograd-functional-hvp" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.autograd.functional.hvp">
-<span class="sig-prename descclassname"><span class="pre">torch.autograd.functional.</span></span><span class="sig-name descname"><span class="pre">hvp</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">func</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inputs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">v</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">create_graph</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">strict</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd/functional.html#hvp"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/autograd/functional.py#L1070"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.functional.hvp" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.autograd.functional.</span></span><span class="sig-name descname"><span class="pre">hvp</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">func</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inputs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">v</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">create_graph</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">strict</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd/functional.html#hvp"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/autograd/functional.py#L1070"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.functional.hvp" title="Permalink to this definition">¶</a></dt>
 <dd><p>Compute the dot product between the scalar function’s Hessian and a vector <code class="docutils literal notranslate"><span class="pre">v</span></code> at a specified point.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.autograd.functional.jacobian.html b/main/generated/torch.autograd.functional.jacobian.html
index 92144c081fb..f47dde1c729 100644
--- a/main/generated/torch.autograd.functional.jacobian.html
+++ b/main/generated/torch.autograd.functional.jacobian.html
@@ -300,7 +300,7 @@
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     <div class="version">
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+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.autograd.functional.jacobian<a class="headerlink" href="#torch-autograd-functional-jacobian" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.autograd.functional.jacobian">
-<span class="sig-prename descclassname"><span class="pre">torch.autograd.functional.</span></span><span class="sig-name descname"><span class="pre">jacobian</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">func</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inputs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">create_graph</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">strict</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">vectorize</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">strategy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'reverse-mode'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd/functional.html#jacobian"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/autograd/functional.py#L574"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.functional.jacobian" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.autograd.functional.</span></span><span class="sig-name descname"><span class="pre">jacobian</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">func</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inputs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">create_graph</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">strict</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">vectorize</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">strategy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'reverse-mode'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd/functional.html#jacobian"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/autograd/functional.py#L574"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.functional.jacobian" title="Permalink to this definition">¶</a></dt>
 <dd><p>Compute the Jacobian of a given function.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.autograd.functional.jvp.html b/main/generated/torch.autograd.functional.jvp.html
index 16ba89673c8..29c21b5b557 100644
--- a/main/generated/torch.autograd.functional.jvp.html
+++ b/main/generated/torch.autograd.functional.jvp.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.autograd.functional.jvp<a class="headerlink" href="#torch-autograd-functional-jvp" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.autograd.functional.jvp">
-<span class="sig-prename descclassname"><span class="pre">torch.autograd.functional.</span></span><span class="sig-name descname"><span class="pre">jvp</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">func</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inputs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">v</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">create_graph</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">strict</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd/functional.html#jvp"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/autograd/functional.py#L358"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.functional.jvp" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.autograd.functional.</span></span><span class="sig-name descname"><span class="pre">jvp</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">func</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inputs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">v</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">create_graph</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">strict</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd/functional.html#jvp"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/autograd/functional.py#L358"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.functional.jvp" title="Permalink to this definition">¶</a></dt>
 <dd><p>Compute the dot product between the Jacobian of the given function at the point given by the inputs and a vector <code class="docutils literal notranslate"><span class="pre">v</span></code>.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.autograd.functional.vhp.html b/main/generated/torch.autograd.functional.vhp.html
index 1501c2147e4..ce3259c209f 100644
--- a/main/generated/torch.autograd.functional.vhp.html
+++ b/main/generated/torch.autograd.functional.vhp.html
@@ -300,7 +300,7 @@
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     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.autograd.functional.vhp<a class="headerlink" href="#torch-autograd-functional-vhp" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.autograd.functional.vhp">
-<span class="sig-prename descclassname"><span class="pre">torch.autograd.functional.</span></span><span class="sig-name descname"><span class="pre">vhp</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">func</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inputs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">v</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">create_graph</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">strict</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd/functional.html#vhp"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/autograd/functional.py#L969"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.functional.vhp" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.autograd.functional.</span></span><span class="sig-name descname"><span class="pre">vhp</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">func</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inputs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">v</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">create_graph</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">strict</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd/functional.html#vhp"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/autograd/functional.py#L969"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.functional.vhp" title="Permalink to this definition">¶</a></dt>
 <dd><p>Compute the dot product between vector <code class="docutils literal notranslate"><span class="pre">v</span></code> and Hessian of a  given scalar function at a specified point.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.autograd.functional.vjp.html b/main/generated/torch.autograd.functional.vjp.html
index 292ac5a9cbc..51740ac799e 100644
--- a/main/generated/torch.autograd.functional.vjp.html
+++ b/main/generated/torch.autograd.functional.vjp.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.autograd.functional.vjp<a class="headerlink" href="#torch-autograd-functional-vjp" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.autograd.functional.vjp">
-<span class="sig-prename descclassname"><span class="pre">torch.autograd.functional.</span></span><span class="sig-name descname"><span class="pre">vjp</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">func</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inputs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">v</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">create_graph</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">strict</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd/functional.html#vjp"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/autograd/functional.py#L263"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.functional.vjp" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.autograd.functional.</span></span><span class="sig-name descname"><span class="pre">vjp</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">func</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inputs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">v</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">create_graph</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">strict</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd/functional.html#vjp"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/autograd/functional.py#L263"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.functional.vjp" title="Permalink to this definition">¶</a></dt>
 <dd><p>Compute the dot product between a vector <code class="docutils literal notranslate"><span class="pre">v</span></code> and the Jacobian of the given function at the point given by the inputs.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.autograd.grad.html b/main/generated/torch.autograd.grad.html
index 0e672bae28b..9d1f9220e0f 100644
--- a/main/generated/torch.autograd.grad.html
+++ b/main/generated/torch.autograd.grad.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.autograd.grad<a class="headerlink" href="#torch-autograd-grad" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.autograd.grad">
-<span class="sig-prename descclassname"><span class="pre">torch.autograd.</span></span><span class="sig-name descname"><span class="pre">grad</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">outputs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inputs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">grad_outputs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">retain_graph</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">create_graph</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">only_inputs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">allow_unused</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">is_grads_batched</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">materialize_grads</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd.html#grad"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/autograd/__init__.py#L364"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.grad" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.autograd.</span></span><span class="sig-name descname"><span class="pre">grad</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">outputs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inputs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">grad_outputs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">retain_graph</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">create_graph</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">only_inputs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">allow_unused</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">is_grads_batched</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">materialize_grads</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd.html#grad"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/autograd/__init__.py#L364"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.grad" title="Permalink to this definition">¶</a></dt>
 <dd><p>Compute and return the sum of gradients of outputs with respect to the inputs.</p>
 <p><code class="docutils literal notranslate"><span class="pre">grad_outputs</span></code> should be a sequence of length matching <code class="docutils literal notranslate"><span class="pre">output</span></code>
 containing the “vector” in vector-Jacobian product, usually the pre-computed
diff --git a/main/generated/torch.autograd.grad_mode.inference_mode.html b/main/generated/torch.autograd.grad_mode.inference_mode.html
index 41ecc923c0c..86d85412104 100644
--- a/main/generated/torch.autograd.grad_mode.inference_mode.html
+++ b/main/generated/torch.autograd.grad_mode.inference_mode.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>inference_mode<a class="headerlink" href="#inference-mode" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.autograd.grad_mode.inference_mode">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.autograd.grad_mode.</span></span><span class="sig-name descname"><span class="pre">inference_mode</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd/grad_mode.html#inference_mode"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/autograd/grad_mode.py#L206"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.grad_mode.inference_mode" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.autograd.grad_mode.</span></span><span class="sig-name descname"><span class="pre">inference_mode</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd/grad_mode.html#inference_mode"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/autograd/grad_mode.py#L206"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.grad_mode.inference_mode" title="Permalink to this definition">¶</a></dt>
 <dd><p>Context-manager that enables or disables inference mode.</p>
 <p>InferenceMode is a context manager analogous to <a class="reference internal" href="torch.no_grad.html#torch.no_grad" title="torch.autograd.grad_mode.no_grad"><code class="xref py py-class docutils literal notranslate"><span class="pre">no_grad</span></code></a>
 to be used when you are certain your operations will have no interactions
@@ -665,7 +665,7 @@ <h1>inference_mode<a class="headerlink" href="#inference-mode" title="Permalink
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.autograd.grad_mode.inference_mode.clone">
-<span class="sig-name descname"><span class="pre">clone</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd/grad_mode.html#inference_mode.clone"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/autograd/grad_mode.py#L276"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.grad_mode.inference_mode.clone" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">clone</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd/grad_mode.html#inference_mode.clone"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/autograd/grad_mode.py#L276"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.grad_mode.inference_mode.clone" title="Permalink to this definition">¶</a></dt>
 <dd><p>Create a copy of this class</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
diff --git a/main/generated/torch.autograd.grad_mode.set_grad_enabled.html b/main/generated/torch.autograd.grad_mode.set_grad_enabled.html
index 4fe2825cf87..3df21fe109c 100644
--- a/main/generated/torch.autograd.grad_mode.set_grad_enabled.html
+++ b/main/generated/torch.autograd.grad_mode.set_grad_enabled.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>set_grad_enabled<a class="headerlink" href="#set-grad-enabled" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.autograd.grad_mode.set_grad_enabled">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.autograd.grad_mode.</span></span><span class="sig-name descname"><span class="pre">set_grad_enabled</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mode</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd/grad_mode.html#set_grad_enabled"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/autograd/grad_mode.py#L143"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.grad_mode.set_grad_enabled" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.autograd.grad_mode.</span></span><span class="sig-name descname"><span class="pre">set_grad_enabled</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mode</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd/grad_mode.html#set_grad_enabled"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/autograd/grad_mode.py#L143"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.grad_mode.set_grad_enabled" title="Permalink to this definition">¶</a></dt>
 <dd><p>Context-manager that sets gradient calculation on or off.</p>
 <p><code class="docutils literal notranslate"><span class="pre">set_grad_enabled</span></code> will enable or disable grads based on its argument <code class="xref py py-attr docutils literal notranslate"><span class="pre">mode</span></code>.
 It can be used as a context-manager or as a function.</p>
@@ -656,7 +656,7 @@ <h1>set_grad_enabled<a class="headerlink" href="#set-grad-enabled" title="Permal
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.autograd.grad_mode.set_grad_enabled.clone">
-<span class="sig-name descname"><span class="pre">clone</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd/grad_mode.html#set_grad_enabled.clone"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/autograd/grad_mode.py#L199"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.grad_mode.set_grad_enabled.clone" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">clone</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd/grad_mode.html#set_grad_enabled.clone"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/autograd/grad_mode.py#L199"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.grad_mode.set_grad_enabled.clone" title="Permalink to this definition">¶</a></dt>
 <dd><p>Create a copy of this class</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
diff --git a/main/generated/torch.autograd.grad_mode.set_multithreading_enabled.html b/main/generated/torch.autograd.grad_mode.set_multithreading_enabled.html
index 942ce7959c9..8cc0ae5e968 100644
--- a/main/generated/torch.autograd.grad_mode.set_multithreading_enabled.html
+++ b/main/generated/torch.autograd.grad_mode.set_multithreading_enabled.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>set_multithreading_enabled<a class="headerlink" href="#set-multithreading-enabled" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.autograd.grad_mode.set_multithreading_enabled">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.autograd.grad_mode.</span></span><span class="sig-name descname"><span class="pre">set_multithreading_enabled</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mode</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd/grad_mode.html#set_multithreading_enabled"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/autograd/grad_mode.py#L293"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.grad_mode.set_multithreading_enabled" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.autograd.grad_mode.</span></span><span class="sig-name descname"><span class="pre">set_multithreading_enabled</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mode</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd/grad_mode.html#set_multithreading_enabled"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/autograd/grad_mode.py#L293"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.grad_mode.set_multithreading_enabled" title="Permalink to this definition">¶</a></dt>
 <dd><p>Context-manager that sets multithreaded backwards on or off.</p>
 <p><code class="docutils literal notranslate"><span class="pre">set_multithreading_enabled</span></code> will enable or disable multithreaded backwards based on its argument <code class="xref py py-attr docutils literal notranslate"><span class="pre">mode</span></code>.
 It can be used as a context-manager or as a function.</p>
@@ -630,7 +630,7 @@ <h1>set_multithreading_enabled<a class="headerlink" href="#set-multithreading-en
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.autograd.grad_mode.set_multithreading_enabled.clone">
-<span class="sig-name descname"><span class="pre">clone</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd/grad_mode.html#set_multithreading_enabled.clone"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/autograd/grad_mode.py#L322"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.grad_mode.set_multithreading_enabled.clone" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">clone</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd/grad_mode.html#set_multithreading_enabled.clone"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/autograd/grad_mode.py#L322"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.grad_mode.set_multithreading_enabled.clone" title="Permalink to this definition">¶</a></dt>
 <dd><p>Create a copy of this class</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
diff --git a/main/generated/torch.autograd.gradcheck.GradcheckError.html b/main/generated/torch.autograd.gradcheck.GradcheckError.html
index d28fee18cc4..0dc33dae44f 100644
--- a/main/generated/torch.autograd.gradcheck.GradcheckError.html
+++ b/main/generated/torch.autograd.gradcheck.GradcheckError.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.autograd.gradcheck.GradcheckError<a class="headerlink" href="#torch-autograd-gradcheck-gradcheckerror" title="Permalink to this heading">¶</a></h1>
 <dl class="py exception">
 <dt class="sig sig-object py" id="torch.autograd.gradcheck.GradcheckError">
-<em class="property"><span class="pre">exception</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.autograd.gradcheck.</span></span><span class="sig-name descname"><span class="pre">GradcheckError</span></span><a class="reference internal" href="../_modules/torch/autograd/gradcheck.html#GradcheckError"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/autograd/gradcheck.py#L30"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.gradcheck.GradcheckError" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">exception</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.autograd.gradcheck.</span></span><span class="sig-name descname"><span class="pre">GradcheckError</span></span><a class="reference internal" href="../_modules/torch/autograd/gradcheck.html#GradcheckError"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/autograd/gradcheck.py#L30"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.gradcheck.GradcheckError" title="Permalink to this definition">¶</a></dt>
 <dd><p>Error raised by <a class="reference internal" href="torch.autograd.gradcheck.gradcheck.html#torch.autograd.gradcheck.gradcheck" title="torch.autograd.gradcheck.gradcheck"><code class="xref py py-func docutils literal notranslate"><span class="pre">gradcheck()</span></code></a> and <a class="reference internal" href="torch.autograd.gradcheck.gradgradcheck.html#torch.autograd.gradcheck.gradgradcheck" title="torch.autograd.gradcheck.gradgradcheck"><code class="xref py py-func docutils literal notranslate"><span class="pre">gradgradcheck()</span></code></a>.</p>
 </dd></dl>
 
diff --git a/main/generated/torch.autograd.gradcheck.gradcheck.html b/main/generated/torch.autograd.gradcheck.gradcheck.html
index 5f011b12b63..0317cd9af74 100644
--- a/main/generated/torch.autograd.gradcheck.gradcheck.html
+++ b/main/generated/torch.autograd.gradcheck.gradcheck.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.autograd.gradcheck.gradcheck<a class="headerlink" href="#torch-autograd-gradcheck-gradcheck" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.autograd.gradcheck.gradcheck">
-<span class="sig-prename descclassname"><span class="pre">torch.autograd.gradcheck.</span></span><span class="sig-name descname"><span class="pre">gradcheck</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">func</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inputs</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-06</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">atol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-05</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">rtol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">raise_exception</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">nondet_tol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">check_undefined_grad</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">check_grad_dtypes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">check_batched_grad</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">check_batched_forward_grad</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">check_forward_ad</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">check_backward_ad</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fast_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">masked</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd/gradcheck.html#gradcheck"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/autograd/gradcheck.py#L1954"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.gradcheck.gradcheck" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.autograd.gradcheck.</span></span><span class="sig-name descname"><span class="pre">gradcheck</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">func</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inputs</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-06</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">atol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-05</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">rtol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">raise_exception</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">nondet_tol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">check_undefined_grad</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">check_grad_dtypes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">check_batched_grad</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">check_batched_forward_grad</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">check_forward_ad</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">check_backward_ad</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fast_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">masked</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd/gradcheck.html#gradcheck"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/autograd/gradcheck.py#L1954"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.gradcheck.gradcheck" title="Permalink to this definition">¶</a></dt>
 <dd><p>Check gradients computed via small finite differences against analytical
 gradients wrt tensors in <code class="xref py py-attr docutils literal notranslate"><span class="pre">inputs</span></code> that are of floating point or complex type
 and with <code class="docutils literal notranslate"><span class="pre">requires_grad=True</span></code>.</p>
diff --git a/main/generated/torch.autograd.gradcheck.gradgradcheck.html b/main/generated/torch.autograd.gradcheck.gradgradcheck.html
index c7fe4b15049..66b3e53ae1b 100644
--- a/main/generated/torch.autograd.gradcheck.gradgradcheck.html
+++ b/main/generated/torch.autograd.gradcheck.gradgradcheck.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.autograd.gradcheck.gradgradcheck<a class="headerlink" href="#torch-autograd-gradcheck-gradgradcheck" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.autograd.gradcheck.gradgradcheck">
-<span class="sig-prename descclassname"><span class="pre">torch.autograd.gradcheck.</span></span><span class="sig-name descname"><span class="pre">gradgradcheck</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">func</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inputs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">grad_outputs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-06</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">atol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-05</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">rtol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">gen_non_contig_grad_outputs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">raise_exception</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">nondet_tol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">check_undefined_grad</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">check_grad_dtypes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">check_batched_grad</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">check_fwd_over_rev</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">check_rev_over_rev</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fast_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">masked</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd/gradcheck.html#gradgradcheck"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/autograd/gradcheck.py#L2119"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.gradcheck.gradgradcheck" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.autograd.gradcheck.</span></span><span class="sig-name descname"><span class="pre">gradgradcheck</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">func</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inputs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">grad_outputs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-06</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">atol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-05</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">rtol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">gen_non_contig_grad_outputs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">raise_exception</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">nondet_tol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">check_undefined_grad</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">check_grad_dtypes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">check_batched_grad</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">check_fwd_over_rev</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">check_rev_over_rev</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fast_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">masked</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd/gradcheck.html#gradgradcheck"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/autograd/gradcheck.py#L2119"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.gradcheck.gradgradcheck" title="Permalink to this definition">¶</a></dt>
 <dd><p>Check gradients of gradients computed via small finite differences
 against analytical gradients wrt tensors in <code class="xref py py-attr docutils literal notranslate"><span class="pre">inputs</span></code> and
 <code class="xref py py-attr docutils literal notranslate"><span class="pre">grad_outputs</span></code> that are of floating point or complex type and with
diff --git a/main/generated/torch.autograd.graph.Node.metadata.html b/main/generated/torch.autograd.graph.Node.metadata.html
index 031cad25e96..11c572ba185 100644
--- a/main/generated/torch.autograd.graph.Node.metadata.html
+++ b/main/generated/torch.autograd.graph.Node.metadata.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.autograd.graph.Node.metadata<a class="headerlink" href="#torch-autograd-graph-node-metadata" title="Permalink to this heading">¶</a></h1>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.autograd.graph.Node.metadata">
-<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">Node.</span></span><span class="sig-name descname"><span class="pre">metadata</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd/graph.html#Node.metadata"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/autograd/graph.py#L68"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.graph.Node.metadata" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">Node.</span></span><span class="sig-name descname"><span class="pre">metadata</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd/graph.html#Node.metadata"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/autograd/graph.py#L68"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.graph.Node.metadata" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the metadata.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
diff --git a/main/generated/torch.autograd.graph.Node.name.html b/main/generated/torch.autograd.graph.Node.name.html
index 7e60c56e00c..db43c899f5d 100644
--- a/main/generated/torch.autograd.graph.Node.name.html
+++ b/main/generated/torch.autograd.graph.Node.name.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.autograd.graph.Node.name<a class="headerlink" href="#torch-autograd-graph-node-name" title="Permalink to this heading">¶</a></h1>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.autograd.graph.Node.name">
-<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">Node.</span></span><span class="sig-name descname"><span class="pre">name</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd/graph.html#Node.name"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/autograd/graph.py#L48"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.graph.Node.name" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">Node.</span></span><span class="sig-name descname"><span class="pre">name</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd/graph.html#Node.name"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/autograd/graph.py#L48"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.graph.Node.name" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the name.</p>
 <p>Example:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span> <span class="nn">torch</span>
diff --git a/main/generated/torch.autograd.graph.Node.next_functions.html b/main/generated/torch.autograd.graph.Node.next_functions.html
index 07ff3ef5394..e20152d6a82 100644
--- a/main/generated/torch.autograd.graph.Node.next_functions.html
+++ b/main/generated/torch.autograd.graph.Node.next_functions.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.autograd.graph.Node.register_hook.html b/main/generated/torch.autograd.graph.Node.register_hook.html
index b6c7b6bdd59..95991a518d0 100644
--- a/main/generated/torch.autograd.graph.Node.register_hook.html
+++ b/main/generated/torch.autograd.graph.Node.register_hook.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.autograd.graph.Node.register_hook<a class="headerlink" href="#torch-autograd-graph-node-register-hook" title="Permalink to this heading">¶</a></h1>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.autograd.graph.Node.register_hook">
-<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">Node.</span></span><span class="sig-name descname"><span class="pre">register_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">fn</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd/graph.html#Node.register_hook"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/autograd/graph.py#L82"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.graph.Node.register_hook" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">Node.</span></span><span class="sig-name descname"><span class="pre">register_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">fn</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd/graph.html#Node.register_hook"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/autograd/graph.py#L82"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.graph.Node.register_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register a backward hook.</p>
 <p>The hook will be called every time a gradient with respect to the
 Node is computed. The hook should have the following signature:</p>
diff --git a/main/generated/torch.autograd.graph.Node.register_prehook.html b/main/generated/torch.autograd.graph.Node.register_prehook.html
index cd0aabeff16..a4a26355fed 100644
--- a/main/generated/torch.autograd.graph.Node.register_prehook.html
+++ b/main/generated/torch.autograd.graph.Node.register_prehook.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.autograd.graph.Node.register_prehook<a class="headerlink" href="#torch-autograd-graph-node-register-prehook" title="Permalink to this heading">¶</a></h1>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.autograd.graph.Node.register_prehook">
-<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">Node.</span></span><span class="sig-name descname"><span class="pre">register_prehook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">fn</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd/graph.html#Node.register_prehook"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/autograd/graph.py#L127"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.graph.Node.register_prehook" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">Node.</span></span><span class="sig-name descname"><span class="pre">register_prehook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">fn</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd/graph.html#Node.register_prehook"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/autograd/graph.py#L127"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.graph.Node.register_prehook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register a backward pre-hook.</p>
 <p>The hook will be called every time a gradient with respect to the
 Node is computed. The hook should have the following signature:</p>
diff --git a/main/generated/torch.autograd.graph.increment_version.html b/main/generated/torch.autograd.graph.increment_version.html
index f6bcd165b87..3f2efeb38eb 100644
--- a/main/generated/torch.autograd.graph.increment_version.html
+++ b/main/generated/torch.autograd.graph.increment_version.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.autograd.graph.increment_version<a class="headerlink" href="#torch-autograd-graph-increment-version" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.autograd.graph.increment_version">
-<span class="sig-prename descclassname"><span class="pre">torch.autograd.graph.</span></span><span class="sig-name descname"><span class="pre">increment_version</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tensor</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd/graph.html#increment_version"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/autograd/graph.py#L217"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.graph.increment_version" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.autograd.graph.</span></span><span class="sig-name descname"><span class="pre">increment_version</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tensor</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd/graph.html#increment_version"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/autograd/graph.py#L217"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.graph.increment_version" title="Permalink to this definition">¶</a></dt>
 <dd><p>Update autograd metadata tracking whether the given Tensor was modified in place.</p>
 <p>This is to enable more accurate error checking within the autograd engine.
 It is already done automatically by PyTorch functions and within custom Function
diff --git a/main/generated/torch.autograd.profiler.EnforceUnique.html b/main/generated/torch.autograd.profiler.EnforceUnique.html
index ddc9385fd31..77b7c025ddf 100644
--- a/main/generated/torch.autograd.profiler.EnforceUnique.html
+++ b/main/generated/torch.autograd.profiler.EnforceUnique.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,11 +612,11 @@
 <h1>EnforceUnique<a class="headerlink" href="#enforceunique" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.autograd.profiler.EnforceUnique">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.autograd.profiler.</span></span><span class="sig-name descname"><span class="pre">EnforceUnique</span></span><a class="reference internal" href="../_modules/torch/autograd/profiler.html#EnforceUnique"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/autograd/profiler.py#L1020"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.profiler.EnforceUnique" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.autograd.profiler.</span></span><span class="sig-name descname"><span class="pre">EnforceUnique</span></span><a class="reference internal" href="../_modules/torch/autograd/profiler.html#EnforceUnique"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/autograd/profiler.py#L1020"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.profiler.EnforceUnique" title="Permalink to this definition">¶</a></dt>
 <dd><p>Raises an error if a key is seen more than once.</p>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.autograd.profiler.EnforceUnique.see">
-<span class="sig-name descname"><span class="pre">see</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">key</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd/profiler.html#EnforceUnique.see"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/autograd/profiler.py#L1026"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.profiler.EnforceUnique.see" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">see</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">key</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd/profiler.html#EnforceUnique.see"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/autograd/profiler.py#L1026"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.profiler.EnforceUnique.see" title="Permalink to this definition">¶</a></dt>
 <dd><p>Observe a key and raise an error if it is seen multiple times.</p>
 </dd></dl>
 
diff --git a/main/generated/torch.autograd.profiler.KinetoStepTracker.html b/main/generated/torch.autograd.profiler.KinetoStepTracker.html
index 3ce77e8e5d0..f7aad1e7ddf 100644
--- a/main/generated/torch.autograd.profiler.KinetoStepTracker.html
+++ b/main/generated/torch.autograd.profiler.KinetoStepTracker.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>KinetoStepTracker<a class="headerlink" href="#kinetosteptracker" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.autograd.profiler.KinetoStepTracker">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.autograd.profiler.</span></span><span class="sig-name descname"><span class="pre">KinetoStepTracker</span></span><a class="reference internal" href="../_modules/torch/autograd/profiler.html#KinetoStepTracker"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/autograd/profiler.py#L1102"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.profiler.KinetoStepTracker" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.autograd.profiler.</span></span><span class="sig-name descname"><span class="pre">KinetoStepTracker</span></span><a class="reference internal" href="../_modules/torch/autograd/profiler.html#KinetoStepTracker"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/autograd/profiler.py#L1102"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.profiler.KinetoStepTracker" title="Permalink to this definition">¶</a></dt>
 <dd><p>Provides an abstraction for incrementing the step count globally.</p>
 <p>Previously, we only had one place to mark that a step() has occurred
 in the program via pytorch profiler step(). We will now add step hooks
@@ -647,7 +647,7 @@ <h1>KinetoStepTracker<a class="headerlink" href="#kinetosteptracker" title="Perm
 for now. The result could be incorrect increments of the step count.</p>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.autograd.profiler.KinetoStepTracker.current_step">
-<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">current_step</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd/profiler.html#KinetoStepTracker.current_step"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/autograd/profiler.py#L1185"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.profiler.KinetoStepTracker.current_step" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">current_step</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd/profiler.html#KinetoStepTracker.current_step"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/autograd/profiler.py#L1185"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.profiler.KinetoStepTracker.current_step" title="Permalink to this definition">¶</a></dt>
 <dd><p>Get the latest step for any requester</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -658,7 +658,7 @@ <h1>KinetoStepTracker<a class="headerlink" href="#kinetosteptracker" title="Perm
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.autograd.profiler.KinetoStepTracker.erase_step_count">
-<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">erase_step_count</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">requester</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd/profiler.html#KinetoStepTracker.erase_step_count"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/autograd/profiler.py#L1154"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.profiler.KinetoStepTracker.erase_step_count" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">erase_step_count</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">requester</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd/profiler.html#KinetoStepTracker.erase_step_count"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/autograd/profiler.py#L1154"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.profiler.KinetoStepTracker.erase_step_count" title="Permalink to this definition">¶</a></dt>
 <dd><p>Remove a given requester.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -669,7 +669,7 @@ <h1>KinetoStepTracker<a class="headerlink" href="#kinetosteptracker" title="Perm
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.autograd.profiler.KinetoStepTracker.increment_step">
-<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">increment_step</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">requester</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd/profiler.html#KinetoStepTracker.increment_step"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/autograd/profiler.py#L1161"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.profiler.KinetoStepTracker.increment_step" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">increment_step</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">requester</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd/profiler.html#KinetoStepTracker.increment_step"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/autograd/profiler.py#L1161"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.profiler.KinetoStepTracker.increment_step" title="Permalink to this definition">¶</a></dt>
 <dd><p>Increments the step count for the requester.</p>
 <p>Additionally if the max over all step counts has incremented then
 trigger the _kineto_step() returns global step count</p>
@@ -682,7 +682,7 @@ <h1>KinetoStepTracker<a class="headerlink" href="#kinetosteptracker" title="Perm
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.autograd.profiler.KinetoStepTracker.init_step_count">
-<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">init_step_count</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">requester</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd/profiler.html#KinetoStepTracker.init_step_count"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/autograd/profiler.py#L1147"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.profiler.KinetoStepTracker.init_step_count" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">init_step_count</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">requester</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd/profiler.html#KinetoStepTracker.init_step_count"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/autograd/profiler.py#L1147"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.profiler.KinetoStepTracker.init_step_count" title="Permalink to this definition">¶</a></dt>
 <dd><p>Initialize for a given requester.</p>
 <dl class="field-list simple">
 </dl>
diff --git a/main/generated/torch.autograd.profiler.load_nvprof.html b/main/generated/torch.autograd.profiler.load_nvprof.html
index 0c6bba9dcfd..7ebecfaf72e 100644
--- a/main/generated/torch.autograd.profiler.load_nvprof.html
+++ b/main/generated/torch.autograd.profiler.load_nvprof.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.autograd.profiler.load_nvprof<a class="headerlink" href="#torch-autograd-profiler-load-nvprof" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.autograd.profiler.load_nvprof">
-<span class="sig-prename descclassname"><span class="pre">torch.autograd.profiler.</span></span><span class="sig-name descname"><span class="pre">load_nvprof</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">path</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd/profiler.html#load_nvprof"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/autograd/profiler.py#L1011"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.profiler.load_nvprof" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.autograd.profiler.</span></span><span class="sig-name descname"><span class="pre">load_nvprof</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">path</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd/profiler.html#load_nvprof"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/autograd/profiler.py#L1011"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.profiler.load_nvprof" title="Permalink to this definition">¶</a></dt>
 <dd><p>Open an nvprof trace file and parses autograd annotations.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.autograd.profiler.parse_nvprof_trace.html b/main/generated/torch.autograd.profiler.parse_nvprof_trace.html
index 74ce2289638..52ed4536a04 100644
--- a/main/generated/torch.autograd.profiler.parse_nvprof_trace.html
+++ b/main/generated/torch.autograd.profiler.parse_nvprof_trace.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.autograd.profiler.parse_nvprof_trace<a class="headerlink" href="#torch-autograd-profiler-parse-nvprof-trace" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.autograd.profiler.parse_nvprof_trace">
-<span class="sig-prename descclassname"><span class="pre">torch.autograd.profiler.</span></span><span class="sig-name descname"><span class="pre">parse_nvprof_trace</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">path</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd/profiler.html#parse_nvprof_trace"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/autograd/profiler.py#L1035"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.profiler.parse_nvprof_trace" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.autograd.profiler.</span></span><span class="sig-name descname"><span class="pre">parse_nvprof_trace</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">path</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd/profiler.html#parse_nvprof_trace"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/autograd/profiler.py#L1035"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.profiler.parse_nvprof_trace" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 </div>
diff --git a/main/generated/torch.autograd.profiler.profile.export_chrome_trace.html b/main/generated/torch.autograd.profiler.profile.export_chrome_trace.html
index 3919ad386f6..2894b4a2ed2 100644
--- a/main/generated/torch.autograd.profiler.profile.export_chrome_trace.html
+++ b/main/generated/torch.autograd.profiler.profile.export_chrome_trace.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.autograd.profiler.profile.export_chrome_trace<a class="headerlink" href="#torch-autograd-profiler-profile-export-chrome-trace" title="Permalink to this heading">¶</a></h1>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.autograd.profiler.profile.export_chrome_trace">
-<span class="sig-prename descclassname"><span class="pre">profile.</span></span><span class="sig-name descname"><span class="pre">export_chrome_trace</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">path</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd/profiler.html#profile.export_chrome_trace"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/autograd/profiler.py#L473"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.profiler.profile.export_chrome_trace" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">profile.</span></span><span class="sig-name descname"><span class="pre">export_chrome_trace</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">path</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd/profiler.html#profile.export_chrome_trace"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/autograd/profiler.py#L473"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.profiler.profile.export_chrome_trace" title="Permalink to this definition">¶</a></dt>
 <dd><p>Export an EventList as a Chrome tracing tools file.</p>
 <p>The checkpoint can be later loaded and inspected under <code class="docutils literal notranslate"><span class="pre">chrome://tracing</span></code> URL.</p>
 <dl class="field-list simple">
diff --git a/main/generated/torch.autograd.profiler.profile.key_averages.html b/main/generated/torch.autograd.profiler.profile.key_averages.html
index cc7e60c3fa3..a8eac5649a5 100644
--- a/main/generated/torch.autograd.profiler.profile.key_averages.html
+++ b/main/generated/torch.autograd.profiler.profile.key_averages.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.autograd.profiler.profile.key_averages<a class="headerlink" href="#torch-autograd-profiler-profile-key-averages" title="Permalink to this heading">¶</a></h1>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.autograd.profiler.profile.key_averages">
-<span class="sig-prename descclassname"><span class="pre">profile.</span></span><span class="sig-name descname"><span class="pre">key_averages</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">group_by_input_shape</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group_by_stack_n</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd/profiler.html#profile.key_averages"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/autograd/profiler.py#L500"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.profiler.profile.key_averages" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">profile.</span></span><span class="sig-name descname"><span class="pre">key_averages</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">group_by_input_shape</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group_by_stack_n</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd/profiler.html#profile.key_averages"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/autograd/profiler.py#L500"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.profiler.profile.key_averages" title="Permalink to this definition">¶</a></dt>
 <dd><p>Averages all function events over their keys.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.autograd.profiler.profile.self_cpu_time_total.html b/main/generated/torch.autograd.profiler.profile.self_cpu_time_total.html
index 7e64ed7eb2a..38c1cbc65f6 100644
--- a/main/generated/torch.autograd.profiler.profile.self_cpu_time_total.html
+++ b/main/generated/torch.autograd.profiler.profile.self_cpu_time_total.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.autograd.profiler.profile.total_average.html b/main/generated/torch.autograd.profiler.profile.total_average.html
index 5e64c98c806..c2b2a23fee0 100644
--- a/main/generated/torch.autograd.profiler.profile.total_average.html
+++ b/main/generated/torch.autograd.profiler.profile.total_average.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.autograd.profiler.profile.total_average<a class="headerlink" href="#torch-autograd-profiler-profile-total-average" title="Permalink to this heading">¶</a></h1>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.autograd.profiler.profile.total_average">
-<span class="sig-prename descclassname"><span class="pre">profile.</span></span><span class="sig-name descname"><span class="pre">total_average</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd/profiler.html#profile.total_average"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/autograd/profiler.py#L509"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.profiler.profile.total_average" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">profile.</span></span><span class="sig-name descname"><span class="pre">total_average</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd/profiler.html#profile.total_average"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/autograd/profiler.py#L509"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.profiler.profile.total_average" title="Permalink to this definition">¶</a></dt>
 <dd><p>Averages all events.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Returns</dt>
diff --git a/main/generated/torch.autograd.profiler.record_function.html b/main/generated/torch.autograd.profiler.record_function.html
index 37b540d19c0..834ec2bfee8 100644
--- a/main/generated/torch.autograd.profiler.record_function.html
+++ b/main/generated/torch.autograd.profiler.record_function.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>record_function<a class="headerlink" href="#record-function" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.autograd.profiler.record_function">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.autograd.profiler.</span></span><span class="sig-name descname"><span class="pre">record_function</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd/profiler.html#record_function"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/autograd/profiler.py#L703"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.profiler.record_function" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.autograd.profiler.</span></span><span class="sig-name descname"><span class="pre">record_function</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd/profiler.html#record_function"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/autograd/profiler.py#L703"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.profiler.record_function" title="Permalink to this definition">¶</a></dt>
 <dd><p>Context manager/function decorator that adds a label to a code block/function when running autograd profiler.
 Label will only appear if CPU activity tracing is enabled.</p>
 <p>It is useful when tracing the code profile.</p>
diff --git a/main/generated/torch.autograd.profiler_util.Interval.html b/main/generated/torch.autograd.profiler_util.Interval.html
index 189a2c3ac32..2fda9fcdbac 100644
--- a/main/generated/torch.autograd.profiler_util.Interval.html
+++ b/main/generated/torch.autograd.profiler_util.Interval.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,10 +612,10 @@
 <h1>Interval<a class="headerlink" href="#interval" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.autograd.profiler_util.Interval">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.autograd.profiler_util.</span></span><span class="sig-name descname"><span class="pre">Interval</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">start</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">end</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd/profiler_util.html#Interval"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/autograd/profiler_util.py#L426"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.profiler_util.Interval" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.autograd.profiler_util.</span></span><span class="sig-name descname"><span class="pre">Interval</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">start</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">end</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd/profiler_util.html#Interval"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/autograd/profiler_util.py#L426"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.profiler_util.Interval" title="Permalink to this definition">¶</a></dt>
 <dd><dl class="py method">
 <dt class="sig sig-object py" id="torch.autograd.profiler_util.Interval.elapsed_us">
-<span class="sig-name descname"><span class="pre">elapsed_us</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd/profiler_util.html#Interval.elapsed_us"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/autograd/profiler_util.py#L431"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.profiler_util.Interval.elapsed_us" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">elapsed_us</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd/profiler_util.html#Interval.elapsed_us"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/autograd/profiler_util.py#L431"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.profiler_util.Interval.elapsed_us" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns the length of the interval</p>
 </dd></dl>
 
diff --git a/main/generated/torch.autograd.profiler_util.Kernel.html b/main/generated/torch.autograd.profiler_util.Kernel.html
index bd6391d7098..efbbcd20bd2 100644
--- a/main/generated/torch.autograd.profiler_util.Kernel.html
+++ b/main/generated/torch.autograd.profiler_util.Kernel.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.autograd.profiler_util.MemRecordsAcc.html b/main/generated/torch.autograd.profiler_util.MemRecordsAcc.html
index 0fe04d49cb1..1f0970741de 100644
--- a/main/generated/torch.autograd.profiler_util.MemRecordsAcc.html
+++ b/main/generated/torch.autograd.profiler_util.MemRecordsAcc.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,11 +612,11 @@
 <h1>MemRecordsAcc<a class="headerlink" href="#memrecordsacc" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.autograd.profiler_util.MemRecordsAcc">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.autograd.profiler_util.</span></span><span class="sig-name descname"><span class="pre">MemRecordsAcc</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mem_records</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd/profiler_util.html#MemRecordsAcc"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/autograd/profiler_util.py#L732"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.profiler_util.MemRecordsAcc" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.autograd.profiler_util.</span></span><span class="sig-name descname"><span class="pre">MemRecordsAcc</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mem_records</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd/profiler_util.html#MemRecordsAcc"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/autograd/profiler_util.py#L732"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.profiler_util.MemRecordsAcc" title="Permalink to this definition">¶</a></dt>
 <dd><p>Acceleration structure for accessing mem_records in interval.</p>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.autograd.profiler_util.MemRecordsAcc.in_interval">
-<span class="sig-name descname"><span class="pre">in_interval</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">start_us</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">end_us</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd/profiler_util.html#MemRecordsAcc.in_interval"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/autograd/profiler_util.py#L743"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.profiler_util.MemRecordsAcc.in_interval" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">in_interval</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">start_us</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">end_us</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd/profiler_util.html#MemRecordsAcc.in_interval"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/autograd/profiler_util.py#L743"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.profiler_util.MemRecordsAcc.in_interval" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return all records in the given interval
 To maintain backward compatibility, convert us to ns in function</p>
 </dd></dl>
diff --git a/main/generated/torch.autograd.profiler_util.StringTable.html b/main/generated/torch.autograd.profiler_util.StringTable.html
index 35a45822236..d2e1c4ea15c 100644
--- a/main/generated/torch.autograd.profiler_util.StringTable.html
+++ b/main/generated/torch.autograd.profiler_util.StringTable.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
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@@ -612,7 +612,7 @@
 <h1>StringTable<a class="headerlink" href="#stringtable" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.autograd.profiler_util.StringTable">
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+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.autograd.profiler_util.</span></span><span class="sig-name descname"><span class="pre">StringTable</span></span><a class="reference internal" href="../_modules/torch/autograd/profiler_util.html#StringTable"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/autograd/profiler_util.py#L723"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.autograd.profiler_util.StringTable" title="Permalink to this definition">¶</a></dt>
 <dd><dl class="py method">
 <dt class="sig sig-object py" id="torch.autograd.profiler_util.StringTable.clear">
 <span class="sig-name descname"><span class="pre">clear</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">None.</span>&#160; <span class="pre">Remove</span> <span class="pre">all</span> <span class="pre">items</span> <span class="pre">from</span> <span class="pre">D.</span></span></span><a class="headerlink" href="#torch.autograd.profiler_util.StringTable.clear" title="Permalink to this definition">¶</a></dt>
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@@ -612,7 +612,7 @@
 <h1>torch.block_diag<a class="headerlink" href="#torch-block-diag" title="Permalink to this heading">¶</a></h1>
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 <dt class="sig sig-object py" id="torch.block_diag">
-<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">block_diag</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">tensors</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/functional.html#block_diag"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/functional.py#L1401"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.block_diag" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">block_diag</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">tensors</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/functional.html#block_diag"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/functional.py#L1401"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.block_diag" title="Permalink to this definition">¶</a></dt>
 <dd><p>Create a block diagonal matrix from provided tensors.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
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@@ -612,7 +612,7 @@
 <h1>torch.broadcast_shapes<a class="headerlink" href="#torch-broadcast-shapes" title="Permalink to this heading">¶</a></h1>
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 <dt class="sig sig-object py" id="torch.broadcast_shapes">
-<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">broadcast_shapes</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">shapes</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="../size.html#torch.Size" title="torch.Size"><span class="pre">Size</span></a></span></span><a class="reference internal" href="../_modules/torch/functional.html#broadcast_shapes"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/functional.py#L80"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.broadcast_shapes" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">broadcast_shapes</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">shapes</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="../size.html#torch.Size" title="torch.Size"><span class="pre">Size</span></a></span></span><a class="reference internal" href="../_modules/torch/functional.html#broadcast_shapes"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/functional.py#L80"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.broadcast_shapes" title="Permalink to this definition">¶</a></dt>
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 <p>This is equivalent to
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diff --git a/main/generated/torch.broadcast_tensors.html b/main/generated/torch.broadcast_tensors.html
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@@ -612,7 +612,7 @@
 <h1>torch.broadcast_tensors<a class="headerlink" href="#torch-broadcast-tensors" title="Permalink to this heading">¶</a></h1>
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 <dt class="sig sig-object py" id="torch.broadcast_tensors">
-<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">broadcast_tensors</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">tensors</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">List</span> <span class="pre">of</span> <span class="pre">Tensors</span></span></span><a class="reference internal" href="../_modules/torch/functional.html#broadcast_tensors"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/functional.py#L48"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.broadcast_tensors" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">broadcast_tensors</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">tensors</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">List</span> <span class="pre">of</span> <span class="pre">Tensors</span></span></span><a class="reference internal" href="../_modules/torch/functional.html#broadcast_tensors"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/functional.py#L48"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.broadcast_tensors" title="Permalink to this definition">¶</a></dt>
 <dd><p>Broadcasts the given tensors according to <a class="reference internal" href="../notes/broadcasting.html#broadcasting-semantics"><span class="std std-ref">Broadcasting semantics</span></a>.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
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           <div class="pytorch-left-menu-search">
             
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@@ -612,7 +612,7 @@
 <h1>torch.cartesian_prod<a class="headerlink" href="#torch-cartesian-prod" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.cartesian_prod">
-<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">cartesian_prod</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">tensors</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/functional.html#cartesian_prod"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/functional.py#L1366"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cartesian_prod" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">cartesian_prod</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">tensors</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/functional.html#cartesian_prod"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/functional.py#L1366"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cartesian_prod" title="Permalink to this definition">¶</a></dt>
 <dd><p>Do cartesian product of the given sequence of tensors. The behavior is similar to
 python’s <cite>itertools.product</cite>.</p>
 <dl class="field-list simple">
diff --git a/main/generated/torch.cat.html b/main/generated/torch.cat.html
index 06fd6ad8286..c1edf7f87c2 100644
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diff --git a/main/generated/torch.cdist.html b/main/generated/torch.cdist.html
index db187d8dbda..b1670068d3a 100644
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@@ -612,7 +612,7 @@
 <h1>torch.cdist<a class="headerlink" href="#torch-cdist" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.cdist">
-<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">cdist</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">x2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">p</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">compute_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'use_mm_for_euclid_dist_if_necessary'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/functional.html#cdist"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/functional.py#L1437"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cdist" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">cdist</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">x2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">p</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">compute_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'use_mm_for_euclid_dist_if_necessary'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/functional.html#cdist"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/functional.py#L1437"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cdist" title="Permalink to this definition">¶</a></dt>
 <dd><p>Computes batched the p-norm distance between each pair of the two collections of row vectors.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.ceil.html b/main/generated/torch.ceil.html
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 <h1>torch.chain_matmul<a class="headerlink" href="#torch-chain-matmul" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.chain_matmul">
-<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">chain_matmul</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">matrices</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/functional.html#chain_matmul"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/functional.py#L1997"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.chain_matmul" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">chain_matmul</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">matrices</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/functional.html#chain_matmul"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/functional.py#L1997"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.chain_matmul" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns the matrix product of the <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>N</mi></mrow><annotation encoding="application/x-tex">N</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.6833em;"></span><span class="mord mathnormal" style="margin-right:0.10903em;">N</span></span></span></span></span> 2-D tensors. This product is efficiently computed
 using the matrix chain order algorithm which selects the order in which incurs the lowest cost in terms
 of arithmetic operations (<a class="reference external" href="https://mitpress.mit.edu/books/introduction-algorithms-third-edition">[CLRS]</a>). Note that since this is a function to compute the product, <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>N</mi></mrow><annotation encoding="application/x-tex">N</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.6833em;"></span><span class="mord mathnormal" style="margin-right:0.10903em;">N</span></span></span></span></span>
diff --git a/main/generated/torch.cholesky.html b/main/generated/torch.cholesky.html
index 0ff10cbfa57..d87492817ca 100644
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index 70cf28d25b5..4da9f1f8417 100644
--- a/main/generated/torch.cholesky_solve.html
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@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
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index 901bc796408..ad86f42256a 100644
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index 910ee2c42b2..3e3a984f24f 100644
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           <div class="pytorch-left-menu-search">
             
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diff --git a/main/generated/torch.compile.html b/main/generated/torch.compile.html
index fb956dc85f3..a15b5ad168b 100644
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           <div class="pytorch-left-menu-search">
             
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@@ -612,7 +612,7 @@
 <h1>torch.compile<a class="headerlink" href="#torch-compile" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.compile">
-<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">compile</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Callable" title="(in Python v3.13)"><span class="pre">Callable</span></a><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><span class="pre">_InputT</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">_RetT</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span 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class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">backend</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Union" title="(in Python v3.13)"><span class="pre">Union</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Callable" title="(in Python v3.13)"><span class="pre">Callable</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'inductor'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mode</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Optional" title="(in Python v3.13)"><span class="pre">Optional</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">options</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Optional" title="(in Python v3.13)"><span class="pre">Optional</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.13)"><span class="pre">dict</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Union" title="(in Python v3.13)"><span class="pre">Union</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><span class="pre">int</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">disable</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Callable" title="(in Python v3.13)"><span class="pre">Callable</span></a><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><span class="pre">_InputT</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">_RetT</span><span class="p"><span class="pre">]</span></span></span></span><a class="reference internal" href="../_modules/torch.html#compile"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/__init__.py#L2421"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.compile" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">compile</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Callable" title="(in Python v3.13)"><span class="pre">Callable</span></a><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><span class="pre">_InputT</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">_RetT</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fullgraph</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dynamic</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Optional" title="(in Python v3.13)"><span class="pre">Optional</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">backend</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Union" title="(in Python v3.13)"><span class="pre">Union</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Callable" title="(in Python v3.13)"><span class="pre">Callable</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'inductor'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mode</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Optional" title="(in Python v3.13)"><span class="pre">Optional</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">options</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Optional" title="(in Python v3.13)"><span class="pre">Optional</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.13)"><span class="pre">dict</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Union" title="(in Python v3.13)"><span class="pre">Union</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><span class="pre">int</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">disable</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Callable" title="(in Python v3.13)"><span class="pre">Callable</span></a><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><span class="pre">_InputT</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">_RetT</span><span class="p"><span class="pre">]</span></span></span></span><a class="reference internal" href="../_modules/torch.html#compile"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/__init__.py#L2421"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.compile" title="Permalink to this definition">¶</a></dt>
 <dt class="sig sig-object py">
 <span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">compile</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fullgraph</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dynamic</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Optional" title="(in Python v3.13)"><span class="pre">Optional</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">backend</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Union" title="(in Python v3.13)"><span class="pre">Union</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Callable" title="(in Python v3.13)"><span class="pre">Callable</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'inductor'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mode</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Optional" title="(in Python v3.13)"><span class="pre">Optional</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">options</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Optional" title="(in Python v3.13)"><span class="pre">Optional</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.13)"><span class="pre">dict</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Union" title="(in Python v3.13)"><span class="pre">Union</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><span class="pre">int</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">disable</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Callable" title="(in Python v3.13)"><span class="pre">Callable</span></a><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Callable" title="(in Python v3.13)"><span class="pre">Callable</span></a><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><span class="pre">_InputT</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">_RetT</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Callable" title="(in Python v3.13)"><span class="pre">Callable</span></a><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><span class="pre">_InputT</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">_RetT</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span></span></dt>
 <dd><p>Optimizes given model/function using TorchDynamo and specified backend.
diff --git a/main/generated/torch.compiled_with_cxx11_abi.html b/main/generated/torch.compiled_with_cxx11_abi.html
index aed57b35e2e..11d0a622951 100644
--- a/main/generated/torch.compiled_with_cxx11_abi.html
+++ b/main/generated/torch.compiled_with_cxx11_abi.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.compiled_with_cxx11_abi<a class="headerlink" href="#torch-compiled-with-cxx11-abi" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.compiled_with_cxx11_abi">
-<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">compiled_with_cxx11_abi</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch.html#compiled_with_cxx11_abi"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/__init__.py#L2215"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.compiled_with_cxx11_abi" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">compiled_with_cxx11_abi</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch.html#compiled_with_cxx11_abi"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/__init__.py#L2215"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.compiled_with_cxx11_abi" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns whether PyTorch was built with _GLIBCXX_USE_CXX11_ABI=1</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
diff --git a/main/generated/torch.compiler.allow_in_graph.html b/main/generated/torch.compiler.allow_in_graph.html
index a8b7afe33b9..d5b3101edd0 100644
--- a/main/generated/torch.compiler.allow_in_graph.html
+++ b/main/generated/torch.compiler.allow_in_graph.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>torch.compiler.allow_in_graph<a class="headerlink" href="#torch-compiler-allow-in-graph" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.compiler.allow_in_graph">
-<span class="sig-prename descclassname"><span class="pre">torch.compiler.</span></span><span class="sig-name descname"><span class="pre">allow_in_graph</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">fn</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/compiler.html#allow_in_graph"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/compiler/__init__.py#L53"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.compiler.allow_in_graph" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.compiler.</span></span><span class="sig-name descname"><span class="pre">allow_in_graph</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">fn</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/compiler.html#allow_in_graph"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/compiler/__init__.py#L53"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.compiler.allow_in_graph" title="Permalink to this definition">¶</a></dt>
 <dd><p>Tells the compiler frontend (Dynamo) to skip symbolic introspection of the function
 and instead directly write it to the graph when encountered.</p>
 <p>If you are using <a class="reference internal" href="torch.compile.html#torch.compile" title="torch.compile"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.compile()</span></code></a> (with backend=”inductor” (the default)), or
diff --git a/main/generated/torch.compiler.assume_constant_result.html b/main/generated/torch.compiler.assume_constant_result.html
index 4afffc17fb6..95660ed8f06 100644
--- a/main/generated/torch.compiler.assume_constant_result.html
+++ b/main/generated/torch.compiler.assume_constant_result.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>torch.compiler.assume_constant_result<a class="headerlink" href="#torch-compiler-assume-constant-result" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.compiler.assume_constant_result">
-<span class="sig-prename descclassname"><span class="pre">torch.compiler.</span></span><span class="sig-name descname"><span class="pre">assume_constant_result</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">fn</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/compiler.html#assume_constant_result"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/compiler/__init__.py#L209"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.compiler.assume_constant_result" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.compiler.</span></span><span class="sig-name descname"><span class="pre">assume_constant_result</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">fn</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/compiler.html#assume_constant_result"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/compiler/__init__.py#L209"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.compiler.assume_constant_result" title="Permalink to this definition">¶</a></dt>
 <dd><p>This function is used to mark a function <cite>fn</cite> as having a constant result.
 This allows the compiler to optimize away your function.
 Returns The same function <cite>fn</cite></p>
diff --git a/main/generated/torch.compiler.compile.html b/main/generated/torch.compiler.compile.html
index 4150f27b0c7..0165d363bb4 100644
--- a/main/generated/torch.compiler.compile.html
+++ b/main/generated/torch.compiler.compile.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>torch.compiler.compile<a class="headerlink" href="#torch-compiler-compile" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.compiler.compile">
-<span class="sig-prename descclassname"><span class="pre">torch.compiler.</span></span><span class="sig-name descname"><span class="pre">compile</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/compiler.html#compile"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/compiler/__init__.py#L35"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.compiler.compile" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.compiler.</span></span><span class="sig-name descname"><span class="pre">compile</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/compiler.html#compile"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/compiler/__init__.py#L35"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.compiler.compile" title="Permalink to this definition">¶</a></dt>
 <dd><p>See <a class="reference internal" href="torch.compile.html#torch.compile" title="torch.compile"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.compile()</span></code></a> for details on the arguments for this function.</p>
 </dd></dl>
 
diff --git a/main/generated/torch.compiler.cudagraph_mark_step_begin.html b/main/generated/torch.compiler.cudagraph_mark_step_begin.html
index d72ab7650e7..b0e05deb5c5 100644
--- a/main/generated/torch.compiler.cudagraph_mark_step_begin.html
+++ b/main/generated/torch.compiler.cudagraph_mark_step_begin.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>torch.compiler.cudagraph_mark_step_begin<a class="headerlink" href="#torch-compiler-cudagraph-mark-step-begin" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.compiler.cudagraph_mark_step_begin">
-<span class="sig-prename descclassname"><span class="pre">torch.compiler.</span></span><span class="sig-name descname"><span class="pre">cudagraph_mark_step_begin</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/compiler.html#cudagraph_mark_step_begin"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/compiler/__init__.py#L315"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.compiler.cudagraph_mark_step_begin" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.compiler.</span></span><span class="sig-name descname"><span class="pre">cudagraph_mark_step_begin</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/compiler.html#cudagraph_mark_step_begin"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/compiler/__init__.py#L315"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.compiler.cudagraph_mark_step_begin" title="Permalink to this definition">¶</a></dt>
 <dd><p>Indicates that a new iteration of inference or training is about to begin.</p>
 <p>CUDA Graphs will free tensors of a prior iteration. A new iteration is started on each invocation of
 torch.compile, so long as there is not a pending backward that has not been called.</p>
diff --git a/main/generated/torch.compiler.disable.html b/main/generated/torch.compiler.disable.html
index e3afd50a2b5..c1a07190a36 100644
--- a/main/generated/torch.compiler.disable.html
+++ b/main/generated/torch.compiler.disable.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>torch.compiler.disable<a class="headerlink" href="#torch-compiler-disable" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.compiler.disable">
-<span class="sig-prename descclassname"><span class="pre">torch.compiler.</span></span><span class="sig-name descname"><span class="pre">disable</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">fn</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">recursive</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/compiler.html#disable"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/compiler/__init__.py#L228"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.compiler.disable" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.compiler.</span></span><span class="sig-name descname"><span class="pre">disable</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">fn</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">recursive</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/compiler.html#disable"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/compiler/__init__.py#L228"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.compiler.disable" title="Permalink to this definition">¶</a></dt>
 <dd><p>This function provides a decorator to disable compilation on a function.
 It also provides the option of recursively disabling called functions.</p>
 <dl class="field-list simple">
diff --git a/main/generated/torch.compiler.is_compiling.html b/main/generated/torch.compiler.is_compiling.html
index e875387e475..a59dbcf34ec 100644
--- a/main/generated/torch.compiler.is_compiling.html
+++ b/main/generated/torch.compiler.is_compiling.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>torch.compiler.is_compiling<a class="headerlink" href="#torch-compiler-is-compiling" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.compiler.is_compiling">
-<span class="sig-prename descclassname"><span class="pre">torch.compiler.</span></span><span class="sig-name descname"><span class="pre">is_compiling</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/compiler.html#is_compiling"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/compiler/__init__.py#L377"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.compiler.is_compiling" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.compiler.</span></span><span class="sig-name descname"><span class="pre">is_compiling</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/compiler.html#is_compiling"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/compiler/__init__.py#L377"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.compiler.is_compiling" title="Permalink to this definition">¶</a></dt>
 <dd><p>Indicates whether a graph is executed/traced as part of torch.compile() or torch.export().</p>
 <dl class="simple">
 <dt>Note that there are 2 other related flags that should deprecated eventually:</dt><dd><ul class="simple">
diff --git a/main/generated/torch.compiler.is_dynamo_compiling.html b/main/generated/torch.compiler.is_dynamo_compiling.html
index bdc4c9f6698..8b6b19f3a5c 100644
--- a/main/generated/torch.compiler.is_dynamo_compiling.html
+++ b/main/generated/torch.compiler.is_dynamo_compiling.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>torch.compiler.is_dynamo_compiling<a class="headerlink" href="#torch-compiler-is-dynamo-compiling" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.compiler.is_dynamo_compiling">
-<span class="sig-prename descclassname"><span class="pre">torch.compiler.</span></span><span class="sig-name descname"><span class="pre">is_dynamo_compiling</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/compiler.html#is_dynamo_compiling"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/compiler/__init__.py#L399"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.compiler.is_dynamo_compiling" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.compiler.</span></span><span class="sig-name descname"><span class="pre">is_dynamo_compiling</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/compiler.html#is_dynamo_compiling"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/compiler/__init__.py#L399"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.compiler.is_dynamo_compiling" title="Permalink to this definition">¶</a></dt>
 <dd><p>Indicates whether a graph is traced via TorchDynamo.</p>
 <p>It’s stricter than is_compiling() flag, as it would only be set to True when
 TorchDynamo is used.</p>
diff --git a/main/generated/torch.compiler.is_exporting.html b/main/generated/torch.compiler.is_exporting.html
index b762cd04af9..67fb24e2373 100644
--- a/main/generated/torch.compiler.is_exporting.html
+++ b/main/generated/torch.compiler.is_exporting.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>torch.compiler.is_exporting<a class="headerlink" href="#torch-compiler-is-exporting" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.compiler.is_exporting">
-<span class="sig-prename descclassname"><span class="pre">torch.compiler.</span></span><span class="sig-name descname"><span class="pre">is_exporting</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/compiler.html#is_exporting"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/compiler/__init__.py#L417"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.compiler.is_exporting" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.compiler.</span></span><span class="sig-name descname"><span class="pre">is_exporting</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/compiler.html#is_exporting"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/compiler/__init__.py#L417"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.compiler.is_exporting" title="Permalink to this definition">¶</a></dt>
 <dd><p>Indicated whether we’re under exporting.</p>
 <p>It’s stricter than is_compiling() flag, as it would only be set to True when
 torch.export is used.</p>
diff --git a/main/generated/torch.compiler.list_backends.html b/main/generated/torch.compiler.list_backends.html
index 4317f17e80c..6e80aa33697 100644
--- a/main/generated/torch.compiler.list_backends.html
+++ b/main/generated/torch.compiler.list_backends.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>torch.compiler.list_backends<a class="headerlink" href="#torch-compiler-list-backends" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.compiler.list_backends">
-<span class="sig-prename descclassname"><span class="pre">torch.compiler.</span></span><span class="sig-name descname"><span class="pre">list_backends</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">exclude_tags</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">('debug',</span> <span class="pre">'experimental')</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/compiler.html#list_backends"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/compiler/__init__.py#L197"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.compiler.list_backends" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.compiler.</span></span><span class="sig-name descname"><span class="pre">list_backends</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">exclude_tags</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">('debug',</span> <span class="pre">'experimental')</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/compiler.html#list_backends"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/compiler/__init__.py#L197"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.compiler.list_backends" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return valid strings that can be passed to <cite>torch.compile(…, backend=”name”)</cite>.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.compiler.reset.html b/main/generated/torch.compiler.reset.html
index b4454ebc17c..5ee2b75d8a6 100644
--- a/main/generated/torch.compiler.reset.html
+++ b/main/generated/torch.compiler.reset.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>torch.compiler.reset<a class="headerlink" href="#torch-compiler-reset" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.compiler.reset">
-<span class="sig-prename descclassname"><span class="pre">torch.compiler.</span></span><span class="sig-name descname"><span class="pre">reset</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/compiler.html#reset"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/compiler/__init__.py#L42"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.compiler.reset" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.compiler.</span></span><span class="sig-name descname"><span class="pre">reset</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/compiler.html#reset"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/compiler/__init__.py#L42"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.compiler.reset" title="Permalink to this definition">¶</a></dt>
 <dd><p>This function clears all compilation caches and restores the system to its initial state.
 It is recommended to call this function, especially after using operations like <cite>torch.compile(…)</cite>
 to ensure a clean state before another unrelated compilation</p>
diff --git a/main/generated/torch.compiler.set_stance.html b/main/generated/torch.compiler.set_stance.html
index f2a2861500b..42848792b6f 100644
--- a/main/generated/torch.compiler.set_stance.html
+++ b/main/generated/torch.compiler.set_stance.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>torch.compiler.set_stance<a class="headerlink" href="#torch-compiler-set-stance" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.compiler.set_stance">
-<span class="sig-prename descclassname"><span class="pre">torch.compiler.</span></span><span class="sig-name descname"><span class="pre">set_stance</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">stance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'default'</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">skip_guard_eval_unsafe</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">force_backend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/compiler.html#set_stance"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/compiler/__init__.py#L242"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.compiler.set_stance" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.compiler.</span></span><span class="sig-name descname"><span class="pre">set_stance</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">stance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'default'</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">skip_guard_eval_unsafe</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">force_backend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/compiler.html#set_stance"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/compiler/__init__.py#L242"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.compiler.set_stance" title="Permalink to this definition">¶</a></dt>
 <dd><p>Set the current stance of the compiler.
 Can be used as a function, context manager, or decorator.
 Do not use this function inside a <cite>torch.compile</cite> region - an error will be raised otherwise.</p>
diff --git a/main/generated/torch.compiler.substitute_in_graph.html b/main/generated/torch.compiler.substitute_in_graph.html
index 093d225a6c3..6d7b2b98fef 100644
--- a/main/generated/torch.compiler.substitute_in_graph.html
+++ b/main/generated/torch.compiler.substitute_in_graph.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>torch.compiler.substitute_in_graph<a class="headerlink" href="#torch-compiler-substitute-in-graph" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.compiler.substitute_in_graph">
-<span class="sig-prename descclassname"><span class="pre">torch.compiler.</span></span><span class="sig-name descname"><span class="pre">substitute_in_graph</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">original_fn</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">can_constant_fold_through</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">skip_signature_check</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/compiler.html#substitute_in_graph"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/compiler/__init__.py#L134"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.compiler.substitute_in_graph" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.compiler.</span></span><span class="sig-name descname"><span class="pre">substitute_in_graph</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">original_fn</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">can_constant_fold_through</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">skip_signature_check</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/compiler.html#substitute_in_graph"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/compiler/__init__.py#L134"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.compiler.substitute_in_graph" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register a polyfill handler for a function, usually a C function from the C extension, to be
 used in place of the original function when inlining the original function in the graph.</p>
 <div class="admonition note">
diff --git a/main/generated/torch.complex.html b/main/generated/torch.complex.html
index 63294197d15..e89101dcade 100644
--- a/main/generated/torch.complex.html
+++ b/main/generated/torch.complex.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.concat.html b/main/generated/torch.concat.html
index d7fc41c4255..10df7634c35 100644
--- a/main/generated/torch.concat.html
+++ b/main/generated/torch.concat.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.concatenate.html b/main/generated/torch.concatenate.html
index 5c3c1a83c08..0cc9f986e0f 100644
--- a/main/generated/torch.concatenate.html
+++ b/main/generated/torch.concatenate.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.cond.html b/main/generated/torch.cond.html
index 4a143b938eb..b50e7e6cb1d 100644
--- a/main/generated/torch.cond.html
+++ b/main/generated/torch.cond.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.cond<a class="headerlink" href="#torch-cond" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.cond">
-<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">cond</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">pred</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">true_fn</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">false_fn</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">operands</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">()</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/_higher_order_ops/cond.py#L69"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cond" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">cond</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">pred</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">true_fn</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">false_fn</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">operands</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">()</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/_higher_order_ops/cond.py#L69"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cond" title="Permalink to this definition">¶</a></dt>
 <dd><p>Conditionally applies <cite>true_fn</cite> or <cite>false_fn</cite>.</p>
 <div class="admonition warning">
 <p class="admonition-title">Warning</p>
diff --git a/main/generated/torch.conj.html b/main/generated/torch.conj.html
index bf4be62a18c..d87c86cf434 100644
--- a/main/generated/torch.conj.html
+++ b/main/generated/torch.conj.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.conj_physical.html b/main/generated/torch.conj_physical.html
index e246852588b..b19b196a45e 100644
--- a/main/generated/torch.conj_physical.html
+++ b/main/generated/torch.conj_physical.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
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     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.copysign.html b/main/generated/torch.copysign.html
index 915acb6cff6..c100380313c 100644
--- a/main/generated/torch.copysign.html
+++ b/main/generated/torch.copysign.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.corrcoef.html b/main/generated/torch.corrcoef.html
index 4a4bb99ecff..6007cb0be68 100644
--- a/main/generated/torch.corrcoef.html
+++ b/main/generated/torch.corrcoef.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.cos.html b/main/generated/torch.cos.html
index c10a16f087a..9cab3ded0c7 100644
--- a/main/generated/torch.cos.html
+++ b/main/generated/torch.cos.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.cosh.html b/main/generated/torch.cosh.html
index f527e584060..527904b995c 100644
--- a/main/generated/torch.cosh.html
+++ b/main/generated/torch.cosh.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
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     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.count_nonzero.html b/main/generated/torch.count_nonzero.html
index 0045f3f20c2..d32221213a2 100644
--- a/main/generated/torch.count_nonzero.html
+++ b/main/generated/torch.count_nonzero.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.cov.html b/main/generated/torch.cov.html
index 086cc38a832..ca72ee5487f 100644
--- a/main/generated/torch.cov.html
+++ b/main/generated/torch.cov.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.cpu.Stream.html b/main/generated/torch.cpu.Stream.html
index b0cb35144b0..74d6c2443d0 100644
--- a/main/generated/torch.cpu.Stream.html
+++ b/main/generated/torch.cpu.Stream.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>Stream<a class="headerlink" href="#stream" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.cpu.Stream">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.cpu.</span></span><span class="sig-name descname"><span class="pre">Stream</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">priority</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-1</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cpu.html#Stream"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cpu/__init__.py#L92"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cpu.Stream" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.cpu.</span></span><span class="sig-name descname"><span class="pre">Stream</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">priority</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-1</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cpu.html#Stream"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/cpu/__init__.py#L92"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cpu.Stream" title="Permalink to this definition">¶</a></dt>
 <dd><p>N.B. This class only exists to facilitate device-agnostic code</p>
 <dl class="field-list simple">
 </dl>
diff --git a/main/generated/torch.cpu.StreamContext.html b/main/generated/torch.cpu.StreamContext.html
index e621c80fcdc..042f0d29309 100644
--- a/main/generated/torch.cpu.StreamContext.html
+++ b/main/generated/torch.cpu.StreamContext.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>StreamContext<a class="headerlink" href="#streamcontext" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.cpu.StreamContext">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.cpu.</span></span><span class="sig-name descname"><span class="pre">StreamContext</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">stream</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cpu.html#StreamContext"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cpu/__init__.py#L134"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cpu.StreamContext" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.cpu.</span></span><span class="sig-name descname"><span class="pre">StreamContext</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">stream</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cpu.html#StreamContext"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/cpu/__init__.py#L134"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cpu.StreamContext" title="Permalink to this definition">¶</a></dt>
 <dd><p>Context-manager that selects a given stream.</p>
 <p>N.B. This class only exists to facilitate device-agnostic code</p>
 </dd></dl>
diff --git a/main/generated/torch.cpu.current_device.html b/main/generated/torch.cpu.current_device.html
index 7e64be5f97e..3a0c64316df 100644
--- a/main/generated/torch.cpu.current_device.html
+++ b/main/generated/torch.cpu.current_device.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.cpu.current_device<a class="headerlink" href="#torch-cpu-current-device" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.cpu.current_device">
-<span class="sig-prename descclassname"><span class="pre">torch.cpu.</span></span><span class="sig-name descname"><span class="pre">current_device</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cpu.html#current_device"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cpu/__init__.py#L189"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cpu.current_device" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.cpu.</span></span><span class="sig-name descname"><span class="pre">current_device</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cpu.html#current_device"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/cpu/__init__.py#L189"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cpu.current_device" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns current device for cpu. Always ‘cpu’.</p>
 <p>N.B. This function only exists to facilitate device-agnostic code</p>
 <dl class="field-list simple">
diff --git a/main/generated/torch.cpu.current_stream.html b/main/generated/torch.cpu.current_stream.html
index 6f280e809af..0e77c1a7d01 100644
--- a/main/generated/torch.cpu.current_stream.html
+++ b/main/generated/torch.cpu.current_stream.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.cpu.current_stream<a class="headerlink" href="#torch-cpu-current-stream" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.cpu.current_stream">
-<span class="sig-prename descclassname"><span class="pre">torch.cpu.</span></span><span class="sig-name descname"><span class="pre">current_stream</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cpu.html#current_stream"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cpu/__init__.py#L122"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cpu.current_stream" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.cpu.</span></span><span class="sig-name descname"><span class="pre">current_stream</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cpu.html#current_stream"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/cpu/__init__.py#L122"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cpu.current_stream" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns the currently selected <a class="reference internal" href="torch.cpu.Stream.html#torch.cpu.Stream" title="torch.cpu.Stream"><code class="xref py py-class docutils literal notranslate"><span class="pre">Stream</span></code></a> for a given device.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.cpu.device_count.html b/main/generated/torch.cpu.device_count.html
index fe587417c87..7feb783b586 100644
--- a/main/generated/torch.cpu.device_count.html
+++ b/main/generated/torch.cpu.device_count.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.cpu.device_count<a class="headerlink" href="#torch-cpu-device-count" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.cpu.device_count">
-<span class="sig-prename descclassname"><span class="pre">torch.cpu.</span></span><span class="sig-name descname"><span class="pre">device_count</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cpu.html#device_count"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cpu/__init__.py#L174"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cpu.device_count" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.cpu.</span></span><span class="sig-name descname"><span class="pre">device_count</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cpu.html#device_count"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/cpu/__init__.py#L174"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cpu.device_count" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns number of CPU devices (not cores). Always 1.</p>
 <p>N.B. This function only exists to facilitate device-agnostic code</p>
 <dl class="field-list simple">
diff --git a/main/generated/torch.cpu.is_available.html b/main/generated/torch.cpu.is_available.html
index f98155366b0..a66bfcc1254 100644
--- a/main/generated/torch.cpu.is_available.html
+++ b/main/generated/torch.cpu.is_available.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.cpu.is_available<a class="headerlink" href="#torch-cpu-is-available" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.cpu.is_available">
-<span class="sig-prename descclassname"><span class="pre">torch.cpu.</span></span><span class="sig-name descname"><span class="pre">is_available</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cpu.html#is_available"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cpu/__init__.py#L73"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cpu.is_available" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.cpu.</span></span><span class="sig-name descname"><span class="pre">is_available</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cpu.html#is_available"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/cpu/__init__.py#L73"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cpu.is_available" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns a bool indicating if CPU is currently available.</p>
 <p>N.B. This function only exists to facilitate device-agnostic code</p>
 <dl class="field-list simple">
diff --git a/main/generated/torch.cpu.set_device.html b/main/generated/torch.cpu.set_device.html
index 7768082e0bf..4733bd2f572 100644
--- a/main/generated/torch.cpu.set_device.html
+++ b/main/generated/torch.cpu.set_device.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.cpu.set_device<a class="headerlink" href="#torch-cpu-set-device" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.cpu.set_device">
-<span class="sig-prename descclassname"><span class="pre">torch.cpu.</span></span><span class="sig-name descname"><span class="pre">set_device</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cpu.html#set_device"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cpu/__init__.py#L182"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cpu.set_device" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.cpu.</span></span><span class="sig-name descname"><span class="pre">set_device</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cpu.html#set_device"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/cpu/__init__.py#L182"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cpu.set_device" title="Permalink to this definition">¶</a></dt>
 <dd><p>Sets the current device, in CPU we do nothing.</p>
 <p>N.B. This function only exists to facilitate device-agnostic code</p>
 <dl class="field-list simple">
diff --git a/main/generated/torch.cpu.stream.html b/main/generated/torch.cpu.stream.html
index b8c9b0b60e2..e236a693b6d 100644
--- a/main/generated/torch.cpu.stream.html
+++ b/main/generated/torch.cpu.stream.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.cpu.stream<a class="headerlink" href="#torch-cpu-stream" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.cpu.stream">
-<span class="sig-prename descclassname"><span class="pre">torch.cpu.</span></span><span class="sig-name descname"><span class="pre">stream</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">stream</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cpu.html#stream"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cpu/__init__.py#L165"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cpu.stream" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.cpu.</span></span><span class="sig-name descname"><span class="pre">stream</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">stream</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cpu.html#stream"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/cpu/__init__.py#L165"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cpu.stream" title="Permalink to this definition">¶</a></dt>
 <dd><p>Wrapper around the Context-manager StreamContext that
 selects a given stream.</p>
 <p>N.B. This function only exists to facilitate device-agnostic code</p>
diff --git a/main/generated/torch.cpu.synchronize.html b/main/generated/torch.cpu.synchronize.html
index 3fdee87c9d0..c393b356d83 100644
--- a/main/generated/torch.cpu.synchronize.html
+++ b/main/generated/torch.cpu.synchronize.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.cpu.synchronize<a class="headerlink" href="#torch-cpu-synchronize" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.cpu.synchronize">
-<span class="sig-prename descclassname"><span class="pre">torch.cpu.</span></span><span class="sig-name descname"><span class="pre">synchronize</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cpu.html#synchronize"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cpu/__init__.py#L82"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cpu.synchronize" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.cpu.</span></span><span class="sig-name descname"><span class="pre">synchronize</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cpu.html#synchronize"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/cpu/__init__.py#L82"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cpu.synchronize" title="Permalink to this definition">¶</a></dt>
 <dd><p>Waits for all kernels in all streams on the CPU device to complete.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.cross.html b/main/generated/torch.cross.html
index e7d9521cd50..d7c59b22bc2 100644
--- a/main/generated/torch.cross.html
+++ b/main/generated/torch.cross.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.cuda.CUDAGraph.html b/main/generated/torch.cuda.CUDAGraph.html
index 74310bea819..a308ee76401 100644
--- a/main/generated/torch.cuda.CUDAGraph.html
+++ b/main/generated/torch.cuda.CUDAGraph.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>CUDAGraph<a class="headerlink" href="#cudagraph" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.cuda.CUDAGraph">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">CUDAGraph</span></span><a class="reference internal" href="../_modules/torch/cuda/graphs.html#CUDAGraph"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/graphs.py#L46"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.CUDAGraph" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">CUDAGraph</span></span><a class="reference internal" href="../_modules/torch/cuda/graphs.html#CUDAGraph"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/cuda/graphs.py#L46"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.CUDAGraph" title="Permalink to this definition">¶</a></dt>
 <dd><p>Wrapper around a CUDA graph.</p>
 <div class="admonition warning">
 <p class="admonition-title">Warning</p>
@@ -620,7 +620,7 @@ <h1>CUDAGraph<a class="headerlink" href="#cudagraph" title="Permalink to this he
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.cuda.CUDAGraph.capture_begin">
-<span class="sig-name descname"><span class="pre">capture_begin</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">pool</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">capture_error_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'global'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/graphs.html#CUDAGraph.capture_begin"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/graphs.py#L56"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.CUDAGraph.capture_begin" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">capture_begin</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">pool</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">capture_error_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'global'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/graphs.html#CUDAGraph.capture_begin"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/cuda/graphs.py#L56"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.CUDAGraph.capture_begin" title="Permalink to this definition">¶</a></dt>
 <dd><p>Begin capturing CUDA work on the current stream.</p>
 <p>Typically, you shouldn’t call <code class="docutils literal notranslate"><span class="pre">capture_begin</span></code> yourself.
 Use <a class="reference internal" href="torch.cuda.graph.html#torch.cuda.graph" title="torch.cuda.graph"><code class="xref py py-class docutils literal notranslate"><span class="pre">graph</span></code></a> or <a class="reference internal" href="torch.cuda.make_graphed_callables.html#torch.cuda.make_graphed_callables" title="torch.cuda.make_graphed_callables"><code class="xref py py-func docutils literal notranslate"><span class="pre">make_graphed_callables()</span></code></a>,
@@ -643,7 +643,7 @@ <h1>CUDAGraph<a class="headerlink" href="#cudagraph" title="Permalink to this he
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.cuda.CUDAGraph.capture_end">
-<span class="sig-name descname"><span class="pre">capture_end</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/graphs.html#CUDAGraph.capture_end"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/graphs.py#L75"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.CUDAGraph.capture_end" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">capture_end</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/graphs.html#CUDAGraph.capture_end"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/cuda/graphs.py#L75"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.CUDAGraph.capture_end" title="Permalink to this definition">¶</a></dt>
 <dd><p>End CUDA graph capture on the current stream.</p>
 <p>After <code class="docutils literal notranslate"><span class="pre">capture_end</span></code>, <code class="docutils literal notranslate"><span class="pre">replay</span></code> may be called on this instance.</p>
 <p>Typically, you shouldn’t call <code class="docutils literal notranslate"><span class="pre">capture_end</span></code> yourself.
@@ -653,7 +653,7 @@ <h1>CUDAGraph<a class="headerlink" href="#cudagraph" title="Permalink to this he
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.cuda.CUDAGraph.debug_dump">
-<span class="sig-name descname"><span class="pre">debug_dump</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">debug_path</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/graphs.html#CUDAGraph.debug_dump"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/graphs.py#L106"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.CUDAGraph.debug_dump" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">debug_dump</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">debug_path</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/graphs.html#CUDAGraph.debug_dump"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/cuda/graphs.py#L106"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.CUDAGraph.debug_dump" title="Permalink to this definition">¶</a></dt>
 <dd><dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
 <dd class="field-odd"><p><strong>debug_path</strong> (<em>required</em>) – Path to dump the graph to.</p>
@@ -665,13 +665,13 @@ <h1>CUDAGraph<a class="headerlink" href="#cudagraph" title="Permalink to this he
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.cuda.CUDAGraph.enable_debug_mode">
-<span class="sig-name descname"><span class="pre">enable_debug_mode</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/graphs.html#CUDAGraph.enable_debug_mode"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/graphs.py#L102"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.CUDAGraph.enable_debug_mode" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">enable_debug_mode</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/graphs.html#CUDAGraph.enable_debug_mode"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/cuda/graphs.py#L102"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.CUDAGraph.enable_debug_mode" title="Permalink to this definition">¶</a></dt>
 <dd><p>Enable debugging mode for CUDAGraph.debug_dump.</p>
 </dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.cuda.CUDAGraph.pool">
-<span class="sig-name descname"><span class="pre">pool</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/graphs.html#CUDAGraph.pool"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/graphs.py#L94"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.CUDAGraph.pool" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">pool</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/graphs.html#CUDAGraph.pool"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/cuda/graphs.py#L94"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.CUDAGraph.pool" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return an opaque token representing the id of this graph’s memory pool.</p>
 <p>This id can optionally be passed to another graph’s <code class="docutils literal notranslate"><span class="pre">capture_begin</span></code>,
 which hints the other graph may share the same memory pool.</p>
@@ -679,13 +679,13 @@ <h1>CUDAGraph<a class="headerlink" href="#cudagraph" title="Permalink to this he
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.cuda.CUDAGraph.replay">
-<span class="sig-name descname"><span class="pre">replay</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/graphs.html#CUDAGraph.replay"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/graphs.py#L86"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.CUDAGraph.replay" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">replay</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/graphs.html#CUDAGraph.replay"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/cuda/graphs.py#L86"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.CUDAGraph.replay" title="Permalink to this definition">¶</a></dt>
 <dd><p>Replay the CUDA work captured by this graph.</p>
 </dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.cuda.CUDAGraph.reset">
-<span class="sig-name descname"><span class="pre">reset</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/graphs.html#CUDAGraph.reset"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/graphs.py#L90"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.CUDAGraph.reset" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">reset</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/graphs.html#CUDAGraph.reset"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/cuda/graphs.py#L90"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.CUDAGraph.reset" title="Permalink to this definition">¶</a></dt>
 <dd><p>Delete the graph currently held by this instance.</p>
 </dd></dl>
 
diff --git a/main/generated/torch.cuda.CUDAPluggableAllocator.html b/main/generated/torch.cuda.CUDAPluggableAllocator.html
index 9f76632bf6f..7461672bd45 100644
--- a/main/generated/torch.cuda.CUDAPluggableAllocator.html
+++ b/main/generated/torch.cuda.CUDAPluggableAllocator.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>CUDAPluggableAllocator<a class="headerlink" href="#cudapluggableallocator" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.cuda.CUDAPluggableAllocator">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">CUDAPluggableAllocator</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">path_to_so_file</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alloc_fn_name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">free_fn_name</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/memory.html#CUDAPluggableAllocator"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/memory.py#L947"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.CUDAPluggableAllocator" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">CUDAPluggableAllocator</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">path_to_so_file</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alloc_fn_name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">free_fn_name</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/memory.html#CUDAPluggableAllocator"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/cuda/memory.py#L947"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.CUDAPluggableAllocator" title="Permalink to this definition">¶</a></dt>
 <dd><p>CUDA memory allocator loaded from a so file.</p>
 <dl class="field-list simple">
 </dl>
diff --git a/main/generated/torch.cuda.Event.html b/main/generated/torch.cuda.Event.html
index 0807a51ef46..df7a03f668d 100644
--- a/main/generated/torch.cuda.Event.html
+++ b/main/generated/torch.cuda.Event.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>Event<a class="headerlink" href="#event" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.cuda.Event">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">Event</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">enable_timing</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">blocking</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">interprocess</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/streams.html#Event"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/streams.py#L143"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.Event" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">Event</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">enable_timing</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">blocking</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">interprocess</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/streams.html#Event"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/cuda/streams.py#L143"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.Event" title="Permalink to this definition">¶</a></dt>
 <dd><p>Wrapper around a CUDA event.</p>
 <p>CUDA events are synchronization markers that can be used to monitor the
 device’s progress, to accurately measure timing, and to synchronize CUDA
@@ -634,7 +634,7 @@ <h1>Event<a class="headerlink" href="#event" title="Permalink to this heading">
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.cuda.Event.elapsed_time">
-<span class="sig-name descname"><span class="pre">elapsed_time</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">end_event</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/streams.html#Event.elapsed_time"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/streams.py#L210"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.Event.elapsed_time" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">elapsed_time</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">end_event</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/streams.html#Event.elapsed_time"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/cuda/streams.py#L210"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.Event.elapsed_time" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the time elapsed.</p>
 <p>Time reported in milliseconds after the event was recorded and
 before the end_event was recorded.</p>
@@ -642,20 +642,20 @@ <h1>Event<a class="headerlink" href="#event" title="Permalink to this heading">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.cuda.Event.from_ipc_handle">
-<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_ipc_handle</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">handle</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/streams.html#Event.from_ipc_handle"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/streams.py#L174"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.Event.from_ipc_handle" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_ipc_handle</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">handle</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/streams.html#Event.from_ipc_handle"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/cuda/streams.py#L174"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.Event.from_ipc_handle" title="Permalink to this definition">¶</a></dt>
 <dd><p>Reconstruct an event from an IPC handle on the given device.</p>
 </dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.cuda.Event.ipc_handle">
-<span class="sig-name descname"><span class="pre">ipc_handle</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/streams.html#Event.ipc_handle"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/streams.py#L229"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.Event.ipc_handle" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">ipc_handle</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/streams.html#Event.ipc_handle"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/cuda/streams.py#L229"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.Event.ipc_handle" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return an IPC handle of this event.</p>
 <p>If not recorded yet, the event will use the current device.</p>
 </dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.cuda.Event.query">
-<span class="sig-name descname"><span class="pre">query</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/streams.html#Event.query"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/streams.py#L201"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.Event.query" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">query</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/streams.html#Event.query"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/cuda/streams.py#L201"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.Event.query" title="Permalink to this definition">¶</a></dt>
 <dd><p>Check if all work currently captured by event has completed.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Returns</dt>
@@ -667,7 +667,7 @@ <h1>Event<a class="headerlink" href="#event" title="Permalink to this heading">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.cuda.Event.record">
-<span class="sig-name descname"><span class="pre">record</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">stream</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/streams.html#Event.record"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/streams.py#L179"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.Event.record" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">record</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">stream</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/streams.html#Event.record"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/cuda/streams.py#L179"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.Event.record" title="Permalink to this definition">¶</a></dt>
 <dd><p>Record the event in a given stream.</p>
 <p>Uses <code class="docutils literal notranslate"><span class="pre">torch.cuda.current_stream()</span></code> if no stream is specified. The
 stream’s device must match the event’s device.</p>
@@ -675,7 +675,7 @@ <h1>Event<a class="headerlink" href="#event" title="Permalink to this heading">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.cuda.Event.synchronize">
-<span class="sig-name descname"><span class="pre">synchronize</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/streams.html#Event.synchronize"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/streams.py#L218"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.Event.synchronize" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">synchronize</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/streams.html#Event.synchronize"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/cuda/streams.py#L218"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.Event.synchronize" title="Permalink to this definition">¶</a></dt>
 <dd><p>Wait for the event to complete.</p>
 <p>Waits until the completion of all work currently captured in this event.
 This prevents the CPU thread from proceeding until the event completes.</p>
@@ -692,7 +692,7 @@ <h1>Event<a class="headerlink" href="#event" title="Permalink to this heading">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.cuda.Event.wait">
-<span class="sig-name descname"><span class="pre">wait</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">stream</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/streams.html#Event.wait"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/streams.py#L189"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.Event.wait" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">wait</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">stream</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/streams.html#Event.wait"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/cuda/streams.py#L189"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.Event.wait" title="Permalink to this definition">¶</a></dt>
 <dd><p>Make all future work submitted to the given stream wait for this event.</p>
 <p>Use <code class="docutils literal notranslate"><span class="pre">torch.cuda.current_stream()</span></code> if no stream is specified.</p>
 <div class="admonition note">
diff --git a/main/generated/torch.cuda.ExternalStream.html b/main/generated/torch.cuda.ExternalStream.html
index 25ff224853f..976e289ad44 100644
--- a/main/generated/torch.cuda.ExternalStream.html
+++ b/main/generated/torch.cuda.ExternalStream.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>ExternalStream<a class="headerlink" href="#externalstream" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.cuda.ExternalStream">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">ExternalStream</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">stream_ptr</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/streams.html#ExternalStream"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/streams.py#L120"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.ExternalStream" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">ExternalStream</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">stream_ptr</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/streams.html#ExternalStream"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/cuda/streams.py#L120"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.ExternalStream" title="Permalink to this definition">¶</a></dt>
 <dd><p>Wrapper around an externally allocated CUDA stream.</p>
 <p>This class is used to wrap streams allocated in other libraries in order
 to facilitate data exchange and multi-library interactions.</p>
@@ -635,7 +635,7 @@ <h1>ExternalStream<a class="headerlink" href="#externalstream" title="Permalink
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.cuda.ExternalStream.query">
-<span class="sig-name descname"><span class="pre">query</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/streams.py#L88"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.ExternalStream.query" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">query</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/cuda/streams.py#L88"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.ExternalStream.query" title="Permalink to this definition">¶</a></dt>
 <dd><p>Check if all the work submitted has been completed.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Returns</dt>
@@ -649,7 +649,7 @@ <h1>ExternalStream<a class="headerlink" href="#externalstream" title="Permalink
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.cuda.ExternalStream.record_event">
-<span class="sig-name descname"><span class="pre">record_event</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">event</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/streams.py#L73"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.ExternalStream.record_event" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">record_event</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">event</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/cuda/streams.py#L73"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.ExternalStream.record_event" title="Permalink to this definition">¶</a></dt>
 <dd><p>Record an event.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -664,7 +664,7 @@ <h1>ExternalStream<a class="headerlink" href="#externalstream" title="Permalink
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.cuda.ExternalStream.synchronize">
-<span class="sig-name descname"><span class="pre">synchronize</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/streams.py#L96"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.ExternalStream.synchronize" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">synchronize</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/cuda/streams.py#L96"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.ExternalStream.synchronize" title="Permalink to this definition">¶</a></dt>
 <dd><p>Wait for all the kernels in this stream to complete.</p>
 <div class="admonition note">
 <p class="admonition-title">Note</p>
@@ -677,7 +677,7 @@ <h1>ExternalStream<a class="headerlink" href="#externalstream" title="Permalink
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.cuda.ExternalStream.wait_event">
-<span class="sig-name descname"><span class="pre">wait_event</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">event</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/streams.py#L42"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.ExternalStream.wait_event" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">wait_event</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">event</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/cuda/streams.py#L42"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.ExternalStream.wait_event" title="Permalink to this definition">¶</a></dt>
 <dd><p>Make all future work submitted to the stream wait for an event.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -695,7 +695,7 @@ <h1>ExternalStream<a class="headerlink" href="#externalstream" title="Permalink
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.cuda.ExternalStream.wait_stream">
-<span class="sig-name descname"><span class="pre">wait_stream</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">stream</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/streams.py#L59"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.ExternalStream.wait_stream" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">wait_stream</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">stream</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/cuda/streams.py#L59"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.ExternalStream.wait_stream" title="Permalink to this definition">¶</a></dt>
 <dd><p>Synchronize with another stream.</p>
 <p>All future work submitted to this stream will wait until all kernels
 submitted to a given stream at the time of call complete.</p>
diff --git a/main/generated/torch.cuda.MemPool.html b/main/generated/torch.cuda.MemPool.html
index 2cfc318d01a..eaf3399f2cf 100644
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+++ b/main/generated/torch.cuda.MemPool.html
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@@ -612,7 +612,7 @@
 <h1>MemPool<a class="headerlink" href="#mempool" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.cuda.MemPool">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">MemPool</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/memory.html#MemPool"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/memory.py#L1021"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.MemPool" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">MemPool</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/memory.html#MemPool"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/cuda/memory.py#L1021"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.MemPool" title="Permalink to this definition">¶</a></dt>
 <dd><p>MemPool represents a pool of memory in a caching allocator. Currently,
 it’s just the ID of the pool object maintained in the CUDACachingAllocator.</p>
 <dl class="field-list simple">
@@ -638,7 +638,7 @@ <h1>MemPool<a class="headerlink" href="#mempool" title="Permalink to this headin
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.cuda.MemPool.snapshot">
-<span class="sig-name descname"><span class="pre">snapshot</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/memory.html#MemPool.snapshot"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/memory.py#L1051"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.MemPool.snapshot" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">snapshot</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/memory.html#MemPool.snapshot"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/cuda/memory.py#L1051"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.MemPool.snapshot" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return a snapshot of the CUDA memory allocator pool state across all
 devices.</p>
 <p>Interpreting the output of this function requires familiarity with the
@@ -652,7 +652,7 @@ <h1>MemPool<a class="headerlink" href="#mempool" title="Permalink to this headin
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.cuda.MemPool.use_count">
-<span class="sig-name descname"><span class="pre">use_count</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/memory.html#MemPool.use_count"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/memory.py#L1047"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.MemPool.use_count" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">use_count</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/memory.html#MemPool.use_count"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/cuda/memory.py#L1047"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.MemPool.use_count" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns the reference count of this pool.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
diff --git a/main/generated/torch.cuda.MemPoolContext.html b/main/generated/torch.cuda.MemPoolContext.html
index 7a6f945b2ef..3bd508a1ff7 100644
--- a/main/generated/torch.cuda.MemPoolContext.html
+++ b/main/generated/torch.cuda.MemPoolContext.html
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+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
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@@ -612,7 +612,7 @@
 <h1>MemPoolContext<a class="headerlink" href="#mempoolcontext" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.cuda.MemPoolContext">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">MemPoolContext</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/memory.html#MemPoolContext"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/memory.py#L1002"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.MemPoolContext" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">MemPoolContext</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/memory.html#MemPoolContext"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/cuda/memory.py#L1002"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.MemPoolContext" title="Permalink to this definition">¶</a></dt>
 <dd><p>MemPoolContext holds the currently active pool and stashes the previous
 pool. On deletion it makes the previous pool active.</p>
 <dl class="field-list simple">
@@ -625,7 +625,7 @@ <h1>MemPoolContext<a class="headerlink" href="#mempoolcontext" title="Permalink
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.cuda.MemPoolContext.active_pool">
-<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">active_pool</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/memory.html#MemPoolContext.active_pool"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/memory.py#L1015"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.MemPoolContext.active_pool" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">active_pool</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/memory.html#MemPoolContext.active_pool"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/cuda/memory.py#L1015"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.MemPoolContext.active_pool" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns the active MemPool</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
diff --git a/main/generated/torch.cuda.OutOfMemoryError.html b/main/generated/torch.cuda.OutOfMemoryError.html
index 4d28a34b045..b1ab1953101 100644
--- a/main/generated/torch.cuda.OutOfMemoryError.html
+++ b/main/generated/torch.cuda.OutOfMemoryError.html
@@ -300,7 +300,7 @@
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     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
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diff --git a/main/generated/torch.cuda.Stream.html b/main/generated/torch.cuda.Stream.html
index 0680befcd6a..aa93ffa73af 100644
--- a/main/generated/torch.cuda.Stream.html
+++ b/main/generated/torch.cuda.Stream.html
@@ -300,7 +300,7 @@
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     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>Stream<a class="headerlink" href="#stream" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.cuda.Stream">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">Stream</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">priority</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/streams.html#Stream"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/streams.py#L14"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.Stream" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">Stream</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">priority</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/streams.html#Stream"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/cuda/streams.py#L14"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.Stream" title="Permalink to this definition">¶</a></dt>
 <dd><p>Wrapper around a CUDA stream.</p>
 <p>A CUDA stream is a linear sequence of execution that belongs to a specific
 device, independent from other streams. It supports with statement as a
@@ -634,7 +634,7 @@ <h1>Stream<a class="headerlink" href="#stream" title="Permalink to this heading"
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.cuda.Stream.query">
-<span class="sig-name descname"><span class="pre">query</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/streams.html#Stream.query"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/streams.py#L88"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.Stream.query" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">query</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/streams.html#Stream.query"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/cuda/streams.py#L88"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.Stream.query" title="Permalink to this definition">¶</a></dt>
 <dd><p>Check if all the work submitted has been completed.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Returns</dt>
@@ -648,7 +648,7 @@ <h1>Stream<a class="headerlink" href="#stream" title="Permalink to this heading"
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.cuda.Stream.record_event">
-<span class="sig-name descname"><span class="pre">record_event</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">event</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/streams.html#Stream.record_event"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/streams.py#L73"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.Stream.record_event" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">record_event</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">event</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/streams.html#Stream.record_event"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/cuda/streams.py#L73"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.Stream.record_event" title="Permalink to this definition">¶</a></dt>
 <dd><p>Record an event.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -663,7 +663,7 @@ <h1>Stream<a class="headerlink" href="#stream" title="Permalink to this heading"
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.cuda.Stream.synchronize">
-<span class="sig-name descname"><span class="pre">synchronize</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/streams.html#Stream.synchronize"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/streams.py#L96"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.Stream.synchronize" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">synchronize</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/streams.html#Stream.synchronize"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/cuda/streams.py#L96"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.Stream.synchronize" title="Permalink to this definition">¶</a></dt>
 <dd><p>Wait for all the kernels in this stream to complete.</p>
 <div class="admonition note">
 <p class="admonition-title">Note</p>
@@ -676,7 +676,7 @@ <h1>Stream<a class="headerlink" href="#stream" title="Permalink to this heading"
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.cuda.Stream.wait_event">
-<span class="sig-name descname"><span class="pre">wait_event</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">event</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/streams.html#Stream.wait_event"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/streams.py#L42"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.Stream.wait_event" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">wait_event</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">event</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/streams.html#Stream.wait_event"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/cuda/streams.py#L42"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.Stream.wait_event" title="Permalink to this definition">¶</a></dt>
 <dd><p>Make all future work submitted to the stream wait for an event.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -694,7 +694,7 @@ <h1>Stream<a class="headerlink" href="#stream" title="Permalink to this heading"
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.cuda.Stream.wait_stream">
-<span class="sig-name descname"><span class="pre">wait_stream</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">stream</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/streams.html#Stream.wait_stream"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/streams.py#L59"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.Stream.wait_stream" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">wait_stream</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">stream</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/streams.html#Stream.wait_stream"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/cuda/streams.py#L59"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.Stream.wait_stream" title="Permalink to this definition">¶</a></dt>
 <dd><p>Synchronize with another stream.</p>
 <p>All future work submitted to this stream will wait until all kernels
 submitted to a given stream at the time of call complete.</p>
diff --git a/main/generated/torch.cuda.StreamContext.html b/main/generated/torch.cuda.StreamContext.html
index 95a9a3b7a7c..8bc6bbdff8f 100644
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@@ -612,7 +612,7 @@
 <h1>StreamContext<a class="headerlink" href="#streamcontext" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.cuda.StreamContext">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">StreamContext</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">stream</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda.html#StreamContext"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/__init__.py#L555"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.StreamContext" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">StreamContext</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">stream</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda.html#StreamContext"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/cuda/__init__.py#L555"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.StreamContext" title="Permalink to this definition">¶</a></dt>
 <dd><p>Context-manager that selects a given stream.</p>
 <p>All CUDA kernels queued within its context will be enqueued on a selected
 stream.</p>
diff --git a/main/generated/torch.cuda.caching_allocator_alloc.html b/main/generated/torch.cuda.caching_allocator_alloc.html
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@@ -612,7 +612,7 @@
 <h1>torch.cuda.caching_allocator_alloc<a class="headerlink" href="#torch-cuda-caching-allocator-alloc" title="Permalink to this heading">¶</a></h1>
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 <dt class="sig sig-object py" id="torch.cuda.caching_allocator_alloc">
-<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">caching_allocator_alloc</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stream</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/memory.html#caching_allocator_alloc"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/memory.py#L102"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.caching_allocator_alloc" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">caching_allocator_alloc</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stream</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/memory.html#caching_allocator_alloc"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/cuda/memory.py#L102"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.caching_allocator_alloc" title="Permalink to this definition">¶</a></dt>
 <dd><p>Perform a memory allocation using the CUDA memory allocator.</p>
 <p>Memory is allocated for a given device and a stream, this
 function is intended to be used for interoperability with other
diff --git a/main/generated/torch.cuda.caching_allocator_delete.html b/main/generated/torch.cuda.caching_allocator_delete.html
index af798106614..4e7f316895f 100644
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@@ -612,7 +612,7 @@
 <h1>torch.cuda.caching_allocator_delete<a class="headerlink" href="#torch-cuda-caching-allocator-delete" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.cuda.caching_allocator_delete">
-<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">caching_allocator_delete</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mem_ptr</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/memory.html#caching_allocator_delete"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/memory.py#L138"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.caching_allocator_delete" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">caching_allocator_delete</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mem_ptr</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/memory.html#caching_allocator_delete"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/cuda/memory.py#L138"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.caching_allocator_delete" title="Permalink to this definition">¶</a></dt>
 <dd><p>Delete memory allocated using the CUDA memory allocator.</p>
 <p>Memory allocated with <a class="reference internal" href="torch.cuda.caching_allocator_alloc.html#torch.cuda.caching_allocator_alloc" title="torch.cuda.caching_allocator_alloc"><code class="xref py py-func docutils literal notranslate"><span class="pre">caching_allocator_alloc()</span></code></a>.
 is freed here. The associated device and stream are tracked inside
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index 4c88b36488c..3428a286504 100644
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+++ b/main/generated/torch.cuda.can_device_access_peer.html
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     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
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@@ -612,7 +612,7 @@
 <h1>torch.cuda.can_device_access_peer<a class="headerlink" href="#torch-cuda-can-device-access-peer" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.cuda.can_device_access_peer">
-<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">can_device_access_peer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">peer_device</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda.html#can_device_access_peer"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/__init__.py#L543"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.can_device_access_peer" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">can_device_access_peer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">peer_device</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda.html#can_device_access_peer"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/cuda/__init__.py#L543"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.can_device_access_peer" title="Permalink to this definition">¶</a></dt>
 <dd><p>Check if peer access between two devices is possible.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
diff --git a/main/generated/torch.cuda.change_current_allocator.html b/main/generated/torch.cuda.change_current_allocator.html
index 56a1abd9e45..88a44ff06ec 100644
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+++ b/main/generated/torch.cuda.change_current_allocator.html
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-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
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     <div id="searchBox">
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@@ -612,7 +612,7 @@
 <h1>torch.cuda.change_current_allocator<a class="headerlink" href="#torch-cuda-change-current-allocator" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.cuda.change_current_allocator">
-<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">change_current_allocator</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">allocator</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/memory.html#change_current_allocator"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/memory.py#L979"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.change_current_allocator" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">change_current_allocator</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">allocator</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/memory.html#change_current_allocator"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/cuda/memory.py#L979"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.change_current_allocator" title="Permalink to this definition">¶</a></dt>
 <dd><p>Change the currently used memory allocator to be the one provided.</p>
 <p>If the current allocator has already been used/initialized, this function will error.</p>
 <dl class="field-list simple">
diff --git a/main/generated/torch.cuda.clock_rate.html b/main/generated/torch.cuda.clock_rate.html
index a6087be81ed..130786da809 100644
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+++ b/main/generated/torch.cuda.clock_rate.html
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@@ -612,7 +612,7 @@
 <h1>torch.cuda.clock_rate<a class="headerlink" href="#torch-cuda-clock-rate" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.cuda.clock_rate">
-<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">clock_rate</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda.html#clock_rate"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/__init__.py#L1321"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.clock_rate" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">clock_rate</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda.html#clock_rate"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/cuda/__init__.py#L1321"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.clock_rate" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the clock speed of the GPU SM in Hz Hertz over the past sample period as given by <cite>nvidia-smi</cite>.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.cuda.comm.broadcast.html b/main/generated/torch.cuda.comm.broadcast.html
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@@ -612,7 +612,7 @@
 <h1>torch.cuda.comm.broadcast<a class="headerlink" href="#torch-cuda-comm-broadcast" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.cuda.comm.broadcast">
-<span class="sig-prename descclassname"><span class="pre">torch.cuda.comm.</span></span><span class="sig-name descname"><span class="pre">broadcast</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">devices</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/parallel/comm.html#broadcast"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/parallel/comm.py#L16"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.comm.broadcast" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.cuda.comm.</span></span><span class="sig-name descname"><span class="pre">broadcast</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">devices</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/parallel/comm.html#broadcast"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/parallel/comm.py#L16"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.comm.broadcast" title="Permalink to this definition">¶</a></dt>
 <dd><p>Broadcasts a tensor to specified GPU devices.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.cuda.comm.broadcast_coalesced.html b/main/generated/torch.cuda.comm.broadcast_coalesced.html
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@@ -612,7 +612,7 @@
 <h1>torch.cuda.comm.broadcast_coalesced<a class="headerlink" href="#torch-cuda-comm-broadcast-coalesced" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.cuda.comm.broadcast_coalesced">
-<span class="sig-prename descclassname"><span class="pre">torch.cuda.comm.</span></span><span class="sig-name descname"><span class="pre">broadcast_coalesced</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tensors</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">devices</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">buffer_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10485760</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/parallel/comm.html#broadcast_coalesced"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/parallel/comm.py#L49"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.comm.broadcast_coalesced" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.cuda.comm.</span></span><span class="sig-name descname"><span class="pre">broadcast_coalesced</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tensors</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">devices</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">buffer_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10485760</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/parallel/comm.html#broadcast_coalesced"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/parallel/comm.py#L49"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.comm.broadcast_coalesced" title="Permalink to this definition">¶</a></dt>
 <dd><p>Broadcast a sequence of tensors to the specified GPUs.</p>
 <p>Small tensors are first coalesced into a buffer to reduce the number of synchronizations.</p>
 <dl class="field-list simple">
diff --git a/main/generated/torch.cuda.comm.gather.html b/main/generated/torch.cuda.comm.gather.html
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@@ -612,7 +612,7 @@
 <h1>torch.cuda.comm.gather<a class="headerlink" href="#torch-cuda-comm-gather" title="Permalink to this heading">¶</a></h1>
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 <dt class="sig sig-object py" id="torch.cuda.comm.gather">
-<span class="sig-prename descclassname"><span class="pre">torch.cuda.comm.</span></span><span class="sig-name descname"><span class="pre">gather</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tensors</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">destination</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/parallel/comm.html#gather"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/parallel/comm.py#L217"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.comm.gather" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.cuda.comm.</span></span><span class="sig-name descname"><span class="pre">gather</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tensors</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">destination</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/parallel/comm.html#gather"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/parallel/comm.py#L217"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.comm.gather" title="Permalink to this definition">¶</a></dt>
 <dd><p>Gathers tensors from multiple GPU devices.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.cuda.comm.reduce_add.html b/main/generated/torch.cuda.comm.reduce_add.html
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@@ -612,7 +612,7 @@
 <h1>torch.cuda.comm.reduce_add<a class="headerlink" href="#torch-cuda-comm-reduce-add" title="Permalink to this heading">¶</a></h1>
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-<span class="sig-prename descclassname"><span class="pre">torch.cuda.comm.</span></span><span class="sig-name descname"><span class="pre">reduce_add</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">inputs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">destination</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/parallel/comm.html#reduce_add"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/parallel/comm.py#L69"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.comm.reduce_add" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.cuda.comm.</span></span><span class="sig-name descname"><span class="pre">reduce_add</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">inputs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">destination</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/parallel/comm.html#reduce_add"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/parallel/comm.py#L69"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.comm.reduce_add" title="Permalink to this definition">¶</a></dt>
 <dd><p>Sum tensors from multiple GPUs.</p>
 <p>All inputs should have matching shapes, dtype, and layout. The output tensor
 will be of the same shape, dtype, and layout.</p>
diff --git a/main/generated/torch.cuda.comm.scatter.html b/main/generated/torch.cuda.comm.scatter.html
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+++ b/main/generated/torch.cuda.comm.scatter.html
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@@ -612,7 +612,7 @@
 <h1>torch.cuda.comm.scatter<a class="headerlink" href="#torch-cuda-comm-scatter" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
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-<span class="sig-prename descclassname"><span class="pre">torch.cuda.comm.</span></span><span class="sig-name descname"><span class="pre">scatter</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">devices</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">chunk_sizes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">streams</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/parallel/comm.html#scatter"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/parallel/comm.py#L167"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.comm.scatter" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.cuda.comm.</span></span><span class="sig-name descname"><span class="pre">scatter</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">devices</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">chunk_sizes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">streams</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/parallel/comm.html#scatter"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/parallel/comm.py#L167"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.comm.scatter" title="Permalink to this definition">¶</a></dt>
 <dd><p>Scatters tensor across multiple GPUs.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.cuda.cudart.html b/main/generated/torch.cuda.cudart.html
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@@ -612,7 +612,7 @@
 <h1>torch.cuda.cudart<a class="headerlink" href="#torch-cuda-cudart" title="Permalink to this heading">¶</a></h1>
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 <dt class="sig sig-object py" id="torch.cuda.cudart">
-<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">cudart</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda.html#cudart"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/__init__.py#L355"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.cudart" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">cudart</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda.html#cudart"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/cuda/__init__.py#L355"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.cudart" title="Permalink to this definition">¶</a></dt>
 <dd><p>Retrieves the CUDA runtime API module.</p>
 <p>This function initializes the CUDA runtime environment if it is not already
 initialized and returns the CUDA runtime API module (_cudart). The CUDA
diff --git a/main/generated/torch.cuda.current_blas_handle.html b/main/generated/torch.cuda.current_blas_handle.html
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@@ -612,7 +612,7 @@
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-<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">current_blas_handle</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda.html#current_blas_handle"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/__init__.py#L1079"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.current_blas_handle" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">current_blas_handle</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda.html#current_blas_handle"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/cuda/__init__.py#L1079"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.current_blas_handle" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return cublasHandle_t pointer to current cuBLAS handle</p>
 </dd></dl>
 
diff --git a/main/generated/torch.cuda.current_device.html b/main/generated/torch.cuda.current_device.html
index 79a435e92d4..b1254fc7b79 100644
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@@ -612,7 +612,7 @@
 <h1>torch.cuda.current_device<a class="headerlink" href="#torch-cuda-current-device" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.cuda.current_device">
-<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">current_device</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda.html#current_device"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/__init__.py#L983"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.current_device" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">current_device</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda.html#current_device"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/cuda/__init__.py#L983"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.current_device" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the index of a currently selected device.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
diff --git a/main/generated/torch.cuda.current_stream.html b/main/generated/torch.cuda.current_stream.html
index 7c6b4ed5d77..78c040ba28e 100644
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+++ b/main/generated/torch.cuda.current_stream.html
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@@ -612,7 +612,7 @@
 <h1>torch.cuda.current_stream<a class="headerlink" href="#torch-cuda-current-stream" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.cuda.current_stream">
-<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">current_stream</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda.html#current_stream"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/__init__.py#L1015"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.current_stream" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">current_stream</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda.html#current_stream"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/cuda/__init__.py#L1015"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.current_stream" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the currently selected <a class="reference internal" href="torch.cuda.Stream.html#torch.cuda.Stream" title="torch.cuda.Stream"><code class="xref py py-class docutils literal notranslate"><span class="pre">Stream</span></code></a> for a given device.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.cuda.default_stream.html b/main/generated/torch.cuda.default_stream.html
index 2d0a6715913..7b75d4d4bdb 100644
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+++ b/main/generated/torch.cuda.default_stream.html
@@ -300,7 +300,7 @@
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     <div class="version">
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@@ -612,7 +612,7 @@
 <h1>torch.cuda.default_stream<a class="headerlink" href="#torch-cuda-default-stream" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.cuda.default_stream">
-<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">default_stream</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda.html#default_stream"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/__init__.py#L1033"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.default_stream" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">default_stream</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda.html#default_stream"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/cuda/__init__.py#L1033"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.default_stream" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the default <a class="reference internal" href="torch.cuda.Stream.html#torch.cuda.Stream" title="torch.cuda.Stream"><code class="xref py py-class docutils literal notranslate"><span class="pre">Stream</span></code></a> for a given device.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.cuda.device.html b/main/generated/torch.cuda.device.html
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@@ -612,7 +612,7 @@
 <h1>device<a class="headerlink" href="#device" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.cuda.device">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">device</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda.html#device"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/__init__.py#L442"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.device" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">device</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda.html#device"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/cuda/__init__.py#L442"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.device" title="Permalink to this definition">¶</a></dt>
 <dd><p>Context-manager that changes the selected device.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.cuda.device_count.html b/main/generated/torch.cuda.device_count.html
index 5eb585ccad4..736136109c4 100644
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+++ b/main/generated/torch.cuda.device_count.html
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     <div class="version">
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@@ -612,7 +612,7 @@
 <h1>torch.cuda.device_count<a class="headerlink" href="#torch-cuda-device-count" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.cuda.device_count">
-<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">device_count</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda.html#device_count"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/__init__.py#L941"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.device_count" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">device_count</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda.html#device_count"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/cuda/__init__.py#L941"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.device_count" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the number of GPUs available.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
diff --git a/main/generated/torch.cuda.device_memory_used.html b/main/generated/torch.cuda.device_memory_used.html
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     <div class="version">
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@@ -612,7 +612,7 @@
 <h1>torch.cuda.device_memory_used<a class="headerlink" href="#torch-cuda-device-memory-used" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.cuda.device_memory_used">
-<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">device_memory_used</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda.html#device_memory_used"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/__init__.py#L1221"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.device_memory_used" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">device_memory_used</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda.html#device_memory_used"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/cuda/__init__.py#L1221"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.device_memory_used" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return used global (device) memory in bytes as given by <cite>nvidia-smi</cite> or <cite>amd-smi</cite>.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.cuda.device_of.html b/main/generated/torch.cuda.device_of.html
index ef9e70512cb..75488955ea4 100644
--- a/main/generated/torch.cuda.device_of.html
+++ b/main/generated/torch.cuda.device_of.html
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     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
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     <div id="searchBox">
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@@ -612,7 +612,7 @@
 <h1>device_of<a class="headerlink" href="#device-of" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.cuda.device_of">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">device_of</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">obj</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda.html#device_of"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/__init__.py#L462"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.device_of" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">device_of</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">obj</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda.html#device_of"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/cuda/__init__.py#L462"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.device_of" title="Permalink to this definition">¶</a></dt>
 <dd><p>Context-manager that changes the current device to that of given object.</p>
 <p>You can use both tensors and storages as arguments. If a given object is
 not allocated on a GPU, this is a no-op.</p>
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+++ b/main/generated/torch.cuda.empty_cache.html
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+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
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     <div id="searchBox">
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@@ -612,7 +612,7 @@
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 <dt class="sig sig-object py" id="torch.cuda.empty_cache">
-<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">empty_cache</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/memory.html#empty_cache"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/memory.py#L206"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.empty_cache" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">empty_cache</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/memory.html#empty_cache"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/cuda/memory.py#L206"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.empty_cache" title="Permalink to this definition">¶</a></dt>
 <dd><p>Release all unoccupied cached memory currently held by the caching
 allocator so that those can be used in other GPU application and visible in
 <cite>nvidia-smi</cite>.</p>
diff --git a/main/generated/torch.cuda.get_allocator_backend.html b/main/generated/torch.cuda.get_allocator_backend.html
index e44774adc64..9d9b01e63bb 100644
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+++ b/main/generated/torch.cuda.get_allocator_backend.html
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-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
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@@ -612,7 +612,7 @@
 <h1>torch.cuda.get_allocator_backend<a class="headerlink" href="#torch-cuda-get-allocator-backend" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.cuda.get_allocator_backend">
-<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">get_allocator_backend</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/memory.html#get_allocator_backend"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/memory.py#L925"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.get_allocator_backend" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">get_allocator_backend</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/memory.html#get_allocator_backend"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/cuda/memory.py#L925"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.get_allocator_backend" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return a string describing the active allocator backend as set by
 <code class="docutils literal notranslate"><span class="pre">PYTORCH_CUDA_ALLOC_CONF</span></code>. Currently available backends are
 <code class="docutils literal notranslate"><span class="pre">native</span></code> (PyTorch’s native caching allocator) and <cite>cudaMallocAsync`</cite>
diff --git a/main/generated/torch.cuda.get_arch_list.html b/main/generated/torch.cuda.get_arch_list.html
index 4dfcd50235c..504bf53e18a 100644
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+++ b/main/generated/torch.cuda.get_arch_list.html
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     <div class="version">
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+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
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@@ -612,7 +612,7 @@
 <h1>torch.cuda.get_arch_list<a class="headerlink" href="#torch-cuda-get-arch-list" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.cuda.get_arch_list">
-<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">get_arch_list</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda.html#get_arch_list"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/__init__.py#L959"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.get_arch_list" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">get_arch_list</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda.html#get_arch_list"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/cuda/__init__.py#L959"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.get_arch_list" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return list CUDA architectures this library was compiled for.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
diff --git a/main/generated/torch.cuda.get_device_capability.html b/main/generated/torch.cuda.get_device_capability.html
index 1400f6a307c..17150d50720 100644
--- a/main/generated/torch.cuda.get_device_capability.html
+++ b/main/generated/torch.cuda.get_device_capability.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
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     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.cuda.get_device_capability<a class="headerlink" href="#torch-cuda-get-device-capability" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.cuda.get_device_capability">
-<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">get_device_capability</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda.html#get_device_capability"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/__init__.py#L507"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.get_device_capability" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">get_device_capability</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda.html#get_device_capability"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/cuda/__init__.py#L507"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.get_device_capability" title="Permalink to this definition">¶</a></dt>
 <dd><p>Get the cuda capability of a device.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.cuda.get_device_name.html b/main/generated/torch.cuda.get_device_name.html
index 3086d52a3fa..2046f688a40 100644
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+++ b/main/generated/torch.cuda.get_device_name.html
@@ -300,7 +300,7 @@
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     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
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     <div id="searchBox">
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@@ -612,7 +612,7 @@
 <h1>torch.cuda.get_device_name<a class="headerlink" href="#torch-cuda-get-device-name" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.cuda.get_device_name">
-<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">get_device_name</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda.html#get_device_name"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/__init__.py#L492"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.get_device_name" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">get_device_name</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda.html#get_device_name"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/cuda/__init__.py#L492"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.get_device_name" title="Permalink to this definition">¶</a></dt>
 <dd><p>Get the name of a device.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.cuda.get_device_properties.html b/main/generated/torch.cuda.get_device_properties.html
index 9a919f0b8c5..d9fbf52ecc4 100644
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+++ b/main/generated/torch.cuda.get_device_properties.html
@@ -300,7 +300,7 @@
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     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.cuda.get_device_properties<a class="headerlink" href="#torch-cuda-get-device-properties" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.cuda.get_device_properties">
-<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">get_device_properties</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda.html#get_device_properties"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/__init__.py#L524"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.get_device_properties" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">get_device_properties</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda.html#get_device_properties"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/cuda/__init__.py#L524"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.get_device_properties" title="Permalink to this definition">¶</a></dt>
 <dd><p>Get the properties of a device.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.cuda.get_gencode_flags.html b/main/generated/torch.cuda.get_gencode_flags.html
index fd88707824f..9fa73b28eeb 100644
--- a/main/generated/torch.cuda.get_gencode_flags.html
+++ b/main/generated/torch.cuda.get_gencode_flags.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.cuda.get_gencode_flags<a class="headerlink" href="#torch-cuda-get-gencode-flags" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.cuda.get_gencode_flags">
-<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">get_gencode_flags</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda.html#get_gencode_flags"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/__init__.py#L969"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.get_gencode_flags" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">get_gencode_flags</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda.html#get_gencode_flags"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/cuda/__init__.py#L969"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.get_gencode_flags" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return NVCC gencode flags this library was compiled with.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
diff --git a/main/generated/torch.cuda.get_per_process_memory_fraction.html b/main/generated/torch.cuda.get_per_process_memory_fraction.html
index 978b7164745..3350c62a6fb 100644
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+++ b/main/generated/torch.cuda.get_per_process_memory_fraction.html
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           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.cuda.get_per_process_memory_fraction<a class="headerlink" href="#torch-cuda-get-per-process-memory-fraction" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.cuda.get_per_process_memory_fraction">
-<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">get_per_process_memory_fraction</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/memory.html#get_per_process_memory_fraction"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/memory.py#L190"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.get_per_process_memory_fraction" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">get_per_process_memory_fraction</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/memory.html#get_per_process_memory_fraction"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/cuda/memory.py#L190"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.get_per_process_memory_fraction" title="Permalink to this definition">¶</a></dt>
 <dd><p>Get memory fraction for a process.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.cuda.get_rng_state.html b/main/generated/torch.cuda.get_rng_state.html
index 0fedb2921af..657015ab6d4 100644
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+++ b/main/generated/torch.cuda.get_rng_state.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.cuda.get_rng_state<a class="headerlink" href="#torch-cuda-get-rng-state" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.cuda.get_rng_state">
-<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">get_rng_state</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'cuda'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/random.html#get_rng_state"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/random.py#L24"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.get_rng_state" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">get_rng_state</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'cuda'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/random.html#get_rng_state"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/cuda/random.py#L24"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.get_rng_state" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the random number generator state of the specified GPU as a ByteTensor.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.cuda.get_rng_state_all.html b/main/generated/torch.cuda.get_rng_state_all.html
index e58e73121be..08e4985574c 100644
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@@ -612,7 +612,7 @@
 <h1>torch.cuda.get_rng_state_all<a class="headerlink" href="#torch-cuda-get-rng-state-all" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.cuda.get_rng_state_all">
-<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">get_rng_state_all</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/random.html#get_rng_state_all"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/random.py#L46"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.get_rng_state_all" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">get_rng_state_all</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/random.html#get_rng_state_all"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/cuda/random.py#L46"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.get_rng_state_all" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return a list of ByteTensor representing the random number states of all devices.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
diff --git a/main/generated/torch.cuda.get_stream_from_external.html b/main/generated/torch.cuda.get_stream_from_external.html
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           <div class="pytorch-left-menu-search">
             
     <div class="version">
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@@ -612,7 +612,7 @@
 <h1>torch.cuda.get_stream_from_external<a class="headerlink" href="#torch-cuda-get-stream-from-external" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.cuda.get_stream_from_external">
-<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">get_stream_from_external</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">data_ptr</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda.html#get_stream_from_external"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/__init__.py#L1051"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.get_stream_from_external" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">get_stream_from_external</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">data_ptr</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda.html#get_stream_from_external"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/cuda/__init__.py#L1051"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.get_stream_from_external" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return a <a class="reference internal" href="torch.cuda.Stream.html#torch.cuda.Stream" title="torch.cuda.Stream"><code class="xref py py-class docutils literal notranslate"><span class="pre">Stream</span></code></a> from an externally allocated CUDA stream.</p>
 <p>This function is used to wrap streams allocated in other libraries in order
 to facilitate data exchange and multi-library interactions.</p>
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     <div id="searchBox">
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@@ -612,7 +612,7 @@
 <h1>torch.cuda.get_sync_debug_mode<a class="headerlink" href="#torch-cuda-get-sync-debug-mode" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.cuda.get_sync_debug_mode">
-<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">get_sync_debug_mode</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda.html#get_sync_debug_mode"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/__init__.py#L1112"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.get_sync_debug_mode" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">get_sync_debug_mode</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda.html#get_sync_debug_mode"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/cuda/__init__.py#L1112"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.get_sync_debug_mode" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return current value of debug mode for cuda synchronizing operations.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
diff --git a/main/generated/torch.cuda.graph.html b/main/generated/torch.cuda.graph.html
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@@ -612,7 +612,7 @@
 <h1>graph<a class="headerlink" href="#graph" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.cuda.graph">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">graph</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">cuda_graph</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pool</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stream</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">capture_error_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'global'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/graphs.html#graph"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/graphs.py#L117"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.graph" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">graph</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">cuda_graph</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pool</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stream</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">capture_error_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'global'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/graphs.html#graph"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/cuda/graphs.py#L117"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.graph" title="Permalink to this definition">¶</a></dt>
 <dd><p>Context-manager that captures CUDA work into a <a class="reference internal" href="torch.cuda.CUDAGraph.html#torch.cuda.CUDAGraph" title="torch.cuda.CUDAGraph"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.cuda.CUDAGraph</span></code></a> object for later replay.</p>
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 detailed use, and constraints.</p>
diff --git a/main/generated/torch.cuda.graph_pool_handle.html b/main/generated/torch.cuda.graph_pool_handle.html
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@@ -612,7 +612,7 @@
 <h1>torch.cuda.graph_pool_handle<a class="headerlink" href="#torch-cuda-graph-pool-handle" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.cuda.graph_pool_handle">
-<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">graph_pool_handle</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/graphs.html#graph_pool_handle"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/graphs.py#L34"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.graph_pool_handle" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">graph_pool_handle</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/graphs.html#graph_pool_handle"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/cuda/graphs.py#L34"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.graph_pool_handle" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return an opaque token representing the id of a graph memory pool.</p>
 <p>See <a class="reference internal" href="../notes/cuda.html#graph-memory-management"><span class="std std-ref">Graph memory management</span></a>.</p>
 <div class="admonition warning">
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     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
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@@ -612,7 +612,7 @@
 <h1>torch.cuda.init<a class="headerlink" href="#torch-cuda-init" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.cuda.init">
-<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">init</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda.html#init"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/__init__.py#L288"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.init" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">init</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda.html#init"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/cuda/__init__.py#L288"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.init" title="Permalink to this definition">¶</a></dt>
 <dd><p>Initialize PyTorch’s CUDA state.</p>
 <p>You may need to call this explicitly if you are interacting with
 PyTorch via its C API, as Python bindings for CUDA functionality
diff --git a/main/generated/torch.cuda.initial_seed.html b/main/generated/torch.cuda.initial_seed.html
index af5bc30b4b0..6b95f5de88a 100644
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+++ b/main/generated/torch.cuda.initial_seed.html
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     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
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     <div id="searchBox">
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@@ -612,7 +612,7 @@
 <h1>torch.cuda.initial_seed<a class="headerlink" href="#torch-cuda-initial-seed" title="Permalink to this heading">¶</a></h1>
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 <dt class="sig sig-object py" id="torch.cuda.initial_seed">
-<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">initial_seed</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/random.html#initial_seed"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/random.py#L172"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.initial_seed" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">initial_seed</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/random.html#initial_seed"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/cuda/random.py#L172"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.initial_seed" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the current random seed of the current GPU.</p>
 <div class="admonition warning">
 <p class="admonition-title">Warning</p>
diff --git a/main/generated/torch.cuda.ipc_collect.html b/main/generated/torch.cuda.ipc_collect.html
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+++ b/main/generated/torch.cuda.ipc_collect.html
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@@ -612,7 +612,7 @@
 <h1>torch.cuda.ipc_collect<a class="headerlink" href="#torch-cuda-ipc-collect" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.cuda.ipc_collect">
-<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">ipc_collect</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda.html#ipc_collect"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/__init__.py#L1002"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.ipc_collect" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">ipc_collect</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda.html#ipc_collect"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/cuda/__init__.py#L1002"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.ipc_collect" title="Permalink to this definition">¶</a></dt>
 <dd><p>Force collects GPU memory after it has been released by CUDA IPC.</p>
 <div class="admonition note">
 <p class="admonition-title">Note</p>
diff --git a/main/generated/torch.cuda.is_available.html b/main/generated/torch.cuda.is_available.html
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@@ -612,7 +612,7 @@
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 <dt class="sig sig-object py" id="torch.cuda.is_available">
-<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">is_available</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda.html#is_available"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/__init__.py#L116"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.is_available" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">is_available</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda.html#is_available"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/cuda/__init__.py#L116"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.is_available" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return a bool indicating if CUDA is currently available.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
diff --git a/main/generated/torch.cuda.is_current_stream_capturing.html b/main/generated/torch.cuda.is_current_stream_capturing.html
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@@ -612,7 +612,7 @@
 <h1>torch.cuda.is_current_stream_capturing<a class="headerlink" href="#torch-cuda-is-current-stream-capturing" title="Permalink to this heading">¶</a></h1>
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 <dt class="sig sig-object py" id="torch.cuda.is_current_stream_capturing">
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+<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">is_current_stream_capturing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/graphs.html#is_current_stream_capturing"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/cuda/graphs.py#L25"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.is_current_stream_capturing" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return True if CUDA graph capture is underway on the current CUDA stream, False otherwise.</p>
 <p>If a CUDA context does not exist on the current device, returns False without initializing the context.</p>
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@@ -612,7 +612,7 @@
 <h1>torch.cuda.is_initialized<a class="headerlink" href="#torch-cuda-is-initialized" title="Permalink to this heading">¶</a></h1>
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-<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">is_initialized</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda.html#is_initialized"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/__init__.py#L254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.is_initialized" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">is_initialized</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda.html#is_initialized"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/cuda/__init__.py#L254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.is_initialized" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return whether PyTorch’s CUDA state has been initialized.</p>
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@@ -612,7 +612,7 @@
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-<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">is_tf32_supported</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda.html#is_tf32_supported"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/__init__.py#L171"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.is_tf32_supported" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">is_tf32_supported</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda.html#is_tf32_supported"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/cuda/__init__.py#L171"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.is_tf32_supported" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return a bool indicating if the current CUDA/ROCm device supports dtype tf32.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
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@@ -612,7 +612,7 @@
 <h1>torch.cuda.jiterator._create_jit_fn<a class="headerlink" href="#torch-cuda-jiterator-create-jit-fn" title="Permalink to this heading">¶</a></h1>
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 <dt class="sig sig-object py" id="torch.cuda.jiterator._create_jit_fn">
-<span class="sig-prename descclassname"><span class="pre">torch.cuda.jiterator.</span></span><span class="sig-name descname"><span class="pre">_create_jit_fn</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">code_string</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/jiterator.html#_create_jit_fn"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/jiterator.py#L98"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.jiterator._create_jit_fn" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.cuda.jiterator.</span></span><span class="sig-name descname"><span class="pre">_create_jit_fn</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">code_string</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/jiterator.html#_create_jit_fn"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/cuda/jiterator.py#L98"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.jiterator._create_jit_fn" title="Permalink to this definition">¶</a></dt>
 <dd><p>Create a jiterator-generated cuda kernel for an elementwise op.</p>
 <p>The code string has to be a valid CUDA function that describes the computation for a single element. The code
 string has to follow the c++ template pattern, as shown in the example below. This function will be inlined
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+++ b/main/generated/torch.cuda.jiterator._create_multi_output_jit_fn.html
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@@ -612,7 +612,7 @@
 <h1>torch.cuda.jiterator._create_multi_output_jit_fn<a class="headerlink" href="#torch-cuda-jiterator-create-multi-output-jit-fn" title="Permalink to this heading">¶</a></h1>
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-<span class="sig-prename descclassname"><span class="pre">torch.cuda.jiterator.</span></span><span class="sig-name descname"><span class="pre">_create_multi_output_jit_fn</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">code_string</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_outputs</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/jiterator.html#_create_multi_output_jit_fn"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/jiterator.py#L159"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.jiterator._create_multi_output_jit_fn" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.cuda.jiterator.</span></span><span class="sig-name descname"><span class="pre">_create_multi_output_jit_fn</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">code_string</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_outputs</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/jiterator.html#_create_multi_output_jit_fn"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/cuda/jiterator.py#L159"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.jiterator._create_multi_output_jit_fn" title="Permalink to this definition">¶</a></dt>
 <dd><p>Create a jiterator-generated cuda kernel for an elementwise op that supports returning one or more outputs.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.cuda.list_gpu_processes.html b/main/generated/torch.cuda.list_gpu_processes.html
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@@ -612,7 +612,7 @@
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 <dt class="sig sig-object py" id="torch.cuda.list_gpu_processes">
-<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">list_gpu_processes</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/memory.html#list_gpu_processes"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/memory.py#L657"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.list_gpu_processes" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">list_gpu_processes</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/memory.html#list_gpu_processes"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/cuda/memory.py#L657"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.list_gpu_processes" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return a human-readable printout of the running processes and their GPU memory use for a given device.</p>
 <p>This can be useful to display periodically during training, or when
 handling out-of-memory exceptions.</p>
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     <div class="version">
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@@ -612,7 +612,7 @@
 <h1>torch.cuda.make_graphed_callables<a class="headerlink" href="#torch-cuda-make-graphed-callables" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.cuda.make_graphed_callables">
-<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">make_graphed_callables</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">callables</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_warmup_iters</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">3</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">allow_unused_input</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pool</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/graphs.html#make_graphed_callables"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/graphs.py#L191"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.make_graphed_callables" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">make_graphed_callables</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">callables</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_warmup_iters</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">3</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">allow_unused_input</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pool</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/graphs.html#make_graphed_callables"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/cuda/graphs.py#L191"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.make_graphed_callables" title="Permalink to this definition">¶</a></dt>
 <dd><p>Accept callables (functions or <a class="reference internal" href="torch.nn.Module.html#torch.nn.Module" title="torch.nn.Module"><code class="xref py py-class docutils literal notranslate"><span class="pre">nn.Module</span></code></a>s) and returns graphed versions.</p>
 <p>Each graphed callable’s forward pass runs its source callable’s
 forward CUDA work as a CUDA graph inside a single autograd node.</p>
diff --git a/main/generated/torch.cuda.manual_seed.html b/main/generated/torch.cuda.manual_seed.html
index 362f57af4a8..53e2f55527e 100644
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+++ b/main/generated/torch.cuda.manual_seed.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
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     <div id="searchBox">
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@@ -612,7 +612,7 @@
 <h1>torch.cuda.manual_seed<a class="headerlink" href="#torch-cuda-manual-seed" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.cuda.manual_seed">
-<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">manual_seed</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">seed</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/random.html#manual_seed"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/random.py#L89"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.manual_seed" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">manual_seed</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">seed</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/random.html#manual_seed"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/cuda/random.py#L89"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.manual_seed" title="Permalink to this definition">¶</a></dt>
 <dd><p>Set the seed for generating random numbers for the current GPU.</p>
 <p>It’s safe to call this function if CUDA is not available; in that
 case, it is silently ignored.</p>
diff --git a/main/generated/torch.cuda.manual_seed_all.html b/main/generated/torch.cuda.manual_seed_all.html
index c33301a811d..7d6331a0e2e 100644
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@@ -300,7 +300,7 @@
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     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
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     </div>
     <div id="searchBox">
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@@ -612,7 +612,7 @@
 <h1>torch.cuda.manual_seed_all<a class="headerlink" href="#torch-cuda-manual-seed-all" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.cuda.manual_seed_all">
-<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">manual_seed_all</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">seed</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/random.html#manual_seed_all"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/random.py#L112"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.manual_seed_all" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">manual_seed_all</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">seed</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/random.html#manual_seed_all"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/cuda/random.py#L112"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.manual_seed_all" title="Permalink to this definition">¶</a></dt>
 <dd><p>Set the seed for generating random numbers on all GPUs.</p>
 <p>It’s safe to call this function if CUDA is not available; in that
 case, it is silently ignored.</p>
diff --git a/main/generated/torch.cuda.max_memory_allocated.html b/main/generated/torch.cuda.max_memory_allocated.html
index bc7cd36ccf7..0e1c99ce589 100644
--- a/main/generated/torch.cuda.max_memory_allocated.html
+++ b/main/generated/torch.cuda.max_memory_allocated.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
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@@ -612,7 +612,7 @@
 <h1>torch.cuda.max_memory_allocated<a class="headerlink" href="#torch-cuda-max-memory-allocated" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.cuda.max_memory_allocated">
-<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">max_memory_allocated</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/memory.html#max_memory_allocated"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/memory.py#L442"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.max_memory_allocated" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">max_memory_allocated</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/memory.html#max_memory_allocated"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/cuda/memory.py#L442"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.max_memory_allocated" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the maximum GPU memory occupied by tensors in bytes for a given device.</p>
 <p>By default, this returns the peak allocated memory since the beginning of
 this program. <a class="reference internal" href="torch.cuda.reset_peak_memory_stats.html#torch.cuda.reset_peak_memory_stats" title="torch.cuda.reset_peak_memory_stats"><code class="xref py py-func docutils literal notranslate"><span class="pre">reset_peak_memory_stats()</span></code></a> can be used to
diff --git a/main/generated/torch.cuda.max_memory_cached.html b/main/generated/torch.cuda.max_memory_cached.html
index bb781aeda97..bf5ac243331 100644
--- a/main/generated/torch.cuda.max_memory_cached.html
+++ b/main/generated/torch.cuda.max_memory_cached.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
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@@ -612,7 +612,7 @@
 <h1>torch.cuda.max_memory_cached<a class="headerlink" href="#torch-cuda-max-memory-cached" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.cuda.max_memory_cached">
-<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">max_memory_cached</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/memory.html#max_memory_cached"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/memory.py#L508"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.max_memory_cached" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">max_memory_cached</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/memory.html#max_memory_cached"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/cuda/memory.py#L508"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.max_memory_cached" title="Permalink to this definition">¶</a></dt>
 <dd><p>Deprecated; see <a class="reference internal" href="torch.cuda.max_memory_reserved.html#torch.cuda.max_memory_reserved" title="torch.cuda.max_memory_reserved"><code class="xref py py-func docutils literal notranslate"><span class="pre">max_memory_reserved()</span></code></a>.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
diff --git a/main/generated/torch.cuda.max_memory_reserved.html b/main/generated/torch.cuda.max_memory_reserved.html
index 2935ecf129a..549504d3d9b 100644
--- a/main/generated/torch.cuda.max_memory_reserved.html
+++ b/main/generated/torch.cuda.max_memory_reserved.html
@@ -300,7 +300,7 @@
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     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
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@@ -612,7 +612,7 @@
 <h1>torch.cuda.max_memory_reserved<a class="headerlink" href="#torch-cuda-max-memory-reserved" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.cuda.max_memory_reserved">
-<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">max_memory_reserved</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/memory.html#max_memory_reserved"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/memory.py#L478"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.max_memory_reserved" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">max_memory_reserved</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/memory.html#max_memory_reserved"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/cuda/memory.py#L478"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.max_memory_reserved" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the maximum GPU memory managed by the caching allocator in bytes for a given device.</p>
 <p>By default, this returns the peak cached memory since the beginning of this
 program. <a class="reference internal" href="torch.cuda.reset_peak_memory_stats.html#torch.cuda.reset_peak_memory_stats" title="torch.cuda.reset_peak_memory_stats"><code class="xref py py-func docutils literal notranslate"><span class="pre">reset_peak_memory_stats()</span></code></a> can be used to reset
diff --git a/main/generated/torch.cuda.mem_get_info.html b/main/generated/torch.cuda.mem_get_info.html
index 7e69841fa7a..b4538eb0fdf 100644
--- a/main/generated/torch.cuda.mem_get_info.html
+++ b/main/generated/torch.cuda.mem_get_info.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.cuda.mem_get_info<a class="headerlink" href="#torch-cuda-mem-get-info" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.cuda.mem_get_info">
-<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">mem_get_info</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/memory.html#mem_get_info"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/memory.py#L722"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.mem_get_info" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">mem_get_info</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/memory.html#mem_get_info"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/cuda/memory.py#L722"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.mem_get_info" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the global free and total GPU memory for a given device using cudaMemGetInfo.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.cuda.memory.caching_allocator_enable.html b/main/generated/torch.cuda.memory.caching_allocator_enable.html
index 24ad5b5b051..a869a14cb65 100644
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+++ b/main/generated/torch.cuda.memory.caching_allocator_enable.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
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+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
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@@ -612,7 +612,7 @@
 <h1>torch.cuda.memory.caching_allocator_enable<a class="headerlink" href="#torch-cuda-memory-caching-allocator-enable" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.cuda.memory.caching_allocator_enable">
-<span class="sig-prename descclassname"><span class="pre">torch.cuda.memory.</span></span><span class="sig-name descname"><span class="pre">caching_allocator_enable</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/memory.html#caching_allocator_enable"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/memory.py#L155"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.memory.caching_allocator_enable" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.cuda.memory.</span></span><span class="sig-name descname"><span class="pre">caching_allocator_enable</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/memory.html#caching_allocator_enable"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/cuda/memory.py#L155"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.memory.caching_allocator_enable" title="Permalink to this definition">¶</a></dt>
 <dd><p>Enable or disable the CUDA memory allocator. On by default.</p>
 <dl class="field-list simple">
 </dl>
diff --git a/main/generated/torch.cuda.memory_allocated.html b/main/generated/torch.cuda.memory_allocated.html
index 3589c597817..2128e055d9e 100644
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@@ -612,7 +612,7 @@
 <h1>torch.cuda.memory_allocated<a class="headerlink" href="#torch-cuda-memory-allocated" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.cuda.memory_allocated">
-<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">memory_allocated</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/memory.html#memory_allocated"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/memory.py#L425"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.memory_allocated" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">memory_allocated</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/memory.html#memory_allocated"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/cuda/memory.py#L425"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.memory_allocated" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the current GPU memory occupied by tensors in bytes for a given device.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.cuda.memory_cached.html b/main/generated/torch.cuda.memory_cached.html
index 8876ad3f8ad..4ad986fcd1f 100644
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+++ b/main/generated/torch.cuda.memory_cached.html
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           <div class="pytorch-left-menu-search">
             
     <div class="version">
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@@ -612,7 +612,7 @@
 <h1>torch.cuda.memory_cached<a class="headerlink" href="#torch-cuda-memory-cached" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.cuda.memory_cached">
-<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">memory_cached</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/memory.html#memory_cached"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/memory.py#L499"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.memory_cached" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">memory_cached</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/memory.html#memory_cached"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/cuda/memory.py#L499"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.memory_cached" title="Permalink to this definition">¶</a></dt>
 <dd><p>Deprecated; see <a class="reference internal" href="torch.cuda.memory_reserved.html#torch.cuda.memory_reserved" title="torch.cuda.memory_reserved"><code class="xref py py-func docutils literal notranslate"><span class="pre">memory_reserved()</span></code></a>.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
diff --git a/main/generated/torch.cuda.memory_reserved.html b/main/generated/torch.cuda.memory_reserved.html
index c4d35935b57..b322c4e9eec 100644
--- a/main/generated/torch.cuda.memory_reserved.html
+++ b/main/generated/torch.cuda.memory_reserved.html
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     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
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     </div>
     <div id="searchBox">
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@@ -612,7 +612,7 @@
 <h1>torch.cuda.memory_reserved<a class="headerlink" href="#torch-cuda-memory-reserved" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.cuda.memory_reserved">
-<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">memory_reserved</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/memory.html#memory_reserved"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/memory.py#L463"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.memory_reserved" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">memory_reserved</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/memory.html#memory_reserved"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/cuda/memory.py#L463"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.memory_reserved" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the current GPU memory managed by the caching allocator in bytes for a given device.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.cuda.memory_snapshot.html b/main/generated/torch.cuda.memory_snapshot.html
index 3e87c06db23..ba6a29515d0 100644
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+++ b/main/generated/torch.cuda.memory_snapshot.html
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     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
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@@ -612,7 +612,7 @@
 <h1>torch.cuda.memory_snapshot<a class="headerlink" href="#torch-cuda-memory-snapshot" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.cuda.memory_snapshot">
-<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">memory_snapshot</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/memory.html#memory_snapshot"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/memory.py#L517"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.memory_snapshot" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">memory_snapshot</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/memory.html#memory_snapshot"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/cuda/memory.py#L517"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.memory_snapshot" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return a snapshot of the CUDA memory allocator state across all devices.</p>
 <p>Interpreting the output of this function requires familiarity with the
 memory allocator internals.</p>
diff --git a/main/generated/torch.cuda.memory_stats.html b/main/generated/torch.cuda.memory_stats.html
index 190aa6794fd..245baa804a2 100644
--- a/main/generated/torch.cuda.memory_stats.html
+++ b/main/generated/torch.cuda.memory_stats.html
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     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
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@@ -612,7 +612,7 @@
 <h1>torch.cuda.memory_stats<a class="headerlink" href="#torch-cuda-memory-stats" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.cuda.memory_stats">
-<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">memory_stats</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/memory.html#memory_stats"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/memory.py#L221"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.memory_stats" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">memory_stats</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/memory.html#memory_stats"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/cuda/memory.py#L221"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.memory_stats" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return a dictionary of CUDA memory allocator statistics for a given device.</p>
 <p>The return value of this function is a dictionary of statistics, each of
 which is a non-negative integer.</p>
diff --git a/main/generated/torch.cuda.memory_summary.html b/main/generated/torch.cuda.memory_summary.html
index 9c1d3bd3c3e..81ff4c1ef71 100644
--- a/main/generated/torch.cuda.memory_summary.html
+++ b/main/generated/torch.cuda.memory_summary.html
@@ -300,7 +300,7 @@
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     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
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     </div>
     <div id="searchBox">
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@@ -612,7 +612,7 @@
 <h1>torch.cuda.memory_summary<a class="headerlink" href="#torch-cuda-memory-summary" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.cuda.memory_summary">
-<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">memory_summary</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">abbreviated</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/memory.html#memory_summary"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/memory.py#L530"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.memory_summary" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">memory_summary</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">abbreviated</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/memory.html#memory_summary"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/cuda/memory.py#L530"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.memory_summary" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return a human-readable printout of the current memory allocator statistics for a given device.</p>
 <p>This can be useful to display periodically during training, or when
 handling out-of-memory exceptions.</p>
diff --git a/main/generated/torch.cuda.memory_usage.html b/main/generated/torch.cuda.memory_usage.html
index 6af27a09c1b..eae2d4fc00d 100644
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+++ b/main/generated/torch.cuda.memory_usage.html
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     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
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@@ -612,7 +612,7 @@
 <h1>torch.cuda.memory_usage<a class="headerlink" href="#torch-cuda-memory-usage" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.cuda.memory_usage">
-<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">memory_usage</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda.html#memory_usage"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/__init__.py#L1239"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.memory_usage" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">memory_usage</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda.html#memory_usage"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/cuda/__init__.py#L1239"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.memory_usage" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the percent of time over the past sample period during which global (device)
 memory was being read or written as given by <cite>nvidia-smi</cite>.</p>
 <dl class="field-list simple">
diff --git a/main/generated/torch.cuda.nvtx.mark.html b/main/generated/torch.cuda.nvtx.mark.html
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+++ b/main/generated/torch.cuda.nvtx.mark.html
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           <div class="pytorch-left-menu-search">
             
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@@ -612,7 +612,7 @@
 <h1>torch.cuda.nvtx.mark<a class="headerlink" href="#torch-cuda-nvtx-mark" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.cuda.nvtx.mark">
-<span class="sig-prename descclassname"><span class="pre">torch.cuda.nvtx.</span></span><span class="sig-name descname"><span class="pre">mark</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">msg</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/nvtx.html#mark"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/nvtx.py#L101"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.nvtx.mark" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.cuda.nvtx.</span></span><span class="sig-name descname"><span class="pre">mark</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">msg</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/nvtx.html#mark"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/cuda/nvtx.py#L101"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.nvtx.mark" title="Permalink to this definition">¶</a></dt>
 <dd><p>Describe an instantaneous event that occurred at some point.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.cuda.nvtx.range.html b/main/generated/torch.cuda.nvtx.range.html
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@@ -612,7 +612,7 @@
 <h1>torch.cuda.nvtx.range<a class="headerlink" href="#torch-cuda-nvtx-range" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.cuda.nvtx.range">
-<span class="sig-prename descclassname"><span class="pre">torch.cuda.nvtx.</span></span><span class="sig-name descname"><span class="pre">range</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">msg</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/nvtx.html#range"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/nvtx.py#L111"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.nvtx.range" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.cuda.nvtx.</span></span><span class="sig-name descname"><span class="pre">range</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">msg</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/nvtx.html#range"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/cuda/nvtx.py#L111"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.nvtx.range" title="Permalink to this definition">¶</a></dt>
 <dd><p>Context manager / decorator that pushes an NVTX range at the beginning
 of its scope, and pops it at the end. If extra arguments are given,
 they are passed as arguments to msg.format().</p>
diff --git a/main/generated/torch.cuda.nvtx.range_pop.html b/main/generated/torch.cuda.nvtx.range_pop.html
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@@ -612,7 +612,7 @@
 <h1>torch.cuda.nvtx.range_pop<a class="headerlink" href="#torch-cuda-nvtx-range-pop" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.cuda.nvtx.range_pop">
-<span class="sig-prename descclassname"><span class="pre">torch.cuda.nvtx.</span></span><span class="sig-name descname"><span class="pre">range_pop</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/nvtx.html#range_pop"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/nvtx.py#L37"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.nvtx.range_pop" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.cuda.nvtx.</span></span><span class="sig-name descname"><span class="pre">range_pop</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/nvtx.html#range_pop"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/cuda/nvtx.py#L37"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.nvtx.range_pop" title="Permalink to this definition">¶</a></dt>
 <dd><p>Pop a range off of a stack of nested range spans.  Returns the  zero-based depth of the range that is ended.</p>
 </dd></dl>
 
diff --git a/main/generated/torch.cuda.nvtx.range_push.html b/main/generated/torch.cuda.nvtx.range_push.html
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@@ -612,7 +612,7 @@
 <h1>torch.cuda.nvtx.range_push<a class="headerlink" href="#torch-cuda-nvtx-range-push" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.cuda.nvtx.range_push">
-<span class="sig-prename descclassname"><span class="pre">torch.cuda.nvtx.</span></span><span class="sig-name descname"><span class="pre">range_push</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">msg</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/nvtx.html#range_push"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/nvtx.py#L27"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.nvtx.range_push" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.cuda.nvtx.</span></span><span class="sig-name descname"><span class="pre">range_push</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">msg</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/nvtx.html#range_push"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/cuda/nvtx.py#L27"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.nvtx.range_push" title="Permalink to this definition">¶</a></dt>
 <dd><p>Push a range onto a stack of nested range span.  Returns zero-based depth of the range that is started.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.cuda.power_draw.html b/main/generated/torch.cuda.power_draw.html
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@@ -612,7 +612,7 @@
 <h1>torch.cuda.power_draw<a class="headerlink" href="#torch-cuda-power-draw" title="Permalink to this heading">¶</a></h1>
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 <dt class="sig sig-object py" id="torch.cuda.power_draw">
-<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">power_draw</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda.html#power_draw"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/__init__.py#L1302"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.power_draw" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">power_draw</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda.html#power_draw"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/cuda/__init__.py#L1302"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.power_draw" title="Permalink to this definition">¶</a></dt>
 <dd><dl class="simple">
 <dt>Return the average power draw of the GPU sensor in mW (MilliWatts)</dt><dd><p>over the past sample period as given by <cite>nvidia-smi</cite> for Fermi or newer fully supported devices.</p>
 </dd>
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@@ -612,7 +612,7 @@
 <h1>torch.cuda.reset_max_memory_allocated<a class="headerlink" href="#torch-cuda-reset-max-memory-allocated" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.cuda.reset_max_memory_allocated">
-<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">reset_max_memory_allocated</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/memory.html#reset_max_memory_allocated"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/memory.py#L373"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.reset_max_memory_allocated" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">reset_max_memory_allocated</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/memory.html#reset_max_memory_allocated"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/cuda/memory.py#L373"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.reset_max_memory_allocated" title="Permalink to this definition">¶</a></dt>
 <dd><p>Reset the starting point in tracking maximum GPU memory occupied by tensors for a given device.</p>
 <p>See <a class="reference internal" href="torch.cuda.max_memory_allocated.html#torch.cuda.max_memory_allocated" title="torch.cuda.max_memory_allocated"><code class="xref py py-func docutils literal notranslate"><span class="pre">max_memory_allocated()</span></code></a> for details.</p>
 <dl class="field-list simple">
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@@ -612,7 +612,7 @@
 <h1>torch.cuda.reset_max_memory_cached<a class="headerlink" href="#torch-cuda-reset-max-memory-cached" title="Permalink to this heading">¶</a></h1>
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 <dt class="sig sig-object py" id="torch.cuda.reset_max_memory_cached">
-<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">reset_max_memory_cached</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/memory.html#reset_max_memory_cached"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/memory.py#L399"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.reset_max_memory_cached" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">reset_max_memory_cached</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/memory.html#reset_max_memory_cached"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/cuda/memory.py#L399"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.reset_max_memory_cached" title="Permalink to this definition">¶</a></dt>
 <dd><p>Reset the starting point in tracking maximum GPU memory managed by the caching allocator for a given device.</p>
 <p>See <a class="reference internal" href="torch.cuda.max_memory_cached.html#torch.cuda.max_memory_cached" title="torch.cuda.max_memory_cached"><code class="xref py py-func docutils literal notranslate"><span class="pre">max_memory_cached()</span></code></a> for details.</p>
 <dl class="field-list simple">
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index 2baa69ecfc3..0b61bb18f4a 100644
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 <h1>torch.cuda.reset_peak_memory_stats<a class="headerlink" href="#torch-cuda-reset-peak-memory-stats" title="Permalink to this heading">¶</a></h1>
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 <dt class="sig sig-object py" id="torch.cuda.reset_peak_memory_stats">
-<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">reset_peak_memory_stats</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/memory.html#reset_peak_memory_stats"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/memory.py#L354"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.reset_peak_memory_stats" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">reset_peak_memory_stats</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/memory.html#reset_peak_memory_stats"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/cuda/memory.py#L354"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.reset_peak_memory_stats" title="Permalink to this definition">¶</a></dt>
 <dd><p>Reset the “peak” stats tracked by the CUDA memory allocator.</p>
 <p>See <a class="reference internal" href="torch.cuda.memory_stats.html#torch.cuda.memory_stats" title="torch.cuda.memory_stats"><code class="xref py py-func docutils literal notranslate"><span class="pre">memory_stats()</span></code></a> for details. Peak stats correspond to the
 <cite>“peak”</cite> key in each individual stat dict.</p>
diff --git a/main/generated/torch.cuda.seed.html b/main/generated/torch.cuda.seed.html
index 8ee9c2b6a7c..e009d497bf3 100644
--- a/main/generated/torch.cuda.seed.html
+++ b/main/generated/torch.cuda.seed.html
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     <div class="version">
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+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.cuda.seed<a class="headerlink" href="#torch-cuda-seed" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.cuda.seed">
-<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">seed</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/random.html#seed"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/random.py#L131"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.seed" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">seed</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/random.html#seed"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/cuda/random.py#L131"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.seed" title="Permalink to this definition">¶</a></dt>
 <dd><p>Set the seed for generating random numbers to a random number for the current GPU.</p>
 <p>It’s safe to call this function if CUDA is not available; in that
 case, it is silently ignored.</p>
diff --git a/main/generated/torch.cuda.seed_all.html b/main/generated/torch.cuda.seed_all.html
index b9ee389b8f3..988b4e1ce2b 100644
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+++ b/main/generated/torch.cuda.seed_all.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
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@@ -612,7 +612,7 @@
 <h1>torch.cuda.seed_all<a class="headerlink" href="#torch-cuda-seed-all" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.cuda.seed_all">
-<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">seed_all</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/random.html#seed_all"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/random.py#L150"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.seed_all" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">seed_all</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/random.html#seed_all"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/cuda/random.py#L150"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.seed_all" title="Permalink to this definition">¶</a></dt>
 <dd><p>Set the seed for generating random numbers to a random number on all GPUs.</p>
 <p>It’s safe to call this function if CUDA is not available; in that
 case, it is silently ignored.</p>
diff --git a/main/generated/torch.cuda.set_device.html b/main/generated/torch.cuda.set_device.html
index 6aa6316272a..0898ee7dff8 100644
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+++ b/main/generated/torch.cuda.set_device.html
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+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
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     <div id="searchBox">
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@@ -612,7 +612,7 @@
 <h1>torch.cuda.set_device<a class="headerlink" href="#torch-cuda-set-device" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.cuda.set_device">
-<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">set_device</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda.html#set_device"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/__init__.py#L477"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.set_device" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">set_device</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda.html#set_device"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/cuda/__init__.py#L477"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.set_device" title="Permalink to this definition">¶</a></dt>
 <dd><p>Set the current device.</p>
 <p>Usage of this function is discouraged in favor of <a class="reference internal" href="torch.cuda.device.html#torch.cuda.device" title="torch.cuda.device"><code class="xref any py py-class docutils literal notranslate"><span class="pre">device</span></code></a>. In most
 cases it’s better to use <code class="docutils literal notranslate"><span class="pre">CUDA_VISIBLE_DEVICES</span></code> environmental variable.</p>
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+++ b/main/generated/torch.cuda.set_per_process_memory_fraction.html
@@ -300,7 +300,7 @@
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-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
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     <div id="searchBox">
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@@ -612,7 +612,7 @@
 <h1>torch.cuda.set_per_process_memory_fraction<a class="headerlink" href="#torch-cuda-set-per-process-memory-fraction" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.cuda.set_per_process_memory_fraction">
-<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">set_per_process_memory_fraction</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">fraction</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/memory.html#set_per_process_memory_fraction"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/memory.py#L161"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.set_per_process_memory_fraction" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">set_per_process_memory_fraction</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">fraction</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/memory.html#set_per_process_memory_fraction"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/cuda/memory.py#L161"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.set_per_process_memory_fraction" title="Permalink to this definition">¶</a></dt>
 <dd><p>Set memory fraction for a process.</p>
 <p>The fraction is used to limit an caching allocator to allocated memory on a CUDA device.
 The allowed value equals the total visible memory multiplied fraction.
diff --git a/main/generated/torch.cuda.set_rng_state.html b/main/generated/torch.cuda.set_rng_state.html
index 2f8f45f8d89..7842164da2d 100644
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+++ b/main/generated/torch.cuda.set_rng_state.html
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           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.cuda.set_rng_state<a class="headerlink" href="#torch-cuda-set-rng-state" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.cuda.set_rng_state">
-<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">set_rng_state</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">new_state</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'cuda'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/random.html#set_rng_state"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/random.py#L52"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.set_rng_state" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">set_rng_state</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">new_state</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'cuda'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/random.html#set_rng_state"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/cuda/random.py#L52"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.set_rng_state" title="Permalink to this definition">¶</a></dt>
 <dd><p>Set the random number generator state of the specified GPU.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.cuda.set_rng_state_all.html b/main/generated/torch.cuda.set_rng_state_all.html
index ce606691d26..6e1b2f24b2a 100644
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+++ b/main/generated/torch.cuda.set_rng_state_all.html
@@ -300,7 +300,7 @@
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     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
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@@ -612,7 +612,7 @@
 <h1>torch.cuda.set_rng_state_all<a class="headerlink" href="#torch-cuda-set-rng-state-all" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.cuda.set_rng_state_all">
-<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">set_rng_state_all</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">new_states</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/random.html#set_rng_state_all"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/random.py#L79"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.set_rng_state_all" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">set_rng_state_all</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">new_states</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda/random.html#set_rng_state_all"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/cuda/random.py#L79"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.set_rng_state_all" title="Permalink to this definition">¶</a></dt>
 <dd><p>Set the random number generator state of all devices.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.cuda.set_stream.html b/main/generated/torch.cuda.set_stream.html
index afb3d621b97..58056018f90 100644
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+++ b/main/generated/torch.cuda.set_stream.html
@@ -300,7 +300,7 @@
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     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
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@@ -612,7 +612,7 @@
 <h1>torch.cuda.set_stream<a class="headerlink" href="#torch-cuda-set-stream" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.cuda.set_stream">
-<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">set_stream</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">stream</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda.html#set_stream"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/__init__.py#L639"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.set_stream" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">set_stream</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">stream</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda.html#set_stream"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/cuda/__init__.py#L639"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.set_stream" title="Permalink to this definition">¶</a></dt>
 <dd><dl class="simple">
 <dt>Set the current stream.This is a wrapper API to set the stream.</dt><dd><p>Usage of this function is discouraged in favor of the <code class="docutils literal notranslate"><span class="pre">stream</span></code>
 context manager.</p>
diff --git a/main/generated/torch.cuda.set_sync_debug_mode.html b/main/generated/torch.cuda.set_sync_debug_mode.html
index d3d44eeb55f..0221369345c 100644
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+++ b/main/generated/torch.cuda.set_sync_debug_mode.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
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@@ -612,7 +612,7 @@
 <h1>torch.cuda.set_sync_debug_mode<a class="headerlink" href="#torch-cuda-set-sync-debug-mode" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.cuda.set_sync_debug_mode">
-<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">set_sync_debug_mode</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">debug_mode</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda.html#set_sync_debug_mode"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/__init__.py#L1085"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.set_sync_debug_mode" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">set_sync_debug_mode</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">debug_mode</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda.html#set_sync_debug_mode"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/cuda/__init__.py#L1085"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.set_sync_debug_mode" title="Permalink to this definition">¶</a></dt>
 <dd><p>Set the debug mode for cuda synchronizing operations.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.cuda.stream.html b/main/generated/torch.cuda.stream.html
index b382317216a..b16163ad653 100644
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@@ -612,7 +612,7 @@
 <h1>torch.cuda.stream<a class="headerlink" href="#torch-cuda-stream" title="Permalink to this heading">¶</a></h1>
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-<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">stream</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">stream</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda.html#stream"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/__init__.py#L611"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.stream" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">stream</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">stream</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda.html#stream"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/cuda/__init__.py#L611"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.stream" title="Permalink to this definition">¶</a></dt>
 <dd><p>Wrap around the Context-manager StreamContext that selects a given stream.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.cuda.synchronize.html b/main/generated/torch.cuda.synchronize.html
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@@ -612,7 +612,7 @@
 <h1>torch.cuda.synchronize<a class="headerlink" href="#torch-cuda-synchronize" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.cuda.synchronize">
-<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">synchronize</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda.html#synchronize"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/__init__.py#L989"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.synchronize" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">synchronize</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda.html#synchronize"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/cuda/__init__.py#L989"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.synchronize" title="Permalink to this definition">¶</a></dt>
 <dd><p>Wait for all kernels in all streams on a CUDA device to complete.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.cuda.temperature.html b/main/generated/torch.cuda.temperature.html
index 23046f6a689..5cbf4a25d23 100644
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@@ -612,7 +612,7 @@
 <h1>torch.cuda.temperature<a class="headerlink" href="#torch-cuda-temperature" title="Permalink to this heading">¶</a></h1>
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 <dt class="sig sig-object py" id="torch.cuda.temperature">
-<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">temperature</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda.html#temperature"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/__init__.py#L1281"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.temperature" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">temperature</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda.html#temperature"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/cuda/__init__.py#L1281"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.temperature" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the average temperature of the GPU sensor in Degrees C (Centigrades).</p>
 <p>The average temperature is computed based on past sample period as given by <cite>nvidia-smi</cite>.</p>
 <dl class="field-list simple">
diff --git a/main/generated/torch.cuda.utilization.html b/main/generated/torch.cuda.utilization.html
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@@ -612,7 +612,7 @@
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 <dt class="sig sig-object py" id="torch.cuda.utilization">
-<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">utilization</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda.html#utilization"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/__init__.py#L1260"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.utilization" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.cuda.</span></span><span class="sig-name descname"><span class="pre">utilization</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/cuda.html#utilization"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/cuda/__init__.py#L1260"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.utilization" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the percent of time over the past sample period during which one or
 more kernels was executing on the GPU as given by <cite>nvidia-smi</cite>.</p>
 <dl class="field-list simple">
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 <h1>torch.einsum<a class="headerlink" href="#torch-einsum" title="Permalink to this heading">¶</a></h1>
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-<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">einsum</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">equation</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">operands</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><span class="pre">Tensor</span></a></span></span><a class="reference internal" href="../_modules/torch/functional.html#einsum"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/functional.py#L211"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.einsum" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">einsum</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">equation</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">operands</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><span class="pre">Tensor</span></a></span></span><a class="reference internal" href="../_modules/torch/functional.html#einsum"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/functional.py#L211"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.einsum" title="Permalink to this definition">¶</a></dt>
 <dd><p>Sums the product of the elements of the input <code class="xref py py-attr docutils literal notranslate"><span class="pre">operands</span></code> along dimensions specified using a notation
 based on the Einstein summation convention.</p>
 <p>Einsum allows computing many common multi-dimensional linear algebraic array operations by representing them
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 <h1>enable_grad<a class="headerlink" href="#enable-grad" title="Permalink to this heading">¶</a></h1>
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-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">enable_grad</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">orig_func</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd/grad_mode.html#enable_grad"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/autograd/grad_mode.py#L88"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.enable_grad" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">enable_grad</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">orig_func</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd/grad_mode.html#enable_grad"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/autograd/grad_mode.py#L88"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.enable_grad" title="Permalink to this definition">¶</a></dt>
 <dd><p>Context-manager that enables gradient calculation.</p>
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diff --git a/main/generated/torch.frac.html b/main/generated/torch.frac.html
index 4a47c182478..5d2075a4d45 100644
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index b31c1f72369..da018c8a6b4 100644
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diff --git a/main/generated/torch.from_dlpack.html b/main/generated/torch.from_dlpack.html
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@@ -612,7 +612,7 @@
 <h1>torch.from_dlpack<a class="headerlink" href="#torch-from-dlpack" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.from_dlpack">
-<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">from_dlpack</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">ext_tensor</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><span class="pre">Tensor</span></a></span></span><a class="reference internal" href="../_modules/torch/utils/dlpack.html#from_dlpack"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/utils/dlpack.py#L49"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.from_dlpack" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">from_dlpack</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">ext_tensor</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><span class="pre">Tensor</span></a></span></span><a class="reference internal" href="../_modules/torch/utils/dlpack.html#from_dlpack"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/utils/dlpack.py#L49"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.from_dlpack" title="Permalink to this definition">¶</a></dt>
 <dd><p>Converts a tensor from an external library into a <code class="docutils literal notranslate"><span class="pre">torch.Tensor</span></code>.</p>
 <p>The returned PyTorch tensor will share the memory with the input tensor
 (which may have come from another library). Note that in-place operations
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index 1a142b615f2..41b42848e96 100644
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diff --git a/main/generated/torch.from_numpy.html b/main/generated/torch.from_numpy.html
index 49056776d72..9cae5d7b320 100644
--- a/main/generated/torch.from_numpy.html
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diff --git a/main/generated/torch.frombuffer.html b/main/generated/torch.frombuffer.html
index 0a559f74a74..57fc4170d0b 100644
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diff --git a/main/generated/torch.full.html b/main/generated/torch.full.html
index 77805b54cdf..fd9639c760e 100644
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index 645c83a7a0e..c64c420cb3a 100644
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diff --git a/main/generated/torch.func.functional_call.html b/main/generated/torch.func.functional_call.html
index 686212c2c5c..9eb495a1bee 100644
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@@ -614,7 +614,7 @@
 <h1>torch.func.functional_call<a class="headerlink" href="#torch-func-functional-call" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.func.functional_call">
-<span class="sig-prename descclassname"><span class="pre">torch.func.</span></span><span class="sig-name descname"><span class="pre">functional_call</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">parameter_and_buffer_dicts</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kwargs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tie_weights</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">strict</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/_functorch/functional_call.py#L11"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.func.functional_call" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.func.</span></span><span class="sig-name descname"><span class="pre">functional_call</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">parameter_and_buffer_dicts</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kwargs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tie_weights</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">strict</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/_functorch/functional_call.py#L11"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.func.functional_call" title="Permalink to this definition">¶</a></dt>
 <dd><p>Performs a functional call on the module by replacing the module parameters
 and buffers with the provided ones.</p>
 <div class="admonition note">
diff --git a/main/generated/torch.func.functionalize.html b/main/generated/torch.func.functionalize.html
index 29c336d5a2f..8f8fb682501 100644
--- a/main/generated/torch.func.functionalize.html
+++ b/main/generated/torch.func.functionalize.html
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     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
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@@ -614,7 +614,7 @@
 <h1>torch.func.functionalize<a class="headerlink" href="#torch-func-functionalize" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.func.functionalize">
-<span class="sig-prename descclassname"><span class="pre">torch.func.</span></span><span class="sig-name descname"><span class="pre">functionalize</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">func</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">remove</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mutations'</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/_functorch/eager_transforms.py#L1464"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.func.functionalize" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.func.</span></span><span class="sig-name descname"><span class="pre">functionalize</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">func</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">remove</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mutations'</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/_functorch/eager_transforms.py#L1464"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.func.functionalize" title="Permalink to this definition">¶</a></dt>
 <dd><p>functionalize is a transform that can be used to remove (intermediate)
 mutations and aliasing from a function, while preserving the function’s
 semantics.</p>
diff --git a/main/generated/torch.func.grad.html b/main/generated/torch.func.grad.html
index 2d26fc7d751..cc964bf78fd 100644
--- a/main/generated/torch.func.grad.html
+++ b/main/generated/torch.func.grad.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>torch.func.grad<a class="headerlink" href="#torch-func-grad" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.func.grad">
-<span class="sig-prename descclassname"><span class="pre">torch.func.</span></span><span class="sig-name descname"><span class="pre">grad</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">func</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">argnums</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">has_aux</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/_functorch/apis.py#L298"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.func.grad" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.func.</span></span><span class="sig-name descname"><span class="pre">grad</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">func</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">argnums</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">has_aux</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/_functorch/apis.py#L298"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.func.grad" title="Permalink to this definition">¶</a></dt>
 <dd><p><code class="docutils literal notranslate"><span class="pre">grad</span></code> operator helps computing gradients of <code class="docutils literal notranslate"><span class="pre">func</span></code> with respect to the
 input(s) specified by <code class="docutils literal notranslate"><span class="pre">argnums</span></code>. This operator can be nested to
 compute higher-order gradients.</p>
diff --git a/main/generated/torch.func.grad_and_value.html b/main/generated/torch.func.grad_and_value.html
index fe65b0a89f5..3443f53707c 100644
--- a/main/generated/torch.func.grad_and_value.html
+++ b/main/generated/torch.func.grad_and_value.html
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           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>torch.func.grad_and_value<a class="headerlink" href="#torch-func-grad-and-value" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.func.grad_and_value">
-<span class="sig-prename descclassname"><span class="pre">torch.func.</span></span><span class="sig-name descname"><span class="pre">grad_and_value</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">func</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">argnums</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">has_aux</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/_functorch/apis.py#L406"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.func.grad_and_value" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.func.</span></span><span class="sig-name descname"><span class="pre">grad_and_value</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">func</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">argnums</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">has_aux</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/_functorch/apis.py#L406"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.func.grad_and_value" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns a function to compute a tuple of the gradient and primal, or
 forward, computation.</p>
 <dl class="field-list simple">
diff --git a/main/generated/torch.func.hessian.html b/main/generated/torch.func.hessian.html
index 875b6068145..402a527f3ec 100644
--- a/main/generated/torch.func.hessian.html
+++ b/main/generated/torch.func.hessian.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>torch.func.hessian<a class="headerlink" href="#torch-func-hessian" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.func.hessian">
-<span class="sig-prename descclassname"><span class="pre">torch.func.</span></span><span class="sig-name descname"><span class="pre">hessian</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">func</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">argnums</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/_functorch/eager_transforms.py#L1310"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.func.hessian" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.func.</span></span><span class="sig-name descname"><span class="pre">hessian</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">func</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">argnums</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/_functorch/eager_transforms.py#L1310"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.func.hessian" title="Permalink to this definition">¶</a></dt>
 <dd><p>Computes the Hessian of <code class="docutils literal notranslate"><span class="pre">func</span></code> with respect to the arg(s) at index
 <code class="docutils literal notranslate"><span class="pre">argnum</span></code> via a forward-over-reverse strategy.</p>
 <p>The forward-over-reverse strategy (composing <code class="docutils literal notranslate"><span class="pre">jacfwd(jacrev(func))</span></code>) is
diff --git a/main/generated/torch.func.jacfwd.html b/main/generated/torch.func.jacfwd.html
index 085d7431fd8..c04805392f2 100644
--- a/main/generated/torch.func.jacfwd.html
+++ b/main/generated/torch.func.jacfwd.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>torch.func.jacfwd<a class="headerlink" href="#torch-func-jacfwd" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.func.jacfwd">
-<span class="sig-prename descclassname"><span class="pre">torch.func.</span></span><span class="sig-name descname"><span class="pre">jacfwd</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">func</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">argnums</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">has_aux</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">randomness</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'error'</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/_functorch/eager_transforms.py#L1139"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.func.jacfwd" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.func.</span></span><span class="sig-name descname"><span class="pre">jacfwd</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">func</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">argnums</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">has_aux</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">randomness</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'error'</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/_functorch/eager_transforms.py#L1139"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.func.jacfwd" title="Permalink to this definition">¶</a></dt>
 <dd><p>Computes the Jacobian of <code class="docutils literal notranslate"><span class="pre">func</span></code> with respect to the arg(s) at index
 <code class="docutils literal notranslate"><span class="pre">argnum</span></code> using forward-mode autodiff</p>
 <dl class="field-list simple">
diff --git a/main/generated/torch.func.jacrev.html b/main/generated/torch.func.jacrev.html
index d494c14d394..297c29e3503 100644
--- a/main/generated/torch.func.jacrev.html
+++ b/main/generated/torch.func.jacrev.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>torch.func.jacrev<a class="headerlink" href="#torch-func-jacrev" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.func.jacrev">
-<span class="sig-prename descclassname"><span class="pre">torch.func.</span></span><span class="sig-name descname"><span class="pre">jacrev</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">func</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">argnums</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">has_aux</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">chunk_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_preallocate_and_copy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/_functorch/eager_transforms.py#L428"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.func.jacrev" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.func.</span></span><span class="sig-name descname"><span class="pre">jacrev</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">func</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">argnums</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">has_aux</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">chunk_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_preallocate_and_copy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/_functorch/eager_transforms.py#L428"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.func.jacrev" title="Permalink to this definition">¶</a></dt>
 <dd><p>Computes the Jacobian of <code class="docutils literal notranslate"><span class="pre">func</span></code> with respect to the arg(s) at index
 <code class="docutils literal notranslate"><span class="pre">argnum</span></code> using reverse mode autodiff</p>
 <div class="admonition note">
diff --git a/main/generated/torch.func.jvp.html b/main/generated/torch.func.jvp.html
index 95dd6be6643..9a28c535dad 100644
--- a/main/generated/torch.func.jvp.html
+++ b/main/generated/torch.func.jvp.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>torch.func.jvp<a class="headerlink" href="#torch-func-jvp" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.func.jvp">
-<span class="sig-prename descclassname"><span class="pre">torch.func.</span></span><span class="sig-name descname"><span class="pre">jvp</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">func</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">primals</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tangents</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">strict</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">has_aux</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/_functorch/eager_transforms.py#L983"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.func.jvp" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.func.</span></span><span class="sig-name descname"><span class="pre">jvp</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">func</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">primals</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tangents</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">strict</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">has_aux</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/_functorch/eager_transforms.py#L983"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.func.jvp" title="Permalink to this definition">¶</a></dt>
 <dd><p>Standing for the Jacobian-vector product, returns a tuple containing
 the output of <cite>func(*primals)</cite> and the “Jacobian of <code class="docutils literal notranslate"><span class="pre">func</span></code> evaluated at
 <code class="docutils literal notranslate"><span class="pre">primals</span></code>” times <code class="docutils literal notranslate"><span class="pre">tangents</span></code>. This is also known as forward-mode autodiff.</p>
diff --git a/main/generated/torch.func.linearize.html b/main/generated/torch.func.linearize.html
index 35b60698288..9ddc48aa2a4 100644
--- a/main/generated/torch.func.linearize.html
+++ b/main/generated/torch.func.linearize.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>torch.func.linearize<a class="headerlink" href="#torch-func-linearize" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.func.linearize">
-<span class="sig-prename descclassname"><span class="pre">torch.func.</span></span><span class="sig-name descname"><span class="pre">linearize</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">func</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">primals</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/_functorch/eager_transforms.py#L1678"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.func.linearize" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.func.</span></span><span class="sig-name descname"><span class="pre">linearize</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">func</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">primals</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/_functorch/eager_transforms.py#L1678"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.func.linearize" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns the value of <code class="docutils literal notranslate"><span class="pre">func</span></code> at <code class="docutils literal notranslate"><span class="pre">primals</span></code> and linear approximation
 at <code class="docutils literal notranslate"><span class="pre">primals</span></code>.</p>
 <dl class="field-list simple">
diff --git a/main/generated/torch.func.replace_all_batch_norm_modules_.html b/main/generated/torch.func.replace_all_batch_norm_modules_.html
index 641cc086389..ba0cda66260 100644
--- a/main/generated/torch.func.replace_all_batch_norm_modules_.html
+++ b/main/generated/torch.func.replace_all_batch_norm_modules_.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>torch.func.replace_all_batch_norm_modules_<a class="headerlink" href="#torch-func-replace-all-batch-norm-modules" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.func.replace_all_batch_norm_modules_">
-<span class="sig-prename descclassname"><span class="pre">torch.func.</span></span><span class="sig-name descname"><span class="pre">replace_all_batch_norm_modules_</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">root</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/_functorch/batch_norm_replacement.py#L16"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.func.replace_all_batch_norm_modules_" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.func.</span></span><span class="sig-name descname"><span class="pre">replace_all_batch_norm_modules_</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">root</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/_functorch/batch_norm_replacement.py#L16"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.func.replace_all_batch_norm_modules_" title="Permalink to this definition">¶</a></dt>
 <dd><p>In place updates <code class="xref py py-attr docutils literal notranslate"><span class="pre">root</span></code> by setting the <code class="docutils literal notranslate"><span class="pre">running_mean</span></code> and <code class="docutils literal notranslate"><span class="pre">running_var</span></code> to be None and
 setting track_running_stats to be False for any nn.BatchNorm module in <code class="xref py py-attr docutils literal notranslate"><span class="pre">root</span></code></p>
 <dl class="field-list simple">
diff --git a/main/generated/torch.func.stack_module_state.html b/main/generated/torch.func.stack_module_state.html
index 5ce7999d4a2..de82d906c1a 100644
--- a/main/generated/torch.func.stack_module_state.html
+++ b/main/generated/torch.func.stack_module_state.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>torch.func.stack_module_state<a class="headerlink" href="#torch-func-stack-module-state" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.func.stack_module_state">
-<span class="sig-prename descclassname"><span class="pre">torch.func.</span></span><span class="sig-name descname"><span class="pre">stack_module_state</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">models</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">params</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">buffers</span></span></span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/_functorch/functional_call.py#L158"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.func.stack_module_state" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.func.</span></span><span class="sig-name descname"><span class="pre">stack_module_state</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">models</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">params</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">buffers</span></span></span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/_functorch/functional_call.py#L158"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.func.stack_module_state" title="Permalink to this definition">¶</a></dt>
 <dd><p>Prepares a list of torch.nn.Modules for ensembling with <a class="reference internal" href="torch.func.vmap.html#torch.func.vmap" title="torch.func.vmap"><code class="xref py py-func docutils literal notranslate"><span class="pre">vmap()</span></code></a>.</p>
 <p>Given a list of <code class="docutils literal notranslate"><span class="pre">M</span></code> <code class="docutils literal notranslate"><span class="pre">nn.Modules</span></code> of the same class, returns two dictionaries
 that stack all of their parameters and buffers together, indexed by name.
diff --git a/main/generated/torch.func.vjp.html b/main/generated/torch.func.vjp.html
index 16f0b574f87..4501ba3d9bc 100644
--- a/main/generated/torch.func.vjp.html
+++ b/main/generated/torch.func.vjp.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>torch.func.vjp<a class="headerlink" href="#torch-func-vjp" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.func.vjp">
-<span class="sig-prename descclassname"><span class="pre">torch.func.</span></span><span class="sig-name descname"><span class="pre">vjp</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">func</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">primals</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">has_aux</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/_functorch/eager_transforms.py#L202"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.func.vjp" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.func.</span></span><span class="sig-name descname"><span class="pre">vjp</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">func</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">primals</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">has_aux</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/_functorch/eager_transforms.py#L202"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.func.vjp" title="Permalink to this definition">¶</a></dt>
 <dd><p>Standing for the vector-Jacobian product, returns a tuple containing the
 results of <code class="docutils literal notranslate"><span class="pre">func</span></code> applied to <code class="docutils literal notranslate"><span class="pre">primals</span></code> and a function that, when
 given <code class="docutils literal notranslate"><span class="pre">cotangents</span></code>, computes the reverse-mode Jacobian of <code class="docutils literal notranslate"><span class="pre">func</span></code> with
diff --git a/main/generated/torch.func.vmap.html b/main/generated/torch.func.vmap.html
index d4f4257ce13..3154cb4d4c5 100644
--- a/main/generated/torch.func.vmap.html
+++ b/main/generated/torch.func.vmap.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>torch.func.vmap<a class="headerlink" href="#torch-func-vmap" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.func.vmap">
-<span class="sig-prename descclassname"><span class="pre">torch.func.</span></span><span class="sig-name descname"><span class="pre">vmap</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">func</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">in_dims</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out_dims</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">randomness</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'error'</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">chunk_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/_functorch/apis.py#L30"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.func.vmap" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.func.</span></span><span class="sig-name descname"><span class="pre">vmap</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">func</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">in_dims</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out_dims</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">randomness</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'error'</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">chunk_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/_functorch/apis.py#L30"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.func.vmap" title="Permalink to this definition">¶</a></dt>
 <dd><p>vmap is the vectorizing map; <code class="docutils literal notranslate"><span class="pre">vmap(func)</span></code> returns a new function that
 maps <code class="docutils literal notranslate"><span class="pre">func</span></code> over some dimension of the inputs. Semantically, vmap
 pushes the map into PyTorch operations called by <code class="docutils literal notranslate"><span class="pre">func</span></code>, effectively
diff --git a/main/generated/torch.fx.experimental.proxy_tensor.get_proxy_mode.html b/main/generated/torch.fx.experimental.proxy_tensor.get_proxy_mode.html
index 74c79b7029e..633a0c765e5 100644
--- a/main/generated/torch.fx.experimental.proxy_tensor.get_proxy_mode.html
+++ b/main/generated/torch.fx.experimental.proxy_tensor.get_proxy_mode.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.fx.experimental.proxy_tensor.get_proxy_mode<a class="headerlink" href="#torch-fx-experimental-proxy-tensor-get-proxy-mode" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.fx.experimental.proxy_tensor.get_proxy_mode">
-<span class="sig-prename descclassname"><span class="pre">torch.fx.experimental.proxy_tensor.</span></span><span class="sig-name descname"><span class="pre">get_proxy_mode</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/proxy_tensor.html#get_proxy_mode"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/experimental/proxy_tensor.py#L2209"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.proxy_tensor.get_proxy_mode" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.fx.experimental.proxy_tensor.</span></span><span class="sig-name descname"><span class="pre">get_proxy_mode</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/proxy_tensor.html#get_proxy_mode"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/fx/experimental/proxy_tensor.py#L2209"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.proxy_tensor.get_proxy_mode" title="Permalink to this definition">¶</a></dt>
 <dd><p>Current the currently active proxy tracing mode, or None if
 we are not currently tracing.  This includes pre-dispatch proxy
 tracing.</p>
diff --git a/main/generated/torch.fx.experimental.proxy_tensor.handle_sym_dispatch.html b/main/generated/torch.fx.experimental.proxy_tensor.handle_sym_dispatch.html
index 33ec72b0f06..d2868a38546 100644
--- a/main/generated/torch.fx.experimental.proxy_tensor.handle_sym_dispatch.html
+++ b/main/generated/torch.fx.experimental.proxy_tensor.handle_sym_dispatch.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.fx.experimental.proxy_tensor.handle_sym_dispatch<a class="headerlink" href="#torch-fx-experimental-proxy-tensor-handle-sym-dispatch" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.fx.experimental.proxy_tensor.handle_sym_dispatch">
-<span class="sig-prename descclassname"><span class="pre">torch.fx.experimental.proxy_tensor.</span></span><span class="sig-name descname"><span class="pre">handle_sym_dispatch</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">func</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/proxy_tensor.html#handle_sym_dispatch"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/experimental/proxy_tensor.py#L2225"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.proxy_tensor.handle_sym_dispatch" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.fx.experimental.proxy_tensor.</span></span><span class="sig-name descname"><span class="pre">handle_sym_dispatch</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">func</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/proxy_tensor.html#handle_sym_dispatch"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/fx/experimental/proxy_tensor.py#L2225"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.proxy_tensor.handle_sym_dispatch" title="Permalink to this definition">¶</a></dt>
 <dd><p>Call into the currently active proxy tracing mode to do a
 SymInt/SymFloat/SymBool dispatch trace on a function that operates on
 these arguments.</p>
diff --git a/main/generated/torch.fx.experimental.proxy_tensor.make_fx.html b/main/generated/torch.fx.experimental.proxy_tensor.make_fx.html
index d086d31c39b..eb221d5f865 100644
--- a/main/generated/torch.fx.experimental.proxy_tensor.make_fx.html
+++ b/main/generated/torch.fx.experimental.proxy_tensor.make_fx.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.fx.experimental.proxy_tensor.make_fx<a class="headerlink" href="#torch-fx-experimental-proxy-tensor-make-fx" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.fx.experimental.proxy_tensor.make_fx">
-<span class="sig-prename descclassname"><span class="pre">torch.fx.experimental.proxy_tensor.</span></span><span class="sig-name descname"><span class="pre">make_fx</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">f</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">decomposition_table</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tracing_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'real'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_allow_non_fake_inputs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pre_dispatch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">record_module_stack</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_allow_fake_constant</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_error_on_data_dependent_ops</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/proxy_tensor.html#make_fx"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/experimental/proxy_tensor.py#L2163"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.proxy_tensor.make_fx" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.fx.experimental.proxy_tensor.</span></span><span class="sig-name descname"><span class="pre">make_fx</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">f</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">decomposition_table</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tracing_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'real'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_allow_non_fake_inputs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pre_dispatch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">record_module_stack</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_allow_fake_constant</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_error_on_data_dependent_ops</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/proxy_tensor.html#make_fx"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/fx/experimental/proxy_tensor.py#L2163"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.proxy_tensor.make_fx" title="Permalink to this definition">¶</a></dt>
 <dd><p>Given a function f, return a new function which when executed with valid
 arguments to f, returns an FX GraphModule representing the set of operations that
 were executed during the course of execution.</p>
diff --git a/main/generated/torch.fx.experimental.proxy_tensor.maybe_disable_thunkify.html b/main/generated/torch.fx.experimental.proxy_tensor.maybe_disable_thunkify.html
index 8439b11c3ea..0690c88fce6 100644
--- a/main/generated/torch.fx.experimental.proxy_tensor.maybe_disable_thunkify.html
+++ b/main/generated/torch.fx.experimental.proxy_tensor.maybe_disable_thunkify.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.fx.experimental.proxy_tensor.maybe_disable_thunkify<a class="headerlink" href="#torch-fx-experimental-proxy-tensor-maybe-disable-thunkify" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.fx.experimental.proxy_tensor.maybe_disable_thunkify">
-<span class="sig-prename descclassname"><span class="pre">torch.fx.experimental.proxy_tensor.</span></span><span class="sig-name descname"><span class="pre">maybe_disable_thunkify</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/proxy_tensor.html#maybe_disable_thunkify"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/experimental/proxy_tensor.py#L444"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.proxy_tensor.maybe_disable_thunkify" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.fx.experimental.proxy_tensor.</span></span><span class="sig-name descname"><span class="pre">maybe_disable_thunkify</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/proxy_tensor.html#maybe_disable_thunkify"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/fx/experimental/proxy_tensor.py#L444"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.proxy_tensor.maybe_disable_thunkify" title="Permalink to this definition">¶</a></dt>
 <dd><p>Within a context, disable thunkification.  See <a class="reference internal" href="torch.fx.experimental.proxy_tensor.maybe_enable_thunkify.html#torch.fx.experimental.proxy_tensor.maybe_enable_thunkify" title="torch.fx.experimental.proxy_tensor.maybe_enable_thunkify"><code class="xref py py-func docutils literal notranslate"><span class="pre">maybe_enable_thunkify()</span></code></a>
 for more details.  This is helpful if you have a wrapper function which
 you want to enable thunkification on, but in some segment on the inside (say,
diff --git a/main/generated/torch.fx.experimental.proxy_tensor.maybe_enable_thunkify.html b/main/generated/torch.fx.experimental.proxy_tensor.maybe_enable_thunkify.html
index a2feb933b52..4351ad7a843 100644
--- a/main/generated/torch.fx.experimental.proxy_tensor.maybe_enable_thunkify.html
+++ b/main/generated/torch.fx.experimental.proxy_tensor.maybe_enable_thunkify.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.fx.experimental.proxy_tensor.maybe_enable_thunkify<a class="headerlink" href="#torch-fx-experimental-proxy-tensor-maybe-enable-thunkify" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.fx.experimental.proxy_tensor.maybe_enable_thunkify">
-<span class="sig-prename descclassname"><span class="pre">torch.fx.experimental.proxy_tensor.</span></span><span class="sig-name descname"><span class="pre">maybe_enable_thunkify</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/proxy_tensor.html#maybe_enable_thunkify"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/experimental/proxy_tensor.py#L460"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.proxy_tensor.maybe_enable_thunkify" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.fx.experimental.proxy_tensor.</span></span><span class="sig-name descname"><span class="pre">maybe_enable_thunkify</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/proxy_tensor.html#maybe_enable_thunkify"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/fx/experimental/proxy_tensor.py#L460"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.proxy_tensor.maybe_enable_thunkify" title="Permalink to this definition">¶</a></dt>
 <dd><p>Within this context manager, if you are doing make_fx tracing, we will thunkify
 all SymNode compute and avoid tracing it into the graph unless it is actually needed.
 You should prefer to avoid using this as much as possible, as lazy evaluation of
diff --git a/main/generated/torch.fx.experimental.symbolic_shapes.CallMethodKey.html b/main/generated/torch.fx.experimental.symbolic_shapes.CallMethodKey.html
index d927caa5221..e679f9f88f1 100644
--- a/main/generated/torch.fx.experimental.symbolic_shapes.CallMethodKey.html
+++ b/main/generated/torch.fx.experimental.symbolic_shapes.CallMethodKey.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,12 +612,12 @@
 <h1>CallMethodKey<a class="headerlink" href="#callmethodkey" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.fx.experimental.symbolic_shapes.CallMethodKey">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.fx.experimental.symbolic_shapes.</span></span><span class="sig-name descname"><span class="pre">CallMethodKey</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="s"><span class="pre">'str'</span></span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#CallMethodKey"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/experimental/symbolic_shapes.py#L902"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.CallMethodKey" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.fx.experimental.symbolic_shapes.</span></span><span class="sig-name descname"><span class="pre">CallMethodKey</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="s"><span class="pre">'str'</span></span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#CallMethodKey"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/fx/experimental/symbolic_shapes.py#L902"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.CallMethodKey" title="Permalink to this definition">¶</a></dt>
 <dd><dl class="field-list simple">
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.experimental.symbolic_shapes.CallMethodKey.get">
-<span class="sig-name descname"><span class="pre">get</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">o</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#CallMethodKey.get"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/experimental/symbolic_shapes.py#L909"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.CallMethodKey.get" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">get</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">o</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#CallMethodKey.get"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/fx/experimental/symbolic_shapes.py#L909"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.CallMethodKey.get" title="Permalink to this definition">¶</a></dt>
 <dd><p>Call the method on object</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
diff --git a/main/generated/torch.fx.experimental.symbolic_shapes.ConvertIntKey.html b/main/generated/torch.fx.experimental.symbolic_shapes.ConvertIntKey.html
index 463e1836997..32af8d7cc3a 100644
--- a/main/generated/torch.fx.experimental.symbolic_shapes.ConvertIntKey.html
+++ b/main/generated/torch.fx.experimental.symbolic_shapes.ConvertIntKey.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,12 +612,12 @@
 <h1>ConvertIntKey<a class="headerlink" href="#convertintkey" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.fx.experimental.symbolic_shapes.ConvertIntKey">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.fx.experimental.symbolic_shapes.</span></span><span class="sig-name descname"><span class="pre">ConvertIntKey</span></span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#ConvertIntKey"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/experimental/symbolic_shapes.py#L892"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.ConvertIntKey" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.fx.experimental.symbolic_shapes.</span></span><span class="sig-name descname"><span class="pre">ConvertIntKey</span></span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#ConvertIntKey"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/fx/experimental/symbolic_shapes.py#L892"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.ConvertIntKey" title="Permalink to this definition">¶</a></dt>
 <dd><dl class="field-list simple">
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.experimental.symbolic_shapes.ConvertIntKey.get">
-<span class="sig-name descname"><span class="pre">get</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">b</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#ConvertIntKey.get"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/experimental/symbolic_shapes.py#L897"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.ConvertIntKey.get" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">get</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">b</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#ConvertIntKey.get"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/fx/experimental/symbolic_shapes.py#L897"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.ConvertIntKey.get" title="Permalink to this definition">¶</a></dt>
 <dd><p>Get the int value from bool</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
diff --git a/main/generated/torch.fx.experimental.symbolic_shapes.DimConstraints.html b/main/generated/torch.fx.experimental.symbolic_shapes.DimConstraints.html
index 7083c0c7f4a..4453aa0f6f3 100644
--- a/main/generated/torch.fx.experimental.symbolic_shapes.DimConstraints.html
+++ b/main/generated/torch.fx.experimental.symbolic_shapes.DimConstraints.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,14 +612,14 @@
 <h1>DimConstraints<a class="headerlink" href="#dimconstraints" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.fx.experimental.symbolic_shapes.DimConstraints">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.fx.experimental.symbolic_shapes.</span></span><span class="sig-name descname"><span class="pre">DimConstraints</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">symbol_to_source</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">var_to_val</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">marked_dynamic</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">source_name_to_debug_name</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#DimConstraints"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/experimental/symbolic_shapes.py#L2322"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.DimConstraints" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.fx.experimental.symbolic_shapes.</span></span><span class="sig-name descname"><span class="pre">DimConstraints</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">symbol_to_source</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">var_to_val</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">marked_dynamic</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">source_name_to_debug_name</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#DimConstraints"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/fx/experimental/symbolic_shapes.py#L2322"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.DimConstraints" title="Permalink to this definition">¶</a></dt>
 <dd><p>Custom solver for a system of constraints on symbolic dimensions.
 Solutions are “static” values or simplified “dynamic” constraints.</p>
 <dl class="field-list simple">
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.experimental.symbolic_shapes.DimConstraints.add">
-<span class="sig-name descname"><span class="pre">add</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">expr</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#DimConstraints.add"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/experimental/symbolic_shapes.py#L2480"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.DimConstraints.add" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">add</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">expr</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#DimConstraints.add"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/fx/experimental/symbolic_shapes.py#L2480"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.DimConstraints.add" title="Permalink to this definition">¶</a></dt>
 <dd><p>Add an expression to the set of constraints.</p>
 <p>Return whether the expression is a trivial constraint (i.e., an obvious tautology).</p>
 <dl class="field-list simple">
@@ -631,7 +631,7 @@ <h1>DimConstraints<a class="headerlink" href="#dimconstraints" title="Permalink
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.experimental.symbolic_shapes.DimConstraints.add_equality">
-<span class="sig-name descname"><span class="pre">add_equality</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">source</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">expr</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#DimConstraints.add_equality"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/experimental/symbolic_shapes.py#L2525"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.DimConstraints.add_equality" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">add_equality</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">source</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">expr</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#DimConstraints.add_equality"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/fx/experimental/symbolic_shapes.py#L2525"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.DimConstraints.add_equality" title="Permalink to this definition">¶</a></dt>
 <dd><p>Add an equality constraint</p>
 <dl class="field-list simple">
 </dl>
@@ -639,7 +639,7 @@ <h1>DimConstraints<a class="headerlink" href="#dimconstraints" title="Permalink
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.experimental.symbolic_shapes.DimConstraints.forced_specializations">
-<span class="sig-name descname"><span class="pre">forced_specializations</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#DimConstraints.forced_specializations"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/experimental/symbolic_shapes.py#L2696"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.DimConstraints.forced_specializations" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">forced_specializations</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#DimConstraints.forced_specializations"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/fx/experimental/symbolic_shapes.py#L2696"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.DimConstraints.forced_specializations" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns a dictionary of the names of symbols to their specialized value</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -650,7 +650,7 @@ <h1>DimConstraints<a class="headerlink" href="#dimconstraints" title="Permalink
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.experimental.symbolic_shapes.DimConstraints.prettify_results">
-<span class="sig-name descname"><span class="pre">prettify_results</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">original_signature</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dynamic_shapes</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">constraint_violation_error</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">forced_specializations</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#DimConstraints.prettify_results"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/experimental/symbolic_shapes.py#L2903"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.DimConstraints.prettify_results" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">prettify_results</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">original_signature</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dynamic_shapes</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">constraint_violation_error</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">forced_specializations</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#DimConstraints.prettify_results"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/fx/experimental/symbolic_shapes.py#L2903"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.DimConstraints.prettify_results" title="Permalink to this definition">¶</a></dt>
 <dd><p>Format a message for constraint violation erros</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -661,7 +661,7 @@ <h1>DimConstraints<a class="headerlink" href="#dimconstraints" title="Permalink
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.experimental.symbolic_shapes.DimConstraints.rewrite_with_congruences">
-<span class="sig-name descname"><span class="pre">rewrite_with_congruences</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">s</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">expr</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#DimConstraints.rewrite_with_congruences"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/experimental/symbolic_shapes.py#L2389"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.DimConstraints.rewrite_with_congruences" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">rewrite_with_congruences</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">s</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">expr</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#DimConstraints.rewrite_with_congruences"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/fx/experimental/symbolic_shapes.py#L2389"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.DimConstraints.rewrite_with_congruences" title="Permalink to this definition">¶</a></dt>
 <dd><p>Eliminate expressions of the form b // d and b % d while adding congruences of the form b % d == k.
 This leaves rational operators (in particular of the form b / d) that our inequality solver can handle.
 We solve the added congruences separately (using our congruence solver, see below).</p>
@@ -674,7 +674,7 @@ <h1>DimConstraints<a class="headerlink" href="#dimconstraints" title="Permalink
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.experimental.symbolic_shapes.DimConstraints.solve">
-<span class="sig-name descname"><span class="pre">solve</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#DimConstraints.solve"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/experimental/symbolic_shapes.py#L2588"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.DimConstraints.solve" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">solve</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#DimConstraints.solve"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/fx/experimental/symbolic_shapes.py#L2588"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.DimConstraints.solve" title="Permalink to this definition">¶</a></dt>
 <dd><p>Solve the system of constraint equations to find simplified constraints</p>
 <dl class="field-list simple">
 </dl>
diff --git a/main/generated/torch.fx.experimental.symbolic_shapes.DimDynamic.html b/main/generated/torch.fx.experimental.symbolic_shapes.DimDynamic.html
index f1ed62ae37b..e42b5e6ef08 100644
--- a/main/generated/torch.fx.experimental.symbolic_shapes.DimDynamic.html
+++ b/main/generated/torch.fx.experimental.symbolic_shapes.DimDynamic.html
@@ -300,7 +300,7 @@
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+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>DimDynamic<a class="headerlink" href="#dimdynamic" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.fx.experimental.symbolic_shapes.DimDynamic">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.fx.experimental.symbolic_shapes.</span></span><span class="sig-name descname"><span class="pre">DimDynamic</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#DimDynamic"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/experimental/symbolic_shapes.py#L1473"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.DimDynamic" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.fx.experimental.symbolic_shapes.</span></span><span class="sig-name descname"><span class="pre">DimDynamic</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#DimDynamic"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/fx/experimental/symbolic_shapes.py#L1473"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.DimDynamic" title="Permalink to this definition">¶</a></dt>
 <dd><p>Controls how to perform symbol allocation for a dimension.  It is always
 sound to default this to DYNAMIC, but the policies DUCK and STATIC can
 result in better trace-time and compile-time performance, as they reduce
diff --git a/main/generated/torch.fx.experimental.symbolic_shapes.DivideByKey.html b/main/generated/torch.fx.experimental.symbolic_shapes.DivideByKey.html
index d89706a9610..17719df2b55 100644
--- a/main/generated/torch.fx.experimental.symbolic_shapes.DivideByKey.html
+++ b/main/generated/torch.fx.experimental.symbolic_shapes.DivideByKey.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,12 +612,12 @@
 <h1>DivideByKey<a class="headerlink" href="#dividebykey" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.fx.experimental.symbolic_shapes.DivideByKey">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.fx.experimental.symbolic_shapes.</span></span><span class="sig-name descname"><span class="pre">DivideByKey</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">divisor</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="s"><span class="pre">'Union[int,</span> <span class="pre">SymInt]'</span></span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#DivideByKey"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/experimental/symbolic_shapes.py#L926"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.DivideByKey" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.fx.experimental.symbolic_shapes.</span></span><span class="sig-name descname"><span class="pre">DivideByKey</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">divisor</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="s"><span class="pre">'Union[int,</span> <span class="pre">SymInt]'</span></span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#DivideByKey"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/fx/experimental/symbolic_shapes.py#L926"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.DivideByKey" title="Permalink to this definition">¶</a></dt>
 <dd><dl class="field-list simple">
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.experimental.symbolic_shapes.DivideByKey.get">
-<span class="sig-name descname"><span class="pre">get</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">o</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#DivideByKey.get"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/experimental/symbolic_shapes.py#L933"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.DivideByKey.get" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">get</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">o</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#DivideByKey.get"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/fx/experimental/symbolic_shapes.py#L933"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.DivideByKey.get" title="Permalink to this definition">¶</a></dt>
 <dd><p>Divide object by divisor</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
diff --git a/main/generated/torch.fx.experimental.symbolic_shapes.EqualityConstraint.html b/main/generated/torch.fx.experimental.symbolic_shapes.EqualityConstraint.html
index 12ef1bf7350..0c2ef2f3a0d 100644
--- a/main/generated/torch.fx.experimental.symbolic_shapes.EqualityConstraint.html
+++ b/main/generated/torch.fx.experimental.symbolic_shapes.EqualityConstraint.html
@@ -300,7 +300,7 @@
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     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>EqualityConstraint<a class="headerlink" href="#equalityconstraint" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.fx.experimental.symbolic_shapes.EqualityConstraint">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.fx.experimental.symbolic_shapes.</span></span><span class="sig-name descname"><span class="pre">EqualityConstraint</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">warn_only</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">source_pairs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">derived_equalities</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">phantom_symbols</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">relaxed_sources</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#EqualityConstraint"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/experimental/symbolic_shapes.py#L1591"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.EqualityConstraint" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.fx.experimental.symbolic_shapes.</span></span><span class="sig-name descname"><span class="pre">EqualityConstraint</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">warn_only</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">source_pairs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">derived_equalities</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">phantom_symbols</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">relaxed_sources</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#EqualityConstraint"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/fx/experimental/symbolic_shapes.py#L1591"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.EqualityConstraint" title="Permalink to this definition">¶</a></dt>
 <dd><p>Represent and decide various kinds of equality constraints between input sources.</p>
 <p>A “source pair” is a pair of input sources for dynamic dimensions that
 are specified equal. We represent <cite>source_pairs</cite> in a union-find forest
diff --git a/main/generated/torch.fx.experimental.symbolic_shapes.InnerTensorKey.html b/main/generated/torch.fx.experimental.symbolic_shapes.InnerTensorKey.html
index bd56040d01d..44d2cee8184 100644
--- a/main/generated/torch.fx.experimental.symbolic_shapes.InnerTensorKey.html
+++ b/main/generated/torch.fx.experimental.symbolic_shapes.InnerTensorKey.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,12 +612,12 @@
 <h1>InnerTensorKey<a class="headerlink" href="#innertensorkey" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.fx.experimental.symbolic_shapes.InnerTensorKey">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.fx.experimental.symbolic_shapes.</span></span><span class="sig-name descname"><span class="pre">InnerTensorKey</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">inner_name</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="s"><span class="pre">'str'</span></span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#InnerTensorKey"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/experimental/symbolic_shapes.py#L914"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.InnerTensorKey" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.fx.experimental.symbolic_shapes.</span></span><span class="sig-name descname"><span class="pre">InnerTensorKey</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">inner_name</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="s"><span class="pre">'str'</span></span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#InnerTensorKey"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/fx/experimental/symbolic_shapes.py#L914"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.InnerTensorKey" title="Permalink to this definition">¶</a></dt>
 <dd><dl class="field-list simple">
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.experimental.symbolic_shapes.InnerTensorKey.get">
-<span class="sig-name descname"><span class="pre">get</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">o</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#InnerTensorKey.get"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/experimental/symbolic_shapes.py#L921"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.InnerTensorKey.get" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">get</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">o</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#InnerTensorKey.get"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/fx/experimental/symbolic_shapes.py#L921"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.InnerTensorKey.get" title="Permalink to this definition">¶</a></dt>
 <dd><p>Get the inner tensor attribute</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
diff --git a/main/generated/torch.fx.experimental.symbolic_shapes.PropagateUnbackedSymInts.html b/main/generated/torch.fx.experimental.symbolic_shapes.PropagateUnbackedSymInts.html
index 1c77ec8133a..c07fcd63541 100644
--- a/main/generated/torch.fx.experimental.symbolic_shapes.PropagateUnbackedSymInts.html
+++ b/main/generated/torch.fx.experimental.symbolic_shapes.PropagateUnbackedSymInts.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,12 +612,12 @@
 <h1>PropagateUnbackedSymInts<a class="headerlink" href="#propagateunbackedsymints" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.fx.experimental.symbolic_shapes.PropagateUnbackedSymInts">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.fx.experimental.symbolic_shapes.</span></span><span class="sig-name descname"><span class="pre">PropagateUnbackedSymInts</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">garbage_collect_values</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">graph</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#PropagateUnbackedSymInts"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/experimental/symbolic_shapes.py#L6937"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.PropagateUnbackedSymInts" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.fx.experimental.symbolic_shapes.</span></span><span class="sig-name descname"><span class="pre">PropagateUnbackedSymInts</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">garbage_collect_values</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">graph</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#PropagateUnbackedSymInts"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/fx/experimental/symbolic_shapes.py#L6937"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.PropagateUnbackedSymInts" title="Permalink to this definition">¶</a></dt>
 <dd><dl class="field-list simple">
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.experimental.symbolic_shapes.PropagateUnbackedSymInts.boxed_run">
-<span class="sig-name descname"><span class="pre">boxed_run</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">args_list</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/interpreter.py#L194"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.PropagateUnbackedSymInts.boxed_run" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">boxed_run</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">args_list</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/fx/interpreter.py#L194"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.PropagateUnbackedSymInts.boxed_run" title="Permalink to this definition">¶</a></dt>
 <dd><p>Run <cite>module</cite> via interpretation and return the result.  This uses the “boxed”
 calling convention, where you pass a list of arguments, which will be cleared
 by the interpreter.  This ensures that input tensors are promptly deallocated.</p>
@@ -629,7 +629,7 @@ <h1>PropagateUnbackedSymInts<a class="headerlink" href="#propagateunbackedsymint
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.experimental.symbolic_shapes.PropagateUnbackedSymInts.call_function">
-<span class="sig-name descname"><span class="pre">call_function</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">target</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/interpreter.py#L294"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.PropagateUnbackedSymInts.call_function" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">call_function</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">target</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/fx/interpreter.py#L294"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.PropagateUnbackedSymInts.call_function" title="Permalink to this definition">¶</a></dt>
 <dd><p>Execute a <code class="docutils literal notranslate"><span class="pre">call_function</span></code> node and return the result.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -657,7 +657,7 @@ <h1>PropagateUnbackedSymInts<a class="headerlink" href="#propagateunbackedsymint
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.experimental.symbolic_shapes.PropagateUnbackedSymInts.call_method">
-<span class="sig-name descname"><span class="pre">call_method</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">target</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/interpreter.py#L316"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.PropagateUnbackedSymInts.call_method" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">call_method</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">target</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/fx/interpreter.py#L316"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.PropagateUnbackedSymInts.call_method" title="Permalink to this definition">¶</a></dt>
 <dd><p>Execute a <code class="docutils literal notranslate"><span class="pre">call_method</span></code> node and return the result.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -685,7 +685,7 @@ <h1>PropagateUnbackedSymInts<a class="headerlink" href="#propagateunbackedsymint
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.experimental.symbolic_shapes.PropagateUnbackedSymInts.call_module">
-<span class="sig-name descname"><span class="pre">call_module</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">target</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/interpreter.py#L340"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.PropagateUnbackedSymInts.call_module" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">call_module</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">target</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/fx/interpreter.py#L340"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.PropagateUnbackedSymInts.call_module" title="Permalink to this definition">¶</a></dt>
 <dd><p>Execute a <code class="docutils literal notranslate"><span class="pre">call_module</span></code> node and return the result.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -713,7 +713,7 @@ <h1>PropagateUnbackedSymInts<a class="headerlink" href="#propagateunbackedsymint
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.experimental.symbolic_shapes.PropagateUnbackedSymInts.fetch_args_kwargs_from_env">
-<span class="sig-name descname"><span class="pre">fetch_args_kwargs_from_env</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/interpreter.py#L407"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.PropagateUnbackedSymInts.fetch_args_kwargs_from_env" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">fetch_args_kwargs_from_env</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/fx/interpreter.py#L407"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.PropagateUnbackedSymInts.fetch_args_kwargs_from_env" title="Permalink to this definition">¶</a></dt>
 <dd><p>Fetch the concrete values of <code class="docutils literal notranslate"><span class="pre">args</span></code> and <code class="docutils literal notranslate"><span class="pre">kwargs</span></code> of node <code class="docutils literal notranslate"><span class="pre">n</span></code>
 from the current execution environment.</p>
 <dl class="field-list simple">
@@ -735,7 +735,7 @@ <h1>PropagateUnbackedSymInts<a class="headerlink" href="#propagateunbackedsymint
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.experimental.symbolic_shapes.PropagateUnbackedSymInts.fetch_attr">
-<span class="sig-name descname"><span class="pre">fetch_attr</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">target</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/interpreter.py#L386"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.PropagateUnbackedSymInts.fetch_attr" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">fetch_attr</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">target</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/fx/interpreter.py#L386"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.PropagateUnbackedSymInts.fetch_attr" title="Permalink to this definition">¶</a></dt>
 <dd><p>Fetch an attribute from the <code class="docutils literal notranslate"><span class="pre">Module</span></code> hierarchy of <code class="docutils literal notranslate"><span class="pre">self.module</span></code>.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -756,7 +756,7 @@ <h1>PropagateUnbackedSymInts<a class="headerlink" href="#propagateunbackedsymint
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.experimental.symbolic_shapes.PropagateUnbackedSymInts.get_attr">
-<span class="sig-name descname"><span class="pre">get_attr</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">target</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/interpreter.py#L273"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.PropagateUnbackedSymInts.get_attr" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">get_attr</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">target</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/fx/interpreter.py#L273"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.PropagateUnbackedSymInts.get_attr" title="Permalink to this definition">¶</a></dt>
 <dd><p>Execute a <code class="docutils literal notranslate"><span class="pre">get_attr</span></code> node. Will retrieve an attribute
 value from the <code class="docutils literal notranslate"><span class="pre">Module</span></code> hierarchy of <code class="docutils literal notranslate"><span class="pre">self.module</span></code>.</p>
 <dl class="field-list simple">
@@ -784,7 +784,7 @@ <h1>PropagateUnbackedSymInts<a class="headerlink" href="#propagateunbackedsymint
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.experimental.symbolic_shapes.PropagateUnbackedSymInts.map_nodes_to_values">
-<span class="sig-name descname"><span class="pre">map_nodes_to_values</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/interpreter.py#L425"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.PropagateUnbackedSymInts.map_nodes_to_values" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">map_nodes_to_values</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/fx/interpreter.py#L425"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.PropagateUnbackedSymInts.map_nodes_to_values" title="Permalink to this definition">¶</a></dt>
 <dd><p>Recursively descend through <code class="docutils literal notranslate"><span class="pre">args</span></code> and look up the concrete value
 for each <code class="docutils literal notranslate"><span class="pre">Node</span></code> in the current execution environment.</p>
 <dl class="field-list simple">
@@ -806,7 +806,7 @@ <h1>PropagateUnbackedSymInts<a class="headerlink" href="#propagateunbackedsymint
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.experimental.symbolic_shapes.PropagateUnbackedSymInts.output">
-<span class="sig-name descname"><span class="pre">output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">target</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/interpreter.py#L365"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.PropagateUnbackedSymInts.output" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">output</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">target</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/fx/interpreter.py#L365"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.PropagateUnbackedSymInts.output" title="Permalink to this definition">¶</a></dt>
 <dd><p>Execute an <code class="docutils literal notranslate"><span class="pre">output</span></code> node. This really just retrieves
 the value referenced by the <code class="docutils literal notranslate"><span class="pre">output</span></code> node and returns it.</p>
 <dl class="field-list simple">
@@ -834,7 +834,7 @@ <h1>PropagateUnbackedSymInts<a class="headerlink" href="#propagateunbackedsymint
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.experimental.symbolic_shapes.PropagateUnbackedSymInts.placeholder">
-<span class="sig-name descname"><span class="pre">placeholder</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">target</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/interpreter.py#L237"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.PropagateUnbackedSymInts.placeholder" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">placeholder</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">target</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/fx/interpreter.py#L237"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.PropagateUnbackedSymInts.placeholder" title="Permalink to this definition">¶</a></dt>
 <dd><p>Execute a <code class="docutils literal notranslate"><span class="pre">placeholder</span></code> node. Note that this is stateful:
 <code class="docutils literal notranslate"><span class="pre">Interpreter</span></code> maintains an internal iterator over
 arguments passed to <code class="docutils literal notranslate"><span class="pre">run</span></code> and this method returns
@@ -864,7 +864,7 @@ <h1>PropagateUnbackedSymInts<a class="headerlink" href="#propagateunbackedsymint
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.experimental.symbolic_shapes.PropagateUnbackedSymInts.run">
-<span class="sig-name descname"><span class="pre">run</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">initial_env</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">enable_io_processing</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/interpreter.py#L121"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.PropagateUnbackedSymInts.run" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">run</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">initial_env</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">enable_io_processing</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/fx/interpreter.py#L121"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.PropagateUnbackedSymInts.run" title="Permalink to this definition">¶</a></dt>
 <dd><p>Run <cite>module</cite> via interpretation and return the result.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -893,7 +893,7 @@ <h1>PropagateUnbackedSymInts<a class="headerlink" href="#propagateunbackedsymint
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.experimental.symbolic_shapes.PropagateUnbackedSymInts.run_node">
-<span class="sig-name descname"><span class="pre">run_node</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#PropagateUnbackedSymInts.run_node"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/experimental/symbolic_shapes.py#L6938"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.PropagateUnbackedSymInts.run_node" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">run_node</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#PropagateUnbackedSymInts.run_node"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/fx/experimental/symbolic_shapes.py#L6938"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.PropagateUnbackedSymInts.run_node" title="Permalink to this definition">¶</a></dt>
 <dd><p>Run an FX node, propagating unbacked Symbol bindings to the new fake tensor</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
diff --git a/main/generated/torch.fx.experimental.symbolic_shapes.RelaxedUnspecConstraint.html b/main/generated/torch.fx.experimental.symbolic_shapes.RelaxedUnspecConstraint.html
index 42842457289..85268efa191 100644
--- a/main/generated/torch.fx.experimental.symbolic_shapes.RelaxedUnspecConstraint.html
+++ b/main/generated/torch.fx.experimental.symbolic_shapes.RelaxedUnspecConstraint.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>RelaxedUnspecConstraint<a class="headerlink" href="#relaxedunspecconstraint" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.fx.experimental.symbolic_shapes.RelaxedUnspecConstraint">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.fx.experimental.symbolic_shapes.</span></span><span class="sig-name descname"><span class="pre">RelaxedUnspecConstraint</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">warn_only</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#RelaxedUnspecConstraint"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/experimental/symbolic_shapes.py#L1560"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.RelaxedUnspecConstraint" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.fx.experimental.symbolic_shapes.</span></span><span class="sig-name descname"><span class="pre">RelaxedUnspecConstraint</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">warn_only</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#RelaxedUnspecConstraint"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/fx/experimental/symbolic_shapes.py#L1560"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.RelaxedUnspecConstraint" title="Permalink to this definition">¶</a></dt>
 <dd><p>For clients: no explicit constraint; constraint is whatever is implicitly
 inferred by guards from tracing.</p>
 <p>For backends: there must exist at least TWO possible values for the
diff --git a/main/generated/torch.fx.experimental.symbolic_shapes.ShapeEnv.html b/main/generated/torch.fx.experimental.symbolic_shapes.ShapeEnv.html
index bd5cf39c823..204756b8383 100644
--- a/main/generated/torch.fx.experimental.symbolic_shapes.ShapeEnv.html
+++ b/main/generated/torch.fx.experimental.symbolic_shapes.ShapeEnv.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,12 +612,12 @@
 <h1>ShapeEnv<a class="headerlink" href="#shapeenv" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.fx.experimental.symbolic_shapes.ShapeEnv">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.fx.experimental.symbolic_shapes.</span></span><span class="sig-name descname"><span class="pre">ShapeEnv</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">should_record_events</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tracked_fakes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#ShapeEnv"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/experimental/symbolic_shapes.py#L3081"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.ShapeEnv" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.fx.experimental.symbolic_shapes.</span></span><span class="sig-name descname"><span class="pre">ShapeEnv</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">should_record_events</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tracked_fakes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#ShapeEnv"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/fx/experimental/symbolic_shapes.py#L3081"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.ShapeEnv" title="Permalink to this definition">¶</a></dt>
 <dd><dl class="field-list simple">
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.experimental.symbolic_shapes.ShapeEnv.add_var_to_val">
-<span class="sig-name descname"><span class="pre">add_var_to_val</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">expr</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">val</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#ShapeEnv.add_var_to_val"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/experimental/symbolic_shapes.py#L4595"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.ShapeEnv.add_var_to_val" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">add_var_to_val</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">expr</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">val</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#ShapeEnv.add_var_to_val"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/fx/experimental/symbolic_shapes.py#L4595"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.ShapeEnv.add_var_to_val" title="Permalink to this definition">¶</a></dt>
 <dd><p>Adds a new symbol to the symbolic environment.</p>
 <dl class="field-list simple">
 </dl>
@@ -625,7 +625,7 @@ <h1>ShapeEnv<a class="headerlink" href="#shapeenv" title="Permalink to this head
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.experimental.symbolic_shapes.ShapeEnv.bind_symbols">
-<span class="sig-name descname"><span class="pre">bind_symbols</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">placeholders</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#ShapeEnv.bind_symbols"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/experimental/symbolic_shapes.py#L5477"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.ShapeEnv.bind_symbols" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">bind_symbols</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">placeholders</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#ShapeEnv.bind_symbols"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/fx/experimental/symbolic_shapes.py#L5477"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.ShapeEnv.bind_symbols" title="Permalink to this definition">¶</a></dt>
 <dd><p>Given a paired list of placeholders (fake tensors with
 symbolic sizes) and concrete arguments (regular tensors
 with real sizes), returns a dictionary mapping each
@@ -648,7 +648,7 @@ <h1>ShapeEnv<a class="headerlink" href="#shapeenv" title="Permalink to this head
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.experimental.symbolic_shapes.ShapeEnv.bound_sympy">
-<span class="sig-name descname"><span class="pre">bound_sympy</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">expr</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">size_oblivious</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#ShapeEnv.bound_sympy"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/experimental/symbolic_shapes.py#L5545"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.ShapeEnv.bound_sympy" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">bound_sympy</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">expr</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">size_oblivious</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#ShapeEnv.bound_sympy"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/fx/experimental/symbolic_shapes.py#L5545"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.ShapeEnv.bound_sympy" title="Permalink to this definition">¶</a></dt>
 <dd><p>Given a sympy expression, computes a ValueRanges bound for what values it can be</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -659,7 +659,7 @@ <h1>ShapeEnv<a class="headerlink" href="#shapeenv" title="Permalink to this head
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.experimental.symbolic_shapes.ShapeEnv.check_equal">
-<span class="sig-name descname"><span class="pre">check_equal</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">other</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#ShapeEnv.check_equal"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/experimental/symbolic_shapes.py#L3414"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.ShapeEnv.check_equal" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">check_equal</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">other</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#ShapeEnv.check_equal"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/fx/experimental/symbolic_shapes.py#L3414"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.ShapeEnv.check_equal" title="Permalink to this definition">¶</a></dt>
 <dd><p>Compare another ShapeEnv for equivalence</p>
 <dl class="field-list simple">
 </dl>
@@ -667,7 +667,7 @@ <h1>ShapeEnv<a class="headerlink" href="#shapeenv" title="Permalink to this head
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.experimental.symbolic_shapes.ShapeEnv.cleanup">
-<span class="sig-name descname"><span class="pre">cleanup</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#ShapeEnv.cleanup"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/experimental/symbolic_shapes.py#L6746"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.ShapeEnv.cleanup" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">cleanup</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#ShapeEnv.cleanup"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/fx/experimental/symbolic_shapes.py#L6746"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.ShapeEnv.cleanup" title="Permalink to this definition">¶</a></dt>
 <dd><p>Break reference cycles.</p>
 <p>This destroys the stacks. If you really want to keep them, we
 just need some way to break references on code objects.</p>
@@ -677,7 +677,7 @@ <h1>ShapeEnv<a class="headerlink" href="#shapeenv" title="Permalink to this head
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.experimental.symbolic_shapes.ShapeEnv.create_symbol">
-<span class="sig-name descname"><span class="pre">create_symbol</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">val</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">source</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dynamic_dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">DimDynamic.DUCK</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">constraint_dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">positive</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">do_not_specialize_zero_one</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">symbolic_context</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#ShapeEnv.create_symbol"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/experimental/symbolic_shapes.py#L4344"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.ShapeEnv.create_symbol" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">create_symbol</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">val</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">source</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dynamic_dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">DimDynamic.DUCK</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">constraint_dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">positive</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">do_not_specialize_zero_one</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">symbolic_context</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#ShapeEnv.create_symbol"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/fx/experimental/symbolic_shapes.py#L4344"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.ShapeEnv.create_symbol" title="Permalink to this definition">¶</a></dt>
 <dd><p>Create a new symbol which is tracked by this ShapeEnv</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -688,7 +688,7 @@ <h1>ShapeEnv<a class="headerlink" href="#shapeenv" title="Permalink to this head
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.experimental.symbolic_shapes.ShapeEnv.create_symbolic_sizes_strides_storage_offset">
-<span class="sig-name descname"><span class="pre">create_symbolic_sizes_strides_storage_offset</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">ex</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">source</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">symbolic_context</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#ShapeEnv.create_symbolic_sizes_strides_storage_offset"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/experimental/symbolic_shapes.py#L3847"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.ShapeEnv.create_symbolic_sizes_strides_storage_offset" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">create_symbolic_sizes_strides_storage_offset</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">ex</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">source</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">symbolic_context</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#ShapeEnv.create_symbolic_sizes_strides_storage_offset"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/fx/experimental/symbolic_shapes.py#L3847"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.ShapeEnv.create_symbolic_sizes_strides_storage_offset" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns a list of symbolic sizes and strides for the given tensor.
 We try our best to express stride in terms of the sizes, so as to not
 introduce new symbolic variables.</p>
@@ -701,7 +701,7 @@ <h1>ShapeEnv<a class="headerlink" href="#shapeenv" title="Permalink to this head
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.experimental.symbolic_shapes.ShapeEnv.create_symboolnode">
-<span class="sig-name descname"><span class="pre">create_symboolnode</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sym</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#ShapeEnv.create_symboolnode"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/experimental/symbolic_shapes.py#L4201"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.ShapeEnv.create_symboolnode" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">create_symboolnode</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sym</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#ShapeEnv.create_symboolnode"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/fx/experimental/symbolic_shapes.py#L4201"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.ShapeEnv.create_symboolnode" title="Permalink to this definition">¶</a></dt>
 <dd><p>Create a SymBool object from a sympy boolean expression</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -712,7 +712,7 @@ <h1>ShapeEnv<a class="headerlink" href="#shapeenv" title="Permalink to this head
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.experimental.symbolic_shapes.ShapeEnv.create_symfloatnode">
-<span class="sig-name descname"><span class="pre">create_symfloatnode</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sym</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">hint</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">source</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#ShapeEnv.create_symfloatnode"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/experimental/symbolic_shapes.py#L4149"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.ShapeEnv.create_symfloatnode" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">create_symfloatnode</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sym</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">hint</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">source</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#ShapeEnv.create_symfloatnode"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/fx/experimental/symbolic_shapes.py#L4149"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.ShapeEnv.create_symfloatnode" title="Permalink to this definition">¶</a></dt>
 <dd><p>Create a SymFloat value from a symbolic expression</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -723,7 +723,7 @@ <h1>ShapeEnv<a class="headerlink" href="#shapeenv" title="Permalink to this head
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.experimental.symbolic_shapes.ShapeEnv.create_symintnode">
-<span class="sig-name descname"><span class="pre">create_symintnode</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sym</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">hint</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">source</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#ShapeEnv.create_symintnode"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/experimental/symbolic_shapes.py#L4105"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.ShapeEnv.create_symintnode" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">create_symintnode</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sym</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">hint</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">source</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#ShapeEnv.create_symintnode"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/fx/experimental/symbolic_shapes.py#L4105"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.ShapeEnv.create_symintnode" title="Permalink to this definition">¶</a></dt>
 <dd><p>Create a SymInt value from a symbolic expression</p>
 <p>If you know what the current hint value of the SymInt to be created
 is, pass it into hint.  Otherwise, pass None and we will make our best
@@ -737,7 +737,7 @@ <h1>ShapeEnv<a class="headerlink" href="#shapeenv" title="Permalink to this head
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.experimental.symbolic_shapes.ShapeEnv.create_unbacked_symbool">
-<span class="sig-name descname"><span class="pre">create_unbacked_symbool</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#ShapeEnv.create_unbacked_symbool"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/experimental/symbolic_shapes.py#L4292"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.ShapeEnv.create_unbacked_symbool" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">create_unbacked_symbool</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#ShapeEnv.create_unbacked_symbool"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/fx/experimental/symbolic_shapes.py#L4292"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.ShapeEnv.create_unbacked_symbool" title="Permalink to this definition">¶</a></dt>
 <dd><p>Create a symbolic boolean without a hint value</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -748,7 +748,7 @@ <h1>ShapeEnv<a class="headerlink" href="#shapeenv" title="Permalink to this head
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.experimental.symbolic_shapes.ShapeEnv.create_unbacked_symfloat">
-<span class="sig-name descname"><span class="pre">create_unbacked_symfloat</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#ShapeEnv.create_unbacked_symfloat"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/experimental/symbolic_shapes.py#L4244"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.ShapeEnv.create_unbacked_symfloat" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">create_unbacked_symfloat</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#ShapeEnv.create_unbacked_symfloat"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/fx/experimental/symbolic_shapes.py#L4244"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.ShapeEnv.create_unbacked_symfloat" title="Permalink to this definition">¶</a></dt>
 <dd><p>Create a symbolic float without a hint value</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -759,7 +759,7 @@ <h1>ShapeEnv<a class="headerlink" href="#shapeenv" title="Permalink to this head
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.experimental.symbolic_shapes.ShapeEnv.create_unbacked_symint">
-<span class="sig-name descname"><span class="pre">create_unbacked_symint</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">source</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#ShapeEnv.create_unbacked_symint"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/experimental/symbolic_shapes.py#L4266"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.ShapeEnv.create_unbacked_symint" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">create_unbacked_symint</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">source</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#ShapeEnv.create_unbacked_symint"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/fx/experimental/symbolic_shapes.py#L4266"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.ShapeEnv.create_unbacked_symint" title="Permalink to this definition">¶</a></dt>
 <dd><p>Create a symbolic integer without a hint value</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -770,7 +770,7 @@ <h1>ShapeEnv<a class="headerlink" href="#shapeenv" title="Permalink to this head
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.experimental.symbolic_shapes.ShapeEnv.create_unspecified_symbol">
-<span class="sig-name descname"><span class="pre">create_unspecified_symbol</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">val</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">source</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dynamic_dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">DimDynamic.DUCK</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">constraint_dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">symbolic_context</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#ShapeEnv.create_unspecified_symbol"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/experimental/symbolic_shapes.py#L4314"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.ShapeEnv.create_unspecified_symbol" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">create_unspecified_symbol</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">val</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">source</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dynamic_dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">DimDynamic.DUCK</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">constraint_dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">symbolic_context</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#ShapeEnv.create_unspecified_symbol"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/fx/experimental/symbolic_shapes.py#L4314"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.ShapeEnv.create_unspecified_symbol" title="Permalink to this definition">¶</a></dt>
 <dd><p>Create a symbol with an unspecified value</p>
 <p>Compared to standard symbols we do not assume the value is positive,
 nor do we specialze on zero or one values.</p>
@@ -783,7 +783,7 @@ <h1>ShapeEnv<a class="headerlink" href="#shapeenv" title="Permalink to this head
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.experimental.symbolic_shapes.ShapeEnv.create_unspecified_symint_and_symbol">
-<span class="sig-name descname"><span class="pre">create_unspecified_symint_and_symbol</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">source</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dynamic_dim</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#ShapeEnv.create_unspecified_symint_and_symbol"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/experimental/symbolic_shapes.py#L4186"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.ShapeEnv.create_unspecified_symint_and_symbol" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">create_unspecified_symint_and_symbol</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">source</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dynamic_dim</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#ShapeEnv.create_unspecified_symint_and_symbol"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/fx/experimental/symbolic_shapes.py#L4186"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.ShapeEnv.create_unspecified_symint_and_symbol" title="Permalink to this definition">¶</a></dt>
 <dd><p>Create a SymInt wrapping a new unspecified symbol</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -794,7 +794,7 @@ <h1>ShapeEnv<a class="headerlink" href="#shapeenv" title="Permalink to this head
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.experimental.symbolic_shapes.ShapeEnv.defer_runtime_assert">
-<span class="sig-name descname"><span class="pre">defer_runtime_assert</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">orig_expr</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">msg</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fx_node</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#ShapeEnv.defer_runtime_assert"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/experimental/symbolic_shapes.py#L6759"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.ShapeEnv.defer_runtime_assert" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">defer_runtime_assert</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">orig_expr</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">msg</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fx_node</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#ShapeEnv.defer_runtime_assert"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/fx/experimental/symbolic_shapes.py#L6759"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.ShapeEnv.defer_runtime_assert" title="Permalink to this definition">¶</a></dt>
 <dd><p>Create an assert that is checked at runtime</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -813,7 +813,7 @@ <h1>ShapeEnv<a class="headerlink" href="#shapeenv" title="Permalink to this head
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.experimental.symbolic_shapes.ShapeEnv.deserialize_symexpr">
-<span class="sig-name descname"><span class="pre">deserialize_symexpr</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">code</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#ShapeEnv.deserialize_symexpr"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/experimental/symbolic_shapes.py#L5433"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.ShapeEnv.deserialize_symexpr" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">deserialize_symexpr</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">code</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#ShapeEnv.deserialize_symexpr"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/fx/experimental/symbolic_shapes.py#L5433"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.ShapeEnv.deserialize_symexpr" title="Permalink to this definition">¶</a></dt>
 <dd><p>To be used by compile_fx to deserialize symexprs</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -824,7 +824,7 @@ <h1>ShapeEnv<a class="headerlink" href="#shapeenv" title="Permalink to this head
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.experimental.symbolic_shapes.ShapeEnv.evaluate_guards_expression">
-<span class="sig-name descname"><span class="pre">evaluate_guards_expression</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">code</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#ShapeEnv.evaluate_guards_expression"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/experimental/symbolic_shapes.py#L5443"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.ShapeEnv.evaluate_guards_expression" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">evaluate_guards_expression</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">code</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#ShapeEnv.evaluate_guards_expression"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/fx/experimental/symbolic_shapes.py#L5443"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.ShapeEnv.evaluate_guards_expression" title="Permalink to this definition">¶</a></dt>
 <dd><p>Expected to be used with produce_guards_expression(). Evaluates an expression
 generated by produce_guards_expression for the given concrete args.</p>
 <dl class="field-list simple">
@@ -836,7 +836,7 @@ <h1>ShapeEnv<a class="headerlink" href="#shapeenv" title="Permalink to this head
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.experimental.symbolic_shapes.ShapeEnv.evaluate_guards_for_args">
-<span class="sig-name descname"><span class="pre">evaluate_guards_for_args</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">placeholders</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ignore_static</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#ShapeEnv.evaluate_guards_for_args"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/experimental/symbolic_shapes.py#L5451"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.ShapeEnv.evaluate_guards_for_args" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">evaluate_guards_for_args</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">placeholders</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ignore_static</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#ShapeEnv.evaluate_guards_for_args"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/fx/experimental/symbolic_shapes.py#L5451"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.ShapeEnv.evaluate_guards_for_args" title="Permalink to this definition">¶</a></dt>
 <dd><p>Generate guards for a graph’s placeholder values and evaluate the guards with args</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -847,7 +847,7 @@ <h1>ShapeEnv<a class="headerlink" href="#shapeenv" title="Permalink to this head
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.experimental.symbolic_shapes.ShapeEnv.evaluate_symexpr">
-<span class="sig-name descname"><span class="pre">evaluate_symexpr</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">code</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#ShapeEnv.evaluate_symexpr"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/experimental/symbolic_shapes.py#L5426"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.ShapeEnv.evaluate_symexpr" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">evaluate_symexpr</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">code</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#ShapeEnv.evaluate_symexpr"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/fx/experimental/symbolic_shapes.py#L5426"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.ShapeEnv.evaluate_symexpr" title="Permalink to this definition">¶</a></dt>
 <dd><p>To be used by compile_fx to evaluate symexprs</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -858,7 +858,7 @@ <h1>ShapeEnv<a class="headerlink" href="#shapeenv" title="Permalink to this head
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.experimental.symbolic_shapes.ShapeEnv.format_guards">
-<span class="sig-name descname"><span class="pre">format_guards</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#ShapeEnv.format_guards"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/experimental/symbolic_shapes.py#L5537"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.ShapeEnv.format_guards" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">format_guards</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#ShapeEnv.format_guards"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/fx/experimental/symbolic_shapes.py#L5537"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.ShapeEnv.format_guards" title="Permalink to this definition">¶</a></dt>
 <dd><p>Format this shape env’s guard expressions with optional traceback info if verbose</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -869,7 +869,7 @@ <h1>ShapeEnv<a class="headerlink" href="#shapeenv" title="Permalink to this head
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.experimental.symbolic_shapes.ShapeEnv.freeze">
-<span class="sig-name descname"><span class="pre">freeze</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#ShapeEnv.freeze"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/experimental/symbolic_shapes.py#L3639"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.ShapeEnv.freeze" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">freeze</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#ShapeEnv.freeze"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/fx/experimental/symbolic_shapes.py#L3639"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.ShapeEnv.freeze" title="Permalink to this definition">¶</a></dt>
 <dd><p>Freeze this ShapeEnv to stop accumulating guards</p>
 <p>A frozen ShapeEnv will ignore any further guards generated on it and
 only emit a warning which may lead to accuracy problems.</p>
@@ -879,7 +879,7 @@ <h1>ShapeEnv<a class="headerlink" href="#shapeenv" title="Permalink to this head
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.experimental.symbolic_shapes.ShapeEnv.freeze_runtime_asserts">
-<span class="sig-name descname"><span class="pre">freeze_runtime_asserts</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#ShapeEnv.freeze_runtime_asserts"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/experimental/symbolic_shapes.py#L3648"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.ShapeEnv.freeze_runtime_asserts" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">freeze_runtime_asserts</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#ShapeEnv.freeze_runtime_asserts"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/fx/experimental/symbolic_shapes.py#L3648"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.ShapeEnv.freeze_runtime_asserts" title="Permalink to this definition">¶</a></dt>
 <dd><p>Freeze this ShapeEnv to stop adding deferred runtime asserts.</p>
 <p>We will error if you try to install a new runtime assert when it is
 frozen.  This would indicate a lowering violation, or perhaps something
@@ -891,7 +891,7 @@ <h1>ShapeEnv<a class="headerlink" href="#shapeenv" title="Permalink to this head
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.experimental.symbolic_shapes.ShapeEnv.get_axioms">
-<span class="sig-name descname"><span class="pre">get_axioms</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">symbols</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">compute_hint</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#ShapeEnv.get_axioms"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/experimental/symbolic_shapes.py#L5563"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.ShapeEnv.get_axioms" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">get_axioms</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">symbols</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">compute_hint</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#ShapeEnv.get_axioms"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/fx/experimental/symbolic_shapes.py#L5563"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.ShapeEnv.get_axioms" title="Permalink to this definition">¶</a></dt>
 <dd><p>Given the symbols in an expression, it returns all the runtime asserts that have those symbols
 concatenated with all the guards.
 If symbols is None, it returns all the runtime asserts (and all the guards)</p>
@@ -904,7 +904,7 @@ <h1>ShapeEnv<a class="headerlink" href="#shapeenv" title="Permalink to this head
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.experimental.symbolic_shapes.ShapeEnv.get_implications">
-<span class="sig-name descname"><span class="pre">get_implications</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">e</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#ShapeEnv.get_implications"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/experimental/symbolic_shapes.py#L5593"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.ShapeEnv.get_implications" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">get_implications</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">e</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#ShapeEnv.get_implications"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/fx/experimental/symbolic_shapes.py#L5593"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.ShapeEnv.get_implications" title="Permalink to this definition">¶</a></dt>
 <dd><p>Given a expression, it returns a list of predicates that follow from it</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -915,7 +915,7 @@ <h1>ShapeEnv<a class="headerlink" href="#shapeenv" title="Permalink to this head
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.experimental.symbolic_shapes.ShapeEnv.get_nontrivial_guards">
-<span class="sig-name descname"><span class="pre">get_nontrivial_guards</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#ShapeEnv.get_nontrivial_guards"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/experimental/symbolic_shapes.py#L5529"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.ShapeEnv.get_nontrivial_guards" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">get_nontrivial_guards</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#ShapeEnv.get_nontrivial_guards"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/fx/experimental/symbolic_shapes.py#L5529"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.ShapeEnv.get_nontrivial_guards" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns a list of guard expressions that aren’t statically known (i.e. not trivial)</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -926,7 +926,7 @@ <h1>ShapeEnv<a class="headerlink" href="#shapeenv" title="Permalink to this head
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.experimental.symbolic_shapes.ShapeEnv.get_pruned_guards">
-<span class="sig-name descname"><span class="pre">get_pruned_guards</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">symints</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#ShapeEnv.get_pruned_guards"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/experimental/symbolic_shapes.py#L5464"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.ShapeEnv.get_pruned_guards" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">get_pruned_guards</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">symints</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#ShapeEnv.get_pruned_guards"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/fx/experimental/symbolic_shapes.py#L5464"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.ShapeEnv.get_pruned_guards" title="Permalink to this definition">¶</a></dt>
 <dd><p>Get a list of guards, but pruned so it only provides guards that
 reference symints from the passed in input</p>
 <dl class="field-list simple">
@@ -938,7 +938,7 @@ <h1>ShapeEnv<a class="headerlink" href="#shapeenv" title="Permalink to this head
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.experimental.symbolic_shapes.ShapeEnv.ignore_fresh_unbacked_symbols">
-<span class="sig-name descname"><span class="pre">ignore_fresh_unbacked_symbols</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#ShapeEnv.ignore_fresh_unbacked_symbols"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/experimental/symbolic_shapes.py#L3627"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.ShapeEnv.ignore_fresh_unbacked_symbols" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">ignore_fresh_unbacked_symbols</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#ShapeEnv.ignore_fresh_unbacked_symbols"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/fx/experimental/symbolic_shapes.py#L3627"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.ShapeEnv.ignore_fresh_unbacked_symbols" title="Permalink to this definition">¶</a></dt>
 <dd><p>Indicates that the newly allocated unbacked SymInts are being
 discarded</p>
 <dl class="field-list simple">
@@ -950,7 +950,7 @@ <h1>ShapeEnv<a class="headerlink" href="#shapeenv" title="Permalink to this head
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.experimental.symbolic_shapes.ShapeEnv.is_unbacked_symint">
-<span class="sig-name descname"><span class="pre">is_unbacked_symint</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">symbol</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#ShapeEnv.is_unbacked_symint"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/experimental/symbolic_shapes.py#L4288"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.ShapeEnv.is_unbacked_symint" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">is_unbacked_symint</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">symbol</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#ShapeEnv.is_unbacked_symint"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/fx/experimental/symbolic_shapes.py#L4288"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.ShapeEnv.is_unbacked_symint" title="Permalink to this definition">¶</a></dt>
 <dd><p>Check if a sympy symbol matches the naming convention for unbacked symbols</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -961,7 +961,7 @@ <h1>ShapeEnv<a class="headerlink" href="#shapeenv" title="Permalink to this head
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.experimental.symbolic_shapes.ShapeEnv.produce_guards">
-<span class="sig-name descname"><span class="pre">produce_guards</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#ShapeEnv.produce_guards"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/experimental/symbolic_shapes.py#L4627"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.ShapeEnv.produce_guards" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">produce_guards</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#ShapeEnv.produce_guards"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/fx/experimental/symbolic_shapes.py#L4627"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.ShapeEnv.produce_guards" title="Permalink to this definition">¶</a></dt>
 <dd><p>Like produce_guards_verbose, but only returns the non-verbose python guard expressions
 (no verbose guards produced.)</p>
 <dl class="field-list simple">
@@ -973,7 +973,7 @@ <h1>ShapeEnv<a class="headerlink" href="#shapeenv" title="Permalink to this head
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.experimental.symbolic_shapes.ShapeEnv.produce_guards_expression">
-<span class="sig-name descname"><span class="pre">produce_guards_expression</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">placeholders</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">guards</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ignore_static</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#ShapeEnv.produce_guards_expression"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/experimental/symbolic_shapes.py#L5401"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.ShapeEnv.produce_guards_expression" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">produce_guards_expression</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">placeholders</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">guards</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ignore_static</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#ShapeEnv.produce_guards_expression"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/fx/experimental/symbolic_shapes.py#L5401"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.ShapeEnv.produce_guards_expression" title="Permalink to this definition">¶</a></dt>
 <dd><p>Expected to be used with evaluate_guards_expression(). Produces the guards
 for the given placeholders and returns a string expression to be evaluated
 by evaluate_guards_expression given concrete values for the placeholders.</p>
@@ -986,7 +986,7 @@ <h1>ShapeEnv<a class="headerlink" href="#shapeenv" title="Permalink to this head
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.experimental.symbolic_shapes.ShapeEnv.produce_guards_verbose">
-<span class="sig-name descname"><span class="pre">produce_guards_verbose</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">placeholders</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sources</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">source_ref=&lt;function</span> <span class="pre">ShapeEnv.&lt;lambda&gt;&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">guards=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_contexts=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">equalities_inputs=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_simplified=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ignore_static=True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">langs=('python'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">'verbose_python')</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#ShapeEnv.produce_guards_verbose"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/experimental/symbolic_shapes.py#L4634"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.ShapeEnv.produce_guards_verbose" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">produce_guards_verbose</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">placeholders</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sources</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">source_ref=&lt;function</span> <span class="pre">ShapeEnv.&lt;lambda&gt;&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">guards=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_contexts=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">equalities_inputs=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_simplified=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ignore_static=True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">langs=('python'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">'verbose_python')</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#ShapeEnv.produce_guards_verbose"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/fx/experimental/symbolic_shapes.py#L4634"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.ShapeEnv.produce_guards_verbose" title="Permalink to this definition">¶</a></dt>
 <dd><p>Generates a list of guards strings which, when evaluated in a context that
 defines tensors for all the sources, returns True or False depending
 on if the guards in the list evaluated to True or not.  Primarily used by Dynamo,
@@ -1011,7 +1011,7 @@ <h1>ShapeEnv<a class="headerlink" href="#shapeenv" title="Permalink to this head
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.experimental.symbolic_shapes.ShapeEnv.replace">
-<span class="sig-name descname"><span class="pre">replace</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">expr</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#ShapeEnv.replace"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/experimental/symbolic_shapes.py#L5722"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.ShapeEnv.replace" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">replace</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">expr</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#ShapeEnv.replace"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/fx/experimental/symbolic_shapes.py#L5722"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.ShapeEnv.replace" title="Permalink to this definition">¶</a></dt>
 <dd><p>Apply symbol replacements to any symbols in the given expression</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -1022,7 +1022,7 @@ <h1>ShapeEnv<a class="headerlink" href="#shapeenv" title="Permalink to this head
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.experimental.symbolic_shapes.ShapeEnv.set_unbacked_var_to_val">
-<span class="sig-name descname"><span class="pre">set_unbacked_var_to_val</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">k</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">v</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#ShapeEnv.set_unbacked_var_to_val"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/experimental/symbolic_shapes.py#L3513"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.ShapeEnv.set_unbacked_var_to_val" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">set_unbacked_var_to_val</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">k</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">v</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#ShapeEnv.set_unbacked_var_to_val"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/fx/experimental/symbolic_shapes.py#L3513"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.ShapeEnv.set_unbacked_var_to_val" title="Permalink to this definition">¶</a></dt>
 <dd><p>Used only when propagate_real_tensors; registers a value for an
 unbacked symbol, which can be used last resort to resolve hints.</p>
 <dl class="field-list simple">
@@ -1031,7 +1031,7 @@ <h1>ShapeEnv<a class="headerlink" href="#shapeenv" title="Permalink to this head
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.experimental.symbolic_shapes.ShapeEnv.simplify">
-<span class="sig-name descname"><span class="pre">simplify</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">expr</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#ShapeEnv.simplify"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/experimental/symbolic_shapes.py#L5749"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.ShapeEnv.simplify" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">simplify</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">expr</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#ShapeEnv.simplify"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/fx/experimental/symbolic_shapes.py#L5749"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.ShapeEnv.simplify" title="Permalink to this definition">¶</a></dt>
 <dd><p>Use known constraints and replacements to simplify the given expr</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -1042,7 +1042,7 @@ <h1>ShapeEnv<a class="headerlink" href="#shapeenv" title="Permalink to this head
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.experimental.symbolic_shapes.ShapeEnv.size_hint">
-<span class="sig-name descname"><span class="pre">size_hint</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">expr</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">allow_none</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#ShapeEnv.size_hint"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/experimental/symbolic_shapes.py#L5797"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.ShapeEnv.size_hint" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">size_hint</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">expr</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">allow_none</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#ShapeEnv.size_hint"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/fx/experimental/symbolic_shapes.py#L5797"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.ShapeEnv.size_hint" title="Permalink to this definition">¶</a></dt>
 <dd><p>Gets a size hint for a given expression from the underlying shapes we had.
 Does not introduce a guard, so only use this when you can guarantee that
 your code is still valid for arbitrary shapes (such as optimization decisions)</p>
@@ -1055,7 +1055,7 @@ <h1>ShapeEnv<a class="headerlink" href="#shapeenv" title="Permalink to this head
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.experimental.symbolic_shapes.ShapeEnv.suppress_guards">
-<span class="sig-name descname"><span class="pre">suppress_guards</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#ShapeEnv.suppress_guards"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/experimental/symbolic_shapes.py#L3775"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.ShapeEnv.suppress_guards" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">suppress_guards</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#ShapeEnv.suppress_guards"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/fx/experimental/symbolic_shapes.py#L3775"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.ShapeEnv.suppress_guards" title="Permalink to this definition">¶</a></dt>
 <dd><p>Context manager to ignore all guards generated inside</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
diff --git a/main/generated/torch.fx.experimental.symbolic_shapes.ShapeEnvSettings.html b/main/generated/torch.fx.experimental.symbolic_shapes.ShapeEnvSettings.html
index 40268b6c269..d2c1d5f4183 100644
--- a/main/generated/torch.fx.experimental.symbolic_shapes.ShapeEnvSettings.html
+++ b/main/generated/torch.fx.experimental.symbolic_shapes.ShapeEnvSettings.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>ShapeEnvSettings<a class="headerlink" href="#shapeenvsettings" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.fx.experimental.symbolic_shapes.ShapeEnvSettings">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.fx.experimental.symbolic_shapes.</span></span><span class="sig-name descname"><span class="pre">ShapeEnvSettings</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">allow_scalar_outputs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">allow_dynamic_output_shape_ops</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">assume_static_by_default</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">specialize_zero_one</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">duck_shape</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prefer_deferred_runtime_asserts_over_guards</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">allow_complex_guards_as_runtime_asserts</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#ShapeEnvSettings"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/experimental/symbolic_shapes.py#L3046"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.ShapeEnvSettings" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.fx.experimental.symbolic_shapes.</span></span><span class="sig-name descname"><span class="pre">ShapeEnvSettings</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">allow_scalar_outputs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">allow_dynamic_output_shape_ops</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">assume_static_by_default</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">specialize_zero_one</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">duck_shape</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prefer_deferred_runtime_asserts_over_guards</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">allow_complex_guards_as_runtime_asserts</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#ShapeEnvSettings"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/fx/experimental/symbolic_shapes.py#L3046"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.ShapeEnvSettings" title="Permalink to this definition">¶</a></dt>
 <dd><p>Encapsulates all shape env settings that could potentially affect
 FakeTensor dispatch. Used when creating dispatch cache keys.</p>
 <dl class="field-list simple">
diff --git a/main/generated/torch.fx.experimental.symbolic_shapes.StatefulSymbolicContext.html b/main/generated/torch.fx.experimental.symbolic_shapes.StatefulSymbolicContext.html
index 734034a7a67..c435e207426 100644
--- a/main/generated/torch.fx.experimental.symbolic_shapes.StatefulSymbolicContext.html
+++ b/main/generated/torch.fx.experimental.symbolic_shapes.StatefulSymbolicContext.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>StatefulSymbolicContext<a class="headerlink" href="#statefulsymboliccontext" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.fx.experimental.symbolic_shapes.StatefulSymbolicContext">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.fx.experimental.symbolic_shapes.</span></span><span class="sig-name descname"><span class="pre">StatefulSymbolicContext</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dynamic_sizes</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dynamic_strides</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">constraint_sizes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">constraint_strides</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">view_base_context</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tensor_source</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shape_env_to_source_to_symbol_cache</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#StatefulSymbolicContext"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/experimental/symbolic_shapes.py#L1807"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.StatefulSymbolicContext" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.fx.experimental.symbolic_shapes.</span></span><span class="sig-name descname"><span class="pre">StatefulSymbolicContext</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dynamic_sizes</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dynamic_strides</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">constraint_sizes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">constraint_strides</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">view_base_context</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tensor_source</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shape_env_to_source_to_symbol_cache</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#StatefulSymbolicContext"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/fx/experimental/symbolic_shapes.py#L1807"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.StatefulSymbolicContext" title="Permalink to this definition">¶</a></dt>
 <dd><p>Create symbols in <code class="docutils literal notranslate"><span class="pre">create_symbolic_sizes_strides_storage_offset</span></code> via
 a symbolic_context determination as given by a cache of Source:Symbol. A cache hit
 will reuse a stored symbol, and a cache miss will write to this cache.</p>
diff --git a/main/generated/torch.fx.experimental.symbolic_shapes.StatelessSymbolicContext.html b/main/generated/torch.fx.experimental.symbolic_shapes.StatelessSymbolicContext.html
index 2179f99c9bc..9a1642eba02 100644
--- a/main/generated/torch.fx.experimental.symbolic_shapes.StatelessSymbolicContext.html
+++ b/main/generated/torch.fx.experimental.symbolic_shapes.StatelessSymbolicContext.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>StatelessSymbolicContext<a class="headerlink" href="#statelesssymboliccontext" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.fx.experimental.symbolic_shapes.StatelessSymbolicContext">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.fx.experimental.symbolic_shapes.</span></span><span class="sig-name descname"><span class="pre">StatelessSymbolicContext</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dynamic_sizes</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dynamic_strides</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">constraint_sizes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">constraint_strides</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">view_base_context</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#StatelessSymbolicContext"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/experimental/symbolic_shapes.py#L1746"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.StatelessSymbolicContext" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.fx.experimental.symbolic_shapes.</span></span><span class="sig-name descname"><span class="pre">StatelessSymbolicContext</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dynamic_sizes</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dynamic_strides</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">constraint_sizes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">constraint_strides</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">view_base_context</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#StatelessSymbolicContext"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/fx/experimental/symbolic_shapes.py#L1746"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.StatelessSymbolicContext" title="Permalink to this definition">¶</a></dt>
 <dd><p>Create symbols in <code class="docutils literal notranslate"><span class="pre">create_symbolic_sizes_strides_storage_offset</span></code> via
 a symbolic_context determination as given by <code class="docutils literal notranslate"><span class="pre">DimDynamic</span></code> and <code class="docutils literal notranslate"><span class="pre">DimConstraint</span></code>.
 This will cause fresh symbols to be allocated</p>
diff --git a/main/generated/torch.fx.experimental.symbolic_shapes.StrictMinMaxConstraint.html b/main/generated/torch.fx.experimental.symbolic_shapes.StrictMinMaxConstraint.html
index 8066b87beab..d0bd66f569c 100644
--- a/main/generated/torch.fx.experimental.symbolic_shapes.StrictMinMaxConstraint.html
+++ b/main/generated/torch.fx.experimental.symbolic_shapes.StrictMinMaxConstraint.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>StrictMinMaxConstraint<a class="headerlink" href="#strictminmaxconstraint" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.fx.experimental.symbolic_shapes.StrictMinMaxConstraint">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.fx.experimental.symbolic_shapes.</span></span><span class="sig-name descname"><span class="pre">StrictMinMaxConstraint</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">warn_only</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">vr</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#StrictMinMaxConstraint"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/experimental/symbolic_shapes.py#L1531"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.StrictMinMaxConstraint" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.fx.experimental.symbolic_shapes.</span></span><span class="sig-name descname"><span class="pre">StrictMinMaxConstraint</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">warn_only</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">vr</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#StrictMinMaxConstraint"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/fx/experimental/symbolic_shapes.py#L1531"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.StrictMinMaxConstraint" title="Permalink to this definition">¶</a></dt>
 <dd><p>For clients: the size at this dimension must be within ‘vr’ (which
 specifies a lower and upper bound, inclusive-inclusive) AND it
 must be non-negative and should not be 0 or 1 (but see NB below).</p>
@@ -630,7 +630,7 @@ <h1>StrictMinMaxConstraint<a class="headerlink" href="#strictminmaxconstraint" t
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.fx.experimental.symbolic_shapes.StrictMinMaxConstraint.render">
-<span class="sig-name descname"><span class="pre">render</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">source</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#StrictMinMaxConstraint.render"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/experimental/symbolic_shapes.py#L1554"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.StrictMinMaxConstraint.render" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">render</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">source</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#StrictMinMaxConstraint.render"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/fx/experimental/symbolic_shapes.py#L1554"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.StrictMinMaxConstraint.render" title="Permalink to this definition">¶</a></dt>
 <dd><p>Format the constrain equation</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
diff --git a/main/generated/torch.fx.experimental.symbolic_shapes.SubclassSymbolicContext.html b/main/generated/torch.fx.experimental.symbolic_shapes.SubclassSymbolicContext.html
index d51081176b7..92b7e47f6c3 100644
--- a/main/generated/torch.fx.experimental.symbolic_shapes.SubclassSymbolicContext.html
+++ b/main/generated/torch.fx.experimental.symbolic_shapes.SubclassSymbolicContext.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>SubclassSymbolicContext<a class="headerlink" href="#subclasssymboliccontext" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.fx.experimental.symbolic_shapes.SubclassSymbolicContext">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.fx.experimental.symbolic_shapes.</span></span><span class="sig-name descname"><span class="pre">SubclassSymbolicContext</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dynamic_sizes</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dynamic_strides</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">constraint_sizes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">constraint_strides</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">view_base_context</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tensor_source</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shape_env_to_source_to_symbol_cache</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inner_contexts</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#SubclassSymbolicContext"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/experimental/symbolic_shapes.py#L1842"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.SubclassSymbolicContext" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.fx.experimental.symbolic_shapes.</span></span><span class="sig-name descname"><span class="pre">SubclassSymbolicContext</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dynamic_sizes</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dynamic_strides</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">constraint_sizes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">constraint_strides</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">view_base_context</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tensor_source</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shape_env_to_source_to_symbol_cache</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inner_contexts</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#SubclassSymbolicContext"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/fx/experimental/symbolic_shapes.py#L1842"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.SubclassSymbolicContext" title="Permalink to this definition">¶</a></dt>
 <dd><p>The correct symbolic context for a given inner tensor of a traceable tensor subclass
 may differ from that of the outer symbolic context. This structure allows for this
 flexibility, with inner symbolic contexts mapped via attr -&gt; symbolic context.</p>
diff --git a/main/generated/torch.fx.experimental.symbolic_shapes.SymbolicContext.html b/main/generated/torch.fx.experimental.symbolic_shapes.SymbolicContext.html
index db3b9cd65ba..e2be744d23d 100644
--- a/main/generated/torch.fx.experimental.symbolic_shapes.SymbolicContext.html
+++ b/main/generated/torch.fx.experimental.symbolic_shapes.SymbolicContext.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>SymbolicContext<a class="headerlink" href="#symboliccontext" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.fx.experimental.symbolic_shapes.SymbolicContext">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.fx.experimental.symbolic_shapes.</span></span><span class="sig-name descname"><span class="pre">SymbolicContext</span></span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#SymbolicContext"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/experimental/symbolic_shapes.py#L1733"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.SymbolicContext" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.fx.experimental.symbolic_shapes.</span></span><span class="sig-name descname"><span class="pre">SymbolicContext</span></span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#SymbolicContext"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/fx/experimental/symbolic_shapes.py#L1733"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.SymbolicContext" title="Permalink to this definition">¶</a></dt>
 <dd><p>Data structure specifying how we should create symbols in
 <code class="docutils literal notranslate"><span class="pre">create_symbolic_sizes_strides_storage_offset</span></code>; e.g., should
 they be static or dynamic.</p>
diff --git a/main/generated/torch.fx.experimental.symbolic_shapes.canonicalize_bool_expr.html b/main/generated/torch.fx.experimental.symbolic_shapes.canonicalize_bool_expr.html
index c3ca781e547..df2b06377b0 100644
--- a/main/generated/torch.fx.experimental.symbolic_shapes.canonicalize_bool_expr.html
+++ b/main/generated/torch.fx.experimental.symbolic_shapes.canonicalize_bool_expr.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.fx.experimental.symbolic_shapes.canonicalize_bool_expr<a class="headerlink" href="#torch-fx-experimental-symbolic-shapes-canonicalize-bool-expr" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.fx.experimental.symbolic_shapes.canonicalize_bool_expr">
-<span class="sig-prename descclassname"><span class="pre">torch.fx.experimental.symbolic_shapes.</span></span><span class="sig-name descname"><span class="pre">canonicalize_bool_expr</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">expr</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#canonicalize_bool_expr"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/experimental/symbolic_shapes.py#L611"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.canonicalize_bool_expr" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.fx.experimental.symbolic_shapes.</span></span><span class="sig-name descname"><span class="pre">canonicalize_bool_expr</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">expr</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#canonicalize_bool_expr"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/fx/experimental/symbolic_shapes.py#L611"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.canonicalize_bool_expr" title="Permalink to this definition">¶</a></dt>
 <dd><p>Canonicalize a boolean expression by transforming it into a lt / le
 inequality and moving all the non-constant terms to the rhs.
 We canonicalize And / Ors / Not via cnf and then canonicalize their subexpr
diff --git a/main/generated/torch.fx.experimental.symbolic_shapes.check_consistent.html b/main/generated/torch.fx.experimental.symbolic_shapes.check_consistent.html
index 4a77e43d25a..0b32c03365c 100644
--- a/main/generated/torch.fx.experimental.symbolic_shapes.check_consistent.html
+++ b/main/generated/torch.fx.experimental.symbolic_shapes.check_consistent.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.fx.experimental.symbolic_shapes.check_consistent<a class="headerlink" href="#torch-fx-experimental-symbolic-shapes-check-consistent" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.fx.experimental.symbolic_shapes.check_consistent">
-<span class="sig-prename descclassname"><span class="pre">torch.fx.experimental.symbolic_shapes.</span></span><span class="sig-name descname"><span class="pre">check_consistent</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">new</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">old</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#check_consistent"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/experimental/symbolic_shapes.py#L424"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.check_consistent" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.fx.experimental.symbolic_shapes.</span></span><span class="sig-name descname"><span class="pre">check_consistent</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">new</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">old</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#check_consistent"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/fx/experimental/symbolic_shapes.py#L424"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.check_consistent" title="Permalink to this definition">¶</a></dt>
 <dd><p>Test that two “meta” values (typically either Tensor or SymInt) have
 the same values, e.g., after retracing.  If we don’t understand the
 quantities in question, we’ll just skip the consistency check.</p>
diff --git a/main/generated/torch.fx.experimental.symbolic_shapes.compute_unbacked_bindings.html b/main/generated/torch.fx.experimental.symbolic_shapes.compute_unbacked_bindings.html
index 08c466fee3d..597cd3f358e 100644
--- a/main/generated/torch.fx.experimental.symbolic_shapes.compute_unbacked_bindings.html
+++ b/main/generated/torch.fx.experimental.symbolic_shapes.compute_unbacked_bindings.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.fx.experimental.symbolic_shapes.compute_unbacked_bindings<a class="headerlink" href="#torch-fx-experimental-symbolic-shapes-compute-unbacked-bindings" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.fx.experimental.symbolic_shapes.compute_unbacked_bindings">
-<span class="sig-prename descclassname"><span class="pre">torch.fx.experimental.symbolic_shapes.</span></span><span class="sig-name descname"><span class="pre">compute_unbacked_bindings</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">shape_env</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">example_value</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">old_example_value</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">peek</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#compute_unbacked_bindings"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/experimental/symbolic_shapes.py#L938"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.compute_unbacked_bindings" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.fx.experimental.symbolic_shapes.</span></span><span class="sig-name descname"><span class="pre">compute_unbacked_bindings</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">shape_env</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">example_value</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">old_example_value</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">peek</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#compute_unbacked_bindings"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/fx/experimental/symbolic_shapes.py#L938"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.compute_unbacked_bindings" title="Permalink to this definition">¶</a></dt>
 <dd><p>After having run fake tensor propagation and producing example_value
 result, traverse example_value looking for freshly bound unbacked
 symbols and record their paths for later.  It is an error if
diff --git a/main/generated/torch.fx.experimental.symbolic_shapes.constrain_range.html b/main/generated/torch.fx.experimental.symbolic_shapes.constrain_range.html
index 1bdff868932..b8fad83d854 100644
--- a/main/generated/torch.fx.experimental.symbolic_shapes.constrain_range.html
+++ b/main/generated/torch.fx.experimental.symbolic_shapes.constrain_range.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.fx.experimental.symbolic_shapes.constrain_range<a class="headerlink" href="#torch-fx-experimental-symbolic-shapes-constrain-range" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.fx.experimental.symbolic_shapes.constrain_range">
-<span class="sig-prename descclassname"><span class="pre">torch.fx.experimental.symbolic_shapes.</span></span><span class="sig-name descname"><span class="pre">constrain_range</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">a</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#constrain_range"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/experimental/symbolic_shapes.py#L1317"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.constrain_range" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.fx.experimental.symbolic_shapes.</span></span><span class="sig-name descname"><span class="pre">constrain_range</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">a</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#constrain_range"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/fx/experimental/symbolic_shapes.py#L1317"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.constrain_range" title="Permalink to this definition">¶</a></dt>
 <dd><p>Applies a constraint that the passed in SymInt must lie between min-max
 inclusive-inclusive, WITHOUT introducing a guard on the SymInt (meaning
 that it can be used on unbacked SymInts).  If min/max are None, we assume
diff --git a/main/generated/torch.fx.experimental.symbolic_shapes.constrain_unify.html b/main/generated/torch.fx.experimental.symbolic_shapes.constrain_unify.html
index becdb8f02b8..af005bbe767 100644
--- a/main/generated/torch.fx.experimental.symbolic_shapes.constrain_unify.html
+++ b/main/generated/torch.fx.experimental.symbolic_shapes.constrain_unify.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
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     </div>
     <div id="searchBox">
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@@ -612,7 +612,7 @@
 <h1>torch.fx.experimental.symbolic_shapes.constrain_unify<a class="headerlink" href="#torch-fx-experimental-symbolic-shapes-constrain-unify" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.fx.experimental.symbolic_shapes.constrain_unify">
-<span class="sig-prename descclassname"><span class="pre">torch.fx.experimental.symbolic_shapes.</span></span><span class="sig-name descname"><span class="pre">constrain_unify</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">a</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">b</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#constrain_unify"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/experimental/symbolic_shapes.py#L1363"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.constrain_unify" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.fx.experimental.symbolic_shapes.</span></span><span class="sig-name descname"><span class="pre">constrain_unify</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">a</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">b</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#constrain_unify"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/fx/experimental/symbolic_shapes.py#L1363"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.constrain_unify" title="Permalink to this definition">¶</a></dt>
 <dd><p>Given two SymInts, constrain them so that they must be equal.  NB:
 this will not work with SymInts that represent nontrivial expressions
 (yet!)</p>
diff --git a/main/generated/torch.fx.experimental.symbolic_shapes.definitely_false.html b/main/generated/torch.fx.experimental.symbolic_shapes.definitely_false.html
index 5b01cb64d1d..8561abae80b 100644
--- a/main/generated/torch.fx.experimental.symbolic_shapes.definitely_false.html
+++ b/main/generated/torch.fx.experimental.symbolic_shapes.definitely_false.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.fx.experimental.symbolic_shapes.definitely_false<a class="headerlink" href="#torch-fx-experimental-symbolic-shapes-definitely-false" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.fx.experimental.symbolic_shapes.definitely_false">
-<span class="sig-prename descclassname"><span class="pre">torch.fx.experimental.symbolic_shapes.</span></span><span class="sig-name descname"><span class="pre">definitely_false</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">a</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#definitely_false"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/experimental/symbolic_shapes.py#L1170"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.definitely_false" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.fx.experimental.symbolic_shapes.</span></span><span class="sig-name descname"><span class="pre">definitely_false</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">a</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#definitely_false"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/fx/experimental/symbolic_shapes.py#L1170"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.definitely_false" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns True only if we can tell that a is False, possibly introducing
 a guard in the process.  If a depends on some unbacked SymInt, we may
 return False even though there may exist a possible value of the SymInt
diff --git a/main/generated/torch.fx.experimental.symbolic_shapes.definitely_true.html b/main/generated/torch.fx.experimental.symbolic_shapes.definitely_true.html
index 6d3b7533a95..55700692d81 100644
--- a/main/generated/torch.fx.experimental.symbolic_shapes.definitely_true.html
+++ b/main/generated/torch.fx.experimental.symbolic_shapes.definitely_true.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.fx.experimental.symbolic_shapes.definitely_true<a class="headerlink" href="#torch-fx-experimental-symbolic-shapes-definitely-true" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.fx.experimental.symbolic_shapes.definitely_true">
-<span class="sig-prename descclassname"><span class="pre">torch.fx.experimental.symbolic_shapes.</span></span><span class="sig-name descname"><span class="pre">definitely_true</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">a</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#definitely_true"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/experimental/symbolic_shapes.py#L1144"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.definitely_true" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.fx.experimental.symbolic_shapes.</span></span><span class="sig-name descname"><span class="pre">definitely_true</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">a</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#definitely_true"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/fx/experimental/symbolic_shapes.py#L1144"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.definitely_true" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns True only if we can tell that a is True, possibly introducing
 a guard in the process.  If a depends on some unbacked SymInt, we may
 return False even though there may exist a possible value of the SymInt
diff --git a/main/generated/torch.fx.experimental.symbolic_shapes.guard_size_oblivious.html b/main/generated/torch.fx.experimental.symbolic_shapes.guard_size_oblivious.html
index 39d98078549..8214622d616 100644
--- a/main/generated/torch.fx.experimental.symbolic_shapes.guard_size_oblivious.html
+++ b/main/generated/torch.fx.experimental.symbolic_shapes.guard_size_oblivious.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.fx.experimental.symbolic_shapes.guard_size_oblivious<a class="headerlink" href="#torch-fx-experimental-symbolic-shapes-guard-size-oblivious" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.fx.experimental.symbolic_shapes.guard_size_oblivious">
-<span class="sig-prename descclassname"><span class="pre">torch.fx.experimental.symbolic_shapes.</span></span><span class="sig-name descname"><span class="pre">guard_size_oblivious</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">expr</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#guard_size_oblivious"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/experimental/symbolic_shapes.py#L393"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.guard_size_oblivious" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.fx.experimental.symbolic_shapes.</span></span><span class="sig-name descname"><span class="pre">guard_size_oblivious</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">expr</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#guard_size_oblivious"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/fx/experimental/symbolic_shapes.py#L393"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.guard_size_oblivious" title="Permalink to this definition">¶</a></dt>
 <dd><p>Perform a guard on a symbolic boolean expression in a size oblivious way.
 This is typically used when a non-oblivious test would result in a guard
 on a data dependent value of which we don’t know the value of at compile time.
diff --git a/main/generated/torch.fx.experimental.symbolic_shapes.has_free_symbols.html b/main/generated/torch.fx.experimental.symbolic_shapes.has_free_symbols.html
index f44713a4c9e..52c56784448 100644
--- a/main/generated/torch.fx.experimental.symbolic_shapes.has_free_symbols.html
+++ b/main/generated/torch.fx.experimental.symbolic_shapes.has_free_symbols.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.fx.experimental.symbolic_shapes.has_free_symbols<a class="headerlink" href="#torch-fx-experimental-symbolic-shapes-has-free-symbols" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.fx.experimental.symbolic_shapes.has_free_symbols">
-<span class="sig-prename descclassname"><span class="pre">torch.fx.experimental.symbolic_shapes.</span></span><span class="sig-name descname"><span class="pre">has_free_symbols</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">val</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#has_free_symbols"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/experimental/symbolic_shapes.py#L836"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.has_free_symbols" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.fx.experimental.symbolic_shapes.</span></span><span class="sig-name descname"><span class="pre">has_free_symbols</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">val</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#has_free_symbols"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/fx/experimental/symbolic_shapes.py#L836"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.has_free_symbols" title="Permalink to this definition">¶</a></dt>
 <dd><p>Faster version of bool(free_symbols(val))</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
diff --git a/main/generated/torch.fx.experimental.symbolic_shapes.has_free_unbacked_symbols.html b/main/generated/torch.fx.experimental.symbolic_shapes.has_free_unbacked_symbols.html
index 4559952a1c5..f135944ad22 100644
--- a/main/generated/torch.fx.experimental.symbolic_shapes.has_free_unbacked_symbols.html
+++ b/main/generated/torch.fx.experimental.symbolic_shapes.has_free_unbacked_symbols.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.fx.experimental.symbolic_shapes.has_free_unbacked_symbols<a class="headerlink" href="#torch-fx-experimental-symbolic-shapes-has-free-unbacked-symbols" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.fx.experimental.symbolic_shapes.has_free_unbacked_symbols">
-<span class="sig-prename descclassname"><span class="pre">torch.fx.experimental.symbolic_shapes.</span></span><span class="sig-name descname"><span class="pre">has_free_unbacked_symbols</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#has_free_unbacked_symbols"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/experimental/symbolic_shapes.py#L841"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.has_free_unbacked_symbols" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.fx.experimental.symbolic_shapes.</span></span><span class="sig-name descname"><span class="pre">has_free_unbacked_symbols</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#has_free_unbacked_symbols"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/fx/experimental/symbolic_shapes.py#L841"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.has_free_unbacked_symbols" title="Permalink to this definition">¶</a></dt>
 <dd><p>Faster version of bool(free_unbacked_symbols(val))</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
diff --git a/main/generated/torch.fx.experimental.symbolic_shapes.hint_int.html b/main/generated/torch.fx.experimental.symbolic_shapes.hint_int.html
index 8bb7a8df805..bdb0e42b09a 100644
--- a/main/generated/torch.fx.experimental.symbolic_shapes.hint_int.html
+++ b/main/generated/torch.fx.experimental.symbolic_shapes.hint_int.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.fx.experimental.symbolic_shapes.hint_int<a class="headerlink" href="#torch-fx-experimental-symbolic-shapes-hint-int" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.fx.experimental.symbolic_shapes.hint_int">
-<span class="sig-prename descclassname"><span class="pre">torch.fx.experimental.symbolic_shapes.</span></span><span class="sig-name descname"><span class="pre">hint_int</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">a</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fallback</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#hint_int"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/experimental/symbolic_shapes.py#L333"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.hint_int" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.fx.experimental.symbolic_shapes.</span></span><span class="sig-name descname"><span class="pre">hint_int</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">a</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fallback</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#hint_int"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/fx/experimental/symbolic_shapes.py#L333"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.hint_int" title="Permalink to this definition">¶</a></dt>
 <dd><p>Retrieve the hint for an int (based on the underlying real values as observed
 at runtime).  If no hint is available (e.g., because data dependent shapes),
 if fallback is not None, use that instead (otherwise raise an error).</p>
diff --git a/main/generated/torch.fx.experimental.symbolic_shapes.is_accessor_node.html b/main/generated/torch.fx.experimental.symbolic_shapes.is_accessor_node.html
index 930ec3138ae..94612918a15 100644
--- a/main/generated/torch.fx.experimental.symbolic_shapes.is_accessor_node.html
+++ b/main/generated/torch.fx.experimental.symbolic_shapes.is_accessor_node.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.fx.experimental.symbolic_shapes.is_accessor_node<a class="headerlink" href="#torch-fx-experimental-symbolic-shapes-is-accessor-node" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.fx.experimental.symbolic_shapes.is_accessor_node">
-<span class="sig-prename descclassname"><span class="pre">torch.fx.experimental.symbolic_shapes.</span></span><span class="sig-name descname"><span class="pre">is_accessor_node</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">node</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#is_accessor_node"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/experimental/symbolic_shapes.py#L587"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.is_accessor_node" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.fx.experimental.symbolic_shapes.</span></span><span class="sig-name descname"><span class="pre">is_accessor_node</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">node</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#is_accessor_node"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/fx/experimental/symbolic_shapes.py#L587"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.is_accessor_node" title="Permalink to this definition">¶</a></dt>
 <dd><dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
 <dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)">bool</a></p>
diff --git a/main/generated/torch.fx.experimental.symbolic_shapes.is_concrete_bool.html b/main/generated/torch.fx.experimental.symbolic_shapes.is_concrete_bool.html
index 67d752b2b8f..4733e598857 100644
--- a/main/generated/torch.fx.experimental.symbolic_shapes.is_concrete_bool.html
+++ b/main/generated/torch.fx.experimental.symbolic_shapes.is_concrete_bool.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.fx.experimental.symbolic_shapes.is_concrete_bool<a class="headerlink" href="#torch-fx-experimental-symbolic-shapes-is-concrete-bool" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.fx.experimental.symbolic_shapes.is_concrete_bool">
-<span class="sig-prename descclassname"><span class="pre">torch.fx.experimental.symbolic_shapes.</span></span><span class="sig-name descname"><span class="pre">is_concrete_bool</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">a</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#is_concrete_bool"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/experimental/symbolic_shapes.py#L764"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.is_concrete_bool" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.fx.experimental.symbolic_shapes.</span></span><span class="sig-name descname"><span class="pre">is_concrete_bool</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">a</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#is_concrete_bool"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/fx/experimental/symbolic_shapes.py#L764"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.is_concrete_bool" title="Permalink to this definition">¶</a></dt>
 <dd><p>Utility to check if underlying object
 in SymBool is concrete value. Also returns
 true if integer is passed in.</p>
diff --git a/main/generated/torch.fx.experimental.symbolic_shapes.is_concrete_float.html b/main/generated/torch.fx.experimental.symbolic_shapes.is_concrete_float.html
index cd1429b8f9e..1d69562b990 100644
--- a/main/generated/torch.fx.experimental.symbolic_shapes.is_concrete_float.html
+++ b/main/generated/torch.fx.experimental.symbolic_shapes.is_concrete_float.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.fx.experimental.symbolic_shapes.is_concrete_float<a class="headerlink" href="#torch-fx-experimental-symbolic-shapes-is-concrete-float" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.fx.experimental.symbolic_shapes.is_concrete_float">
-<span class="sig-prename descclassname"><span class="pre">torch.fx.experimental.symbolic_shapes.</span></span><span class="sig-name descname"><span class="pre">is_concrete_float</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">a</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#is_concrete_float"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/experimental/symbolic_shapes.py#L374"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.is_concrete_float" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.fx.experimental.symbolic_shapes.</span></span><span class="sig-name descname"><span class="pre">is_concrete_float</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">a</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#is_concrete_float"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/fx/experimental/symbolic_shapes.py#L374"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.is_concrete_float" title="Permalink to this definition">¶</a></dt>
 <dd><p>Utility to check if underlying object
 in SymInt is concrete value. Also returns
 true if integer is passed in.</p>
diff --git a/main/generated/torch.fx.experimental.symbolic_shapes.is_concrete_int.html b/main/generated/torch.fx.experimental.symbolic_shapes.is_concrete_int.html
index 0821580aef4..05754a9afe9 100644
--- a/main/generated/torch.fx.experimental.symbolic_shapes.is_concrete_int.html
+++ b/main/generated/torch.fx.experimental.symbolic_shapes.is_concrete_int.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.fx.experimental.symbolic_shapes.is_concrete_int<a class="headerlink" href="#torch-fx-experimental-symbolic-shapes-is-concrete-int" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.fx.experimental.symbolic_shapes.is_concrete_int">
-<span class="sig-prename descclassname"><span class="pre">torch.fx.experimental.symbolic_shapes.</span></span><span class="sig-name descname"><span class="pre">is_concrete_int</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">a</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#is_concrete_int"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/experimental/symbolic_shapes.py#L354"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.is_concrete_int" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.fx.experimental.symbolic_shapes.</span></span><span class="sig-name descname"><span class="pre">is_concrete_int</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">a</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#is_concrete_int"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/fx/experimental/symbolic_shapes.py#L354"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.is_concrete_int" title="Permalink to this definition">¶</a></dt>
 <dd><p>Utility to check if underlying object
 in SymInt is concrete value. Also returns
 true if integer is passed in.</p>
diff --git a/main/generated/torch.fx.experimental.symbolic_shapes.lru_cache.html b/main/generated/torch.fx.experimental.symbolic_shapes.lru_cache.html
index 8cdc6383fef..be0397a09bd 100644
--- a/main/generated/torch.fx.experimental.symbolic_shapes.lru_cache.html
+++ b/main/generated/torch.fx.experimental.symbolic_shapes.lru_cache.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.fx.experimental.symbolic_shapes.lru_cache<a class="headerlink" href="#torch-fx-experimental-symbolic-shapes-lru-cache" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.fx.experimental.symbolic_shapes.lru_cache">
-<span class="sig-prename descclassname"><span class="pre">torch.fx.experimental.symbolic_shapes.</span></span><span class="sig-name descname"><span class="pre">lru_cache</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">maxsize</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#lru_cache"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/experimental/symbolic_shapes.py#L242"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.lru_cache" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.fx.experimental.symbolic_shapes.</span></span><span class="sig-name descname"><span class="pre">lru_cache</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">maxsize</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#lru_cache"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/fx/experimental/symbolic_shapes.py#L242"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.lru_cache" title="Permalink to this definition">¶</a></dt>
 <dd><dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
 <dd class="field-odd"><p>Callable[[Callable[…, _T]], functools._lru_cache_wrapper[_T]]</p>
diff --git a/main/generated/torch.fx.experimental.symbolic_shapes.rebind_unbacked.html b/main/generated/torch.fx.experimental.symbolic_shapes.rebind_unbacked.html
index 971856a3ced..fd5c7e63da8 100644
--- a/main/generated/torch.fx.experimental.symbolic_shapes.rebind_unbacked.html
+++ b/main/generated/torch.fx.experimental.symbolic_shapes.rebind_unbacked.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.fx.experimental.symbolic_shapes.rebind_unbacked<a class="headerlink" href="#torch-fx-experimental-symbolic-shapes-rebind-unbacked" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.fx.experimental.symbolic_shapes.rebind_unbacked">
-<span class="sig-prename descclassname"><span class="pre">torch.fx.experimental.symbolic_shapes.</span></span><span class="sig-name descname"><span class="pre">rebind_unbacked</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">shape_env</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">result</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#rebind_unbacked"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/experimental/symbolic_shapes.py#L466"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.rebind_unbacked" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.fx.experimental.symbolic_shapes.</span></span><span class="sig-name descname"><span class="pre">rebind_unbacked</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">shape_env</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">result</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#rebind_unbacked"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/fx/experimental/symbolic_shapes.py#L466"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.rebind_unbacked" title="Permalink to this definition">¶</a></dt>
 <dd><p>Suppose we are retracing a pre-existing FX graph that previously had
 fake tensor propagation (and therefore unbacked SymInts).  When we retrace,
 we re-propagate fake tensors, which results in new unbacked SymInts.
diff --git a/main/generated/torch.fx.experimental.symbolic_shapes.resolve_unbacked_bindings.html b/main/generated/torch.fx.experimental.symbolic_shapes.resolve_unbacked_bindings.html
index d6362d37f4e..a357b8eaa68 100644
--- a/main/generated/torch.fx.experimental.symbolic_shapes.resolve_unbacked_bindings.html
+++ b/main/generated/torch.fx.experimental.symbolic_shapes.resolve_unbacked_bindings.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.fx.experimental.symbolic_shapes.resolve_unbacked_bindings<a class="headerlink" href="#torch-fx-experimental-symbolic-shapes-resolve-unbacked-bindings" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
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-<span class="sig-prename descclassname"><span class="pre">torch.fx.experimental.symbolic_shapes.</span></span><span class="sig-name descname"><span class="pre">resolve_unbacked_bindings</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">shape_env</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bindings</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#resolve_unbacked_bindings"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/experimental/symbolic_shapes.py#L453"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.resolve_unbacked_bindings" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.fx.experimental.symbolic_shapes.</span></span><span class="sig-name descname"><span class="pre">resolve_unbacked_bindings</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">shape_env</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bindings</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#resolve_unbacked_bindings"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/fx/experimental/symbolic_shapes.py#L453"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.resolve_unbacked_bindings" title="Permalink to this definition">¶</a></dt>
 <dd><dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
 <dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Optional" title="(in Python v3.13)"><em>Optional</em></a>[<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.13)">dict</a>[sympy.core.symbol.Symbol, <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.13)">tuple</a>[torch.utils._pytree.KeyEntry, …]]]</p>
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 <dl class="py function">
 <dt class="sig sig-object py" id="torch.fx.experimental.symbolic_shapes.statically_known_true">
-<span class="sig-prename descclassname"><span class="pre">torch.fx.experimental.symbolic_shapes.</span></span><span class="sig-name descname"><span class="pre">statically_known_true</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#statically_known_true"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/experimental/symbolic_shapes.py#L1186"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.statically_known_true" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.fx.experimental.symbolic_shapes.</span></span><span class="sig-name descname"><span class="pre">statically_known_true</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#statically_known_true"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/fx/experimental/symbolic_shapes.py#L1186"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.statically_known_true" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns True if x can be simplified to a constant and is true.</p>
 <div class="admonition note">
 <p class="admonition-title">Note</p>
diff --git a/main/generated/torch.fx.experimental.symbolic_shapes.sym_eq.html b/main/generated/torch.fx.experimental.symbolic_shapes.sym_eq.html
index ebc918ec706..3ce7eee349f 100644
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+++ b/main/generated/torch.fx.experimental.symbolic_shapes.sym_eq.html
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 <h1>torch.fx.experimental.symbolic_shapes.sym_eq<a class="headerlink" href="#torch-fx-experimental-symbolic-shapes-sym-eq" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.fx.experimental.symbolic_shapes.sym_eq">
-<span class="sig-prename descclassname"><span class="pre">torch.fx.experimental.symbolic_shapes.</span></span><span class="sig-name descname"><span class="pre">sym_eq</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#sym_eq"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/fx/experimental/symbolic_shapes.py#L1211"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.sym_eq" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.fx.experimental.symbolic_shapes.</span></span><span class="sig-name descname"><span class="pre">sym_eq</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/fx/experimental/symbolic_shapes.html#sym_eq"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/fx/experimental/symbolic_shapes.py#L1211"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.fx.experimental.symbolic_shapes.sym_eq" title="Permalink to this definition">¶</a></dt>
 <dd><p>Like ==, but when run on list/tuple, it will recursively test equality
 and use sym_and to join the results together, without guarding.</p>
 <dl class="field-list simple">
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index d9b80b1353e..a9400e0b0ce 100644
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@@ -612,7 +612,7 @@
 <h1>torch.get_default_device<a class="headerlink" href="#torch-get-default-device" title="Permalink to this heading">¶</a></h1>
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 <dt class="sig sig-object py" id="torch.get_default_device">
-<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">get_default_device</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch.html#get_default_device"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/__init__.py#L1124"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.get_default_device" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">get_default_device</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch.html#get_default_device"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/__init__.py#L1124"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.get_default_device" title="Permalink to this definition">¶</a></dt>
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 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
diff --git a/main/generated/torch.get_default_dtype.html b/main/generated/torch.get_default_dtype.html
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 <h1>torch.get_deterministic_debug_mode<a class="headerlink" href="#torch-get-deterministic-debug-mode" title="Permalink to this heading">¶</a></h1>
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 <dt class="sig sig-object py" id="torch.get_deterministic_debug_mode">
-<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">get_deterministic_debug_mode</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch.html#get_deterministic_debug_mode"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/__init__.py#L1489"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.get_deterministic_debug_mode" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">get_deterministic_debug_mode</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch.html#get_deterministic_debug_mode"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/__init__.py#L1489"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.get_deterministic_debug_mode" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns the current value of the debug mode for deterministic
 operations. Refer to <a class="reference internal" href="torch.set_deterministic_debug_mode.html#torch.set_deterministic_debug_mode" title="torch.set_deterministic_debug_mode"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.set_deterministic_debug_mode()</span></code></a>
 documentation for more details.</p>
diff --git a/main/generated/torch.get_device_module.html b/main/generated/torch.get_device_module.html
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@@ -612,7 +612,7 @@
 <h1>torch.get_device_module<a class="headerlink" href="#torch-get-device-module" title="Permalink to this heading">¶</a></h1>
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 <dt class="sig sig-object py" id="torch.get_device_module">
-<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">get_device_module</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch.html#get_device_module"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/__init__.py#L2683"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.get_device_module" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">get_device_module</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch.html#get_device_module"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/__init__.py#L2683"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.get_device_module" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns the module associated with a given device(e.g., torch.device(‘cuda’), “mtia:0”, “xpu”, …).
 If no device is given, return the module for the current accelerator or CPU if none is present.</p>
 <dl class="field-list simple">
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@@ -612,7 +612,7 @@
 <h1>torch.get_float32_matmul_precision<a class="headerlink" href="#torch-get-float32-matmul-precision" title="Permalink to this heading">¶</a></h1>
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-<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">get_float32_matmul_precision</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch.html#get_float32_matmul_precision"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/__init__.py#L1504"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.get_float32_matmul_precision" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">get_float32_matmul_precision</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch.html#get_float32_matmul_precision"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/__init__.py#L1504"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.get_float32_matmul_precision" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns the current value of float32 matrix multiplication precision. Refer to
 <a class="reference internal" href="torch.set_float32_matmul_precision.html#torch.set_float32_matmul_precision" title="torch.set_float32_matmul_precision"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.set_float32_matmul_precision()</span></code></a> documentation for more details.</p>
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 <h1>torch.get_rng_state<a class="headerlink" href="#torch-get-rng-state" title="Permalink to this heading">¶</a></h1>
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-<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">get_rng_state</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/random.html#get_rng_state"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/random.py#L22"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.get_rng_state" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">get_rng_state</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/random.html#get_rng_state"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/random.py#L22"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.get_rng_state" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns the random number generator state as a <cite>torch.ByteTensor</cite>.</p>
 <div class="admonition note">
 <p class="admonition-title">Note</p>
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 <h1>torch.initial_seed<a class="headerlink" href="#torch-initial-seed" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.initial_seed">
-<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">initial_seed</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/random.html#initial_seed"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/random.py#L113"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.initial_seed" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">initial_seed</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/random.html#initial_seed"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/random.py#L113"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.initial_seed" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns the initial seed for generating random numbers as a
 Python <cite>long</cite>.</p>
 <div class="admonition note">
diff --git a/main/generated/torch.inner.html b/main/generated/torch.inner.html
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diff --git a/main/generated/torch.is_complex.html b/main/generated/torch.is_complex.html
index de6ec708278..1fec2a69d09 100644
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diff --git a/main/generated/torch.is_conj.html b/main/generated/torch.is_conj.html
index 440d9d08cf7..c03193639e7 100644
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diff --git a/main/generated/torch.is_deterministic_algorithms_warn_only_enabled.html b/main/generated/torch.is_deterministic_algorithms_warn_only_enabled.html
index e79a8958c21..88f402917b5 100644
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+++ b/main/generated/torch.is_deterministic_algorithms_warn_only_enabled.html
@@ -300,7 +300,7 @@
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 <h1>torch.is_deterministic_algorithms_warn_only_enabled<a class="headerlink" href="#torch-is-deterministic-algorithms-warn-only-enabled" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.is_deterministic_algorithms_warn_only_enabled">
-<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">is_deterministic_algorithms_warn_only_enabled</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch.html#is_deterministic_algorithms_warn_only_enabled"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/__init__.py#L1437"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.is_deterministic_algorithms_warn_only_enabled" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">is_deterministic_algorithms_warn_only_enabled</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch.html#is_deterministic_algorithms_warn_only_enabled"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/__init__.py#L1437"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.is_deterministic_algorithms_warn_only_enabled" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns True if the global deterministic flag is set to warn only.
 Refer to <a class="reference internal" href="torch.use_deterministic_algorithms.html#torch.use_deterministic_algorithms" title="torch.use_deterministic_algorithms"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.use_deterministic_algorithms()</span></code></a> documentation for more
 details.</p>
diff --git a/main/generated/torch.is_floating_point.html b/main/generated/torch.is_floating_point.html
index f405052ed8e..fd461dc3548 100644
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diff --git a/main/generated/torch.is_grad_enabled.html b/main/generated/torch.is_grad_enabled.html
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index 29889a3b505..676e5e99c2f 100644
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index 80dfc7c3ebe..691cfbc99dc 100644
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diff --git a/main/generated/torch.is_storage.html b/main/generated/torch.is_storage.html
index e260f519e0b..d1577f45802 100644
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@@ -612,7 +612,7 @@
 <h1>torch.is_storage<a class="headerlink" href="#torch-is-storage" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.is_storage">
-<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">is_storage</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">obj</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">/</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch.html#is_storage"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/__init__.py#L1112"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.is_storage" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">is_storage</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">obj</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">/</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch.html#is_storage"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/__init__.py#L1112"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.is_storage" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns True if <cite>obj</cite> is a PyTorch storage object.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.is_tensor.html b/main/generated/torch.is_tensor.html
index c50e44cf537..200950ee30f 100644
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 <h1>torch.is_tensor<a class="headerlink" href="#torch-is-tensor" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.is_tensor">
-<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">is_tensor</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">obj</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">/</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch.html#is_tensor"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/__init__.py#L1092"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.is_tensor" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">is_tensor</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">obj</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">/</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch.html#is_tensor"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/__init__.py#L1092"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.is_tensor" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns True if <cite>obj</cite> is a PyTorch tensor.</p>
 <p>Note that this function is simply doing <code class="docutils literal notranslate"><span class="pre">isinstance(obj,</span> <span class="pre">Tensor)</span></code>.
 Using that <code class="docutils literal notranslate"><span class="pre">isinstance</span></code> check is better for typechecking with mypy,
diff --git a/main/generated/torch.is_warn_always_enabled.html b/main/generated/torch.is_warn_always_enabled.html
index 97b7601848f..6a3c0984b2b 100644
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 <h1>torch.is_warn_always_enabled<a class="headerlink" href="#torch-is-warn-always-enabled" title="Permalink to this heading">¶</a></h1>
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 <dt class="sig sig-object py" id="torch.is_warn_always_enabled">
-<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">is_warn_always_enabled</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch.html#is_warn_always_enabled"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/__init__.py#L1590"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.is_warn_always_enabled" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">is_warn_always_enabled</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch.html#is_warn_always_enabled"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/__init__.py#L1590"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.is_warn_always_enabled" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns True if the global warn_always flag is turned on. Refer to
 <a class="reference internal" href="torch.set_warn_always.html#torch.set_warn_always" title="torch.set_warn_always"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.set_warn_always()</span></code></a> documentation for more details.</p>
 <dl class="field-list simple">
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index 17b2b2330e0..cdfeec407be 100644
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diff --git a/main/generated/torch.isfinite.html b/main/generated/torch.isfinite.html
index 5afe942649d..599d1dc54bc 100644
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+++ b/main/generated/torch.isposinf.html
@@ -300,7 +300,7 @@
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     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.isreal.html b/main/generated/torch.isreal.html
index af662339f41..6fed7b79488 100644
--- a/main/generated/torch.isreal.html
+++ b/main/generated/torch.isreal.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.istft.html b/main/generated/torch.istft.html
index ed3f655313d..30ec2a41b5f 100644
--- a/main/generated/torch.istft.html
+++ b/main/generated/torch.istft.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.jit.Attribute.html b/main/generated/torch.jit.Attribute.html
index 1c594029828..34c4b1df85c 100644
--- a/main/generated/torch.jit.Attribute.html
+++ b/main/generated/torch.jit.Attribute.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.jit.ScriptFunction.html b/main/generated/torch.jit.ScriptFunction.html
index aa6ac3383ef..5ae042ce1fa 100644
--- a/main/generated/torch.jit.ScriptFunction.html
+++ b/main/generated/torch.jit.ScriptFunction.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.jit.ScriptModule.html b/main/generated/torch.jit.ScriptModule.html
index 0a7fc5ae775..1f60250ec39 100644
--- a/main/generated/torch.jit.ScriptModule.html
+++ b/main/generated/torch.jit.ScriptModule.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>ScriptModule<a class="headerlink" href="#scriptmodule" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.jit.ScriptModule">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.jit.</span></span><span class="sig-name descname"><span class="pre">ScriptModule</span></span><a class="reference internal" href="../_modules/torch/jit/_script.html#ScriptModule"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/jit/_script.py#L511"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.ScriptModule" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.jit.</span></span><span class="sig-name descname"><span class="pre">ScriptModule</span></span><a class="reference internal" href="../_modules/torch/jit/_script.html#ScriptModule"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/jit/_script.py#L511"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.ScriptModule" title="Permalink to this definition">¶</a></dt>
 <dd><p>Wrapper for C++ torch::jit::Module with methods, attributes, and parameters.</p>
 <p>A wrapper around C++ <code class="docutils literal notranslate"><span class="pre">torch::jit::Module</span></code>. <code class="docutils literal notranslate"><span class="pre">ScriptModule</span></code>s
 contain methods, attributes, parameters, and
@@ -621,7 +621,7 @@ <h1>ScriptModule<a class="headerlink" href="#scriptmodule" title="Permalink to t
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.jit.ScriptModule.add_module">
-<span class="sig-name descname"><span class="pre">add_module</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">module</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L622"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.ScriptModule.add_module" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">add_module</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">module</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/module.py#L622"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.ScriptModule.add_module" title="Permalink to this definition">¶</a></dt>
 <dd><p>Add a child module to the current module.</p>
 <p>The module can be accessed as an attribute using the given name.</p>
 <dl class="field-list simple">
@@ -637,7 +637,7 @@ <h1>ScriptModule<a class="headerlink" href="#scriptmodule" title="Permalink to t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.jit.ScriptModule.apply">
-<span class="sig-name descname"><span class="pre">apply</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">fn</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L1005"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.ScriptModule.apply" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">apply</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">fn</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/module.py#L1005"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.ScriptModule.apply" title="Permalink to this definition">¶</a></dt>
 <dd><p>Apply <code class="docutils literal notranslate"><span class="pre">fn</span></code> recursively to every submodule (as returned by <code class="docutils literal notranslate"><span class="pre">.children()</span></code>) as well as self.</p>
 <p>Typical use includes initializing the parameters of a model
 (see also <a class="reference internal" href="../nn.init.html#nn-init-doc"><span class="std std-ref">torch.nn.init</span></a>).</p>
@@ -679,7 +679,7 @@ <h1>ScriptModule<a class="headerlink" href="#scriptmodule" title="Permalink to t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.jit.ScriptModule.bfloat16">
-<span class="sig-name descname"><span class="pre">bfloat16</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L1180"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.ScriptModule.bfloat16" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">bfloat16</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/module.py#L1180"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.ScriptModule.bfloat16" title="Permalink to this definition">¶</a></dt>
 <dd><p>Casts all floating point parameters and buffers to <code class="docutils literal notranslate"><span class="pre">bfloat16</span></code> datatype.</p>
 <div class="admonition note">
 <p class="admonition-title">Note</p>
@@ -697,7 +697,7 @@ <h1>ScriptModule<a class="headerlink" href="#scriptmodule" title="Permalink to t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.jit.ScriptModule.buffers">
-<span class="sig-name descname"><span class="pre">buffers</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">recurse</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L2675"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.ScriptModule.buffers" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">buffers</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">recurse</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/module.py#L2675"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.ScriptModule.buffers" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return an iterator over module buffers.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -723,7 +723,7 @@ <h1>ScriptModule<a class="headerlink" href="#scriptmodule" title="Permalink to t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.jit.ScriptModule.children">
-<span class="sig-name descname"><span class="pre">children</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L2729"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.ScriptModule.children" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">children</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/module.py#L2729"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.ScriptModule.children" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return an iterator over immediate children modules.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Yields</dt>
@@ -756,7 +756,7 @@ <h1>ScriptModule<a class="headerlink" href="#scriptmodule" title="Permalink to t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.jit.ScriptModule.compile">
-<span class="sig-name descname"><span class="pre">compile</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L2992"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.ScriptModule.compile" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">compile</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/module.py#L2992"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.ScriptModule.compile" title="Permalink to this definition">¶</a></dt>
 <dd><p>Compile this Module’s forward using <a class="reference internal" href="torch.compile.html#torch.compile" title="torch.compile"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.compile()</span></code></a>.</p>
 <p>This Module’s <cite>__call__</cite> method is compiled and all arguments are passed as-is
 to <a class="reference internal" href="torch.compile.html#torch.compile" title="torch.compile"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.compile()</span></code></a>.</p>
@@ -765,7 +765,7 @@ <h1>ScriptModule<a class="headerlink" href="#scriptmodule" title="Permalink to t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.jit.ScriptModule.cpu">
-<span class="sig-name descname"><span class="pre">cpu</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L1122"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.ScriptModule.cpu" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">cpu</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/module.py#L1122"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.ScriptModule.cpu" title="Permalink to this definition">¶</a></dt>
 <dd><p>Move all model parameters and buffers to the CPU.</p>
 <div class="admonition note">
 <p class="admonition-title">Note</p>
@@ -783,7 +783,7 @@ <h1>ScriptModule<a class="headerlink" href="#scriptmodule" title="Permalink to t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.jit.ScriptModule.cuda">
-<span class="sig-name descname"><span class="pre">cuda</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L1046"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.ScriptModule.cuda" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">cuda</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/module.py#L1046"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.ScriptModule.cuda" title="Permalink to this definition">¶</a></dt>
 <dd><p>Move all model parameters and buffers to the GPU.</p>
 <p>This also makes associated parameters and buffers different objects. So
 it should be called before constructing the optimizer if the module will
@@ -808,7 +808,7 @@ <h1>ScriptModule<a class="headerlink" href="#scriptmodule" title="Permalink to t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.jit.ScriptModule.double">
-<span class="sig-name descname"><span class="pre">double</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L1158"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.ScriptModule.double" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">double</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/module.py#L1158"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.ScriptModule.double" title="Permalink to this definition">¶</a></dt>
 <dd><p>Casts all floating point parameters and buffers to <code class="docutils literal notranslate"><span class="pre">double</span></code> datatype.</p>
 <div class="admonition note">
 <p class="admonition-title">Note</p>
@@ -826,7 +826,7 @@ <h1>ScriptModule<a class="headerlink" href="#scriptmodule" title="Permalink to t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.jit.ScriptModule.eval">
-<span class="sig-name descname"><span class="pre">eval</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L2856"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.ScriptModule.eval" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">eval</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/module.py#L2856"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.ScriptModule.eval" title="Permalink to this definition">¶</a></dt>
 <dd><p>Set the module in evaluation mode.</p>
 <p>This has an effect only on certain modules. See the documentation of
 particular modules for details of their behaviors in training/evaluation
@@ -847,7 +847,7 @@ <h1>ScriptModule<a class="headerlink" href="#scriptmodule" title="Permalink to t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.jit.ScriptModule.extra_repr">
-<span class="sig-name descname"><span class="pre">extra_repr</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L2932"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.ScriptModule.extra_repr" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">extra_repr</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/module.py#L2932"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.ScriptModule.extra_repr" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the extra representation of the module.</p>
 <p>To print customized extra information, you should re-implement
 this method in your own modules. Both single-line and multi-line
@@ -861,7 +861,7 @@ <h1>ScriptModule<a class="headerlink" href="#scriptmodule" title="Permalink to t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.jit.ScriptModule.float">
-<span class="sig-name descname"><span class="pre">float</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L1147"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.ScriptModule.float" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">float</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/module.py#L1147"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.ScriptModule.float" title="Permalink to this definition">¶</a></dt>
 <dd><p>Casts all floating point parameters and buffers to <code class="docutils literal notranslate"><span class="pre">float</span></code> datatype.</p>
 <div class="admonition note">
 <p class="admonition-title">Note</p>
@@ -879,7 +879,7 @@ <h1>ScriptModule<a class="headerlink" href="#scriptmodule" title="Permalink to t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.jit.ScriptModule.get_buffer">
-<span class="sig-name descname"><span class="pre">get_buffer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">target</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L836"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.ScriptModule.get_buffer" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">get_buffer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">target</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/module.py#L836"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.ScriptModule.get_buffer" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the buffer given by <code class="docutils literal notranslate"><span class="pre">target</span></code> if it exists, otherwise throw an error.</p>
 <p>See the docstring for <code class="docutils literal notranslate"><span class="pre">get_submodule</span></code> for a more detailed
 explanation of this method’s functionality as well as how to
@@ -906,7 +906,7 @@ <h1>ScriptModule<a class="headerlink" href="#scriptmodule" title="Permalink to t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.jit.ScriptModule.get_extra_state">
-<span class="sig-name descname"><span class="pre">get_extra_state</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L872"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.ScriptModule.get_extra_state" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">get_extra_state</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/module.py#L872"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.ScriptModule.get_extra_state" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return any extra state to include in the module’s state_dict.</p>
 <p>Implement this and a corresponding <a class="reference internal" href="#torch.jit.ScriptModule.set_extra_state" title="torch.jit.ScriptModule.set_extra_state"><code class="xref py py-func docutils literal notranslate"><span class="pre">set_extra_state()</span></code></a> for your module
 if you need to store extra state. This function is called when building the
@@ -927,7 +927,7 @@ <h1>ScriptModule<a class="headerlink" href="#scriptmodule" title="Permalink to t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.jit.ScriptModule.get_parameter">
-<span class="sig-name descname"><span class="pre">get_parameter</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">target</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L800"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.ScriptModule.get_parameter" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">get_parameter</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">target</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/module.py#L800"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.ScriptModule.get_parameter" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the parameter given by <code class="docutils literal notranslate"><span class="pre">target</span></code> if it exists, otherwise throw an error.</p>
 <p>See the docstring for <code class="docutils literal notranslate"><span class="pre">get_submodule</span></code> for a more detailed
 explanation of this method’s functionality as well as how to
@@ -954,7 +954,7 @@ <h1>ScriptModule<a class="headerlink" href="#scriptmodule" title="Permalink to t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.jit.ScriptModule.get_submodule">
-<span class="sig-name descname"><span class="pre">get_submodule</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">target</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L654"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.ScriptModule.get_submodule" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">get_submodule</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">target</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/module.py#L654"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.ScriptModule.get_submodule" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the submodule given by <code class="docutils literal notranslate"><span class="pre">target</span></code> if it exists, otherwise throw an error.</p>
 <p>For example, let’s say you have an <code class="docutils literal notranslate"><span class="pre">nn.Module</span></code> <code class="docutils literal notranslate"><span class="pre">A</span></code> that
 looks like this:</p>
@@ -1010,7 +1010,7 @@ <h1>ScriptModule<a class="headerlink" href="#scriptmodule" title="Permalink to t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.jit.ScriptModule.half">
-<span class="sig-name descname"><span class="pre">half</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L1169"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.ScriptModule.half" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">half</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/module.py#L1169"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.ScriptModule.half" title="Permalink to this definition">¶</a></dt>
 <dd><p>Casts all floating point parameters and buffers to <code class="docutils literal notranslate"><span class="pre">half</span></code> datatype.</p>
 <div class="admonition note">
 <p class="admonition-title">Note</p>
@@ -1036,7 +1036,7 @@ <h1>ScriptModule<a class="headerlink" href="#scriptmodule" title="Permalink to t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.jit.ScriptModule.ipu">
-<span class="sig-name descname"><span class="pre">ipu</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L1065"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.ScriptModule.ipu" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">ipu</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/module.py#L1065"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.ScriptModule.ipu" title="Permalink to this definition">¶</a></dt>
 <dd><p>Move all model parameters and buffers to the IPU.</p>
 <p>This also makes associated parameters and buffers different objects. So
 it should be called before constructing the optimizer if the module will
@@ -1061,7 +1061,7 @@ <h1>ScriptModule<a class="headerlink" href="#scriptmodule" title="Permalink to t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.jit.ScriptModule.load_state_dict">
-<span class="sig-name descname"><span class="pre">load_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state_dict</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">strict</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">assign</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L2483"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.ScriptModule.load_state_dict" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">load_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state_dict</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">strict</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">assign</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/module.py#L2483"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.ScriptModule.load_state_dict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Copy parameters and buffers from <a class="reference internal" href="#torch.jit.ScriptModule.state_dict" title="torch.jit.ScriptModule.state_dict"><code class="xref py py-attr docutils literal notranslate"><span class="pre">state_dict</span></code></a> into this module and its descendants.</p>
 <p>If <code class="xref py py-attr docutils literal notranslate"><span class="pre">strict</span></code> is <code class="docutils literal notranslate"><span class="pre">True</span></code>, then
 the keys of <a class="reference internal" href="#torch.jit.ScriptModule.state_dict" title="torch.jit.ScriptModule.state_dict"><code class="xref py py-attr docutils literal notranslate"><span class="pre">state_dict</span></code></a> must exactly match the keys returned
@@ -1116,7 +1116,7 @@ <h1>ScriptModule<a class="headerlink" href="#scriptmodule" title="Permalink to t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.jit.ScriptModule.modules">
-<span class="sig-name descname"><span class="pre">modules</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L2758"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.ScriptModule.modules" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">modules</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/module.py#L2758"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.ScriptModule.modules" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return an iterator over all modules in the network.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Yields</dt>
@@ -1148,7 +1148,7 @@ <h1>ScriptModule<a class="headerlink" href="#scriptmodule" title="Permalink to t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.jit.ScriptModule.mtia">
-<span class="sig-name descname"><span class="pre">mtia</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L1103"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.ScriptModule.mtia" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">mtia</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/module.py#L1103"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.ScriptModule.mtia" title="Permalink to this definition">¶</a></dt>
 <dd><p>Move all model parameters and buffers to the MTIA.</p>
 <p>This also makes associated parameters and buffers different objects. So
 it should be called before constructing the optimizer if the module will
@@ -1173,7 +1173,7 @@ <h1>ScriptModule<a class="headerlink" href="#scriptmodule" title="Permalink to t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.jit.ScriptModule.named_buffers">
-<span class="sig-name descname"><span class="pre">named_buffers</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">prefix</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">recurse</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">remove_duplicate</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L2698"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.ScriptModule.named_buffers" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">named_buffers</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">prefix</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">recurse</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">remove_duplicate</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/module.py#L2698"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.ScriptModule.named_buffers" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return an iterator over module buffers, yielding both the name of the buffer as well as the buffer itself.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -1202,7 +1202,7 @@ <h1>ScriptModule<a class="headerlink" href="#scriptmodule" title="Permalink to t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.jit.ScriptModule.named_children">
-<span class="sig-name descname"><span class="pre">named_children</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L2738"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.ScriptModule.named_children" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">named_children</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/module.py#L2738"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.ScriptModule.named_children" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return an iterator over immediate children modules, yielding both the name of the module as well as the module itself.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Yields</dt>
@@ -1222,7 +1222,7 @@ <h1>ScriptModule<a class="headerlink" href="#scriptmodule" title="Permalink to t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.jit.ScriptModule.named_modules">
-<span class="sig-name descname"><span class="pre">named_modules</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">memo</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prefix</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">remove_duplicate</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L2785"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.ScriptModule.named_modules" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">named_modules</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">memo</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prefix</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">remove_duplicate</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/module.py#L2785"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.ScriptModule.named_modules" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return an iterator over all modules in the network, yielding both the name of the module as well as the module itself.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -1259,7 +1259,7 @@ <h1>ScriptModule<a class="headerlink" href="#scriptmodule" title="Permalink to t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.jit.ScriptModule.named_parameters">
-<span class="sig-name descname"><span class="pre">named_parameters</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">prefix</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">recurse</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">remove_duplicate</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L2643"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.ScriptModule.named_parameters" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">named_parameters</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">prefix</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">recurse</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">remove_duplicate</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/module.py#L2643"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.ScriptModule.named_parameters" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return an iterator over module parameters, yielding both the name of the parameter as well as the parameter itself.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -1289,7 +1289,7 @@ <h1>ScriptModule<a class="headerlink" href="#scriptmodule" title="Permalink to t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.jit.ScriptModule.parameters">
-<span class="sig-name descname"><span class="pre">parameters</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">recurse</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L2618"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.ScriptModule.parameters" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">parameters</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">recurse</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/module.py#L2618"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.ScriptModule.parameters" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return an iterator over module parameters.</p>
 <p>This is typically passed to an optimizer.</p>
 <dl class="field-list simple">
@@ -1316,7 +1316,7 @@ <h1>ScriptModule<a class="headerlink" href="#scriptmodule" title="Permalink to t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.jit.ScriptModule.register_backward_hook">
-<span class="sig-name descname"><span class="pre">register_backward_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L1404"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.ScriptModule.register_backward_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_backward_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/module.py#L1404"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.ScriptModule.register_backward_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register a backward hook on the module.</p>
 <p>This function is deprecated in favor of <a class="reference internal" href="torch.nn.Module.html#torch.nn.Module.register_full_backward_hook" title="torch.nn.Module.register_full_backward_hook"><code class="xref py py-meth docutils literal notranslate"><span class="pre">register_full_backward_hook()</span></code></a> and
 the behavior of this function will change in future versions.</p>
@@ -1333,7 +1333,7 @@ <h1>ScriptModule<a class="headerlink" href="#scriptmodule" title="Permalink to t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.jit.ScriptModule.register_buffer">
-<span class="sig-name descname"><span class="pre">register_buffer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">persistent</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L510"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.ScriptModule.register_buffer" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_buffer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">persistent</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/module.py#L510"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.ScriptModule.register_buffer" title="Permalink to this definition">¶</a></dt>
 <dd><p>Add a buffer to the module.</p>
 <p>This is typically used to register a buffer that should not to be
 considered a model parameter. For example, BatchNorm’s <code class="docutils literal notranslate"><span class="pre">running_mean</span></code>
@@ -1365,7 +1365,7 @@ <h1>ScriptModule<a class="headerlink" href="#scriptmodule" title="Permalink to t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.jit.ScriptModule.register_forward_hook">
-<span class="sig-name descname"><span class="pre">register_forward_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">with_kwargs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">always_call</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L1656"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.ScriptModule.register_forward_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_forward_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">with_kwargs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">always_call</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/module.py#L1656"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.ScriptModule.register_forward_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register a forward hook on the module.</p>
 <p>The hook will be called every time after <code class="xref py py-func docutils literal notranslate"><span class="pre">forward()</span></code> has computed an output.</p>
 <p>If <code class="docutils literal notranslate"><span class="pre">with_kwargs</span></code> is <code class="docutils literal notranslate"><span class="pre">False</span></code> or not specified, the input contains only
@@ -1416,7 +1416,7 @@ <h1>ScriptModule<a class="headerlink" href="#scriptmodule" title="Permalink to t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.jit.ScriptModule.register_forward_pre_hook">
-<span class="sig-name descname"><span class="pre">register_forward_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">with_kwargs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L1590"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.ScriptModule.register_forward_pre_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_forward_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">with_kwargs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/module.py#L1590"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.ScriptModule.register_forward_pre_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register a forward pre-hook on the module.</p>
 <p>The hook will be called every time before <code class="xref py py-func docutils literal notranslate"><span class="pre">forward()</span></code> is invoked.</p>
 <p>If <code class="docutils literal notranslate"><span class="pre">with_kwargs</span></code> is false or not specified, the input contains only
@@ -1466,7 +1466,7 @@ <h1>ScriptModule<a class="headerlink" href="#scriptmodule" title="Permalink to t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.jit.ScriptModule.register_full_backward_hook">
-<span class="sig-name descname"><span class="pre">register_full_backward_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L1430"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.ScriptModule.register_full_backward_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_full_backward_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/module.py#L1430"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.ScriptModule.register_full_backward_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register a backward hook on the module.</p>
 <p>The hook will be called every time the gradients with respect to a module
 are computed, i.e. the hook will execute if and only if the gradients with
@@ -1517,7 +1517,7 @@ <h1>ScriptModule<a class="headerlink" href="#scriptmodule" title="Permalink to t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.jit.ScriptModule.register_full_backward_pre_hook">
-<span class="sig-name descname"><span class="pre">register_full_backward_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L1355"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.ScriptModule.register_full_backward_pre_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_full_backward_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/module.py#L1355"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.ScriptModule.register_full_backward_pre_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register a backward pre-hook on the module.</p>
 <p>The hook will be called every time the gradients for the module are computed.
 The hook should have the following signature:</p>
@@ -1563,7 +1563,7 @@ <h1>ScriptModule<a class="headerlink" href="#scriptmodule" title="Permalink to t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.jit.ScriptModule.register_load_state_dict_post_hook">
-<span class="sig-name descname"><span class="pre">register_load_state_dict_post_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L2273"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.ScriptModule.register_load_state_dict_post_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_load_state_dict_post_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/module.py#L2273"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.ScriptModule.register_load_state_dict_post_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register a post-hook to be run after module’s <code class="xref py py-meth docutils literal notranslate"><span class="pre">load_state_dict()</span></code> is called.</p>
 <dl class="simple">
 <dt>It should have the following signature::</dt><dd><p>hook(module, incompatible_keys) -&gt; None</p>
@@ -1593,7 +1593,7 @@ <h1>ScriptModule<a class="headerlink" href="#scriptmodule" title="Permalink to t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.jit.ScriptModule.register_load_state_dict_pre_hook">
-<span class="sig-name descname"><span class="pre">register_load_state_dict_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L2261"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.ScriptModule.register_load_state_dict_pre_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_load_state_dict_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/module.py#L2261"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.ScriptModule.register_load_state_dict_pre_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register a pre-hook to be run before module’s <code class="xref py py-meth docutils literal notranslate"><span class="pre">load_state_dict()</span></code> is called.</p>
 <dl class="simple">
 <dt>It should have the following signature::</dt><dd><p>hook(module, state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs) -&gt; None  # noqa: B950</p>
@@ -1609,7 +1609,7 @@ <h1>ScriptModule<a class="headerlink" href="#scriptmodule" title="Permalink to t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.jit.ScriptModule.register_module">
-<span class="sig-name descname"><span class="pre">register_module</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">module</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L650"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.ScriptModule.register_module" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_module</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">module</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/module.py#L650"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.ScriptModule.register_module" title="Permalink to this definition">¶</a></dt>
 <dd><p>Alias for <a class="reference internal" href="#torch.jit.ScriptModule.add_module" title="torch.jit.ScriptModule.add_module"><code class="xref py py-func docutils literal notranslate"><span class="pre">add_module()</span></code></a>.</p>
 <dl class="field-list simple">
 </dl>
@@ -1617,7 +1617,7 @@ <h1>ScriptModule<a class="headerlink" href="#scriptmodule" title="Permalink to t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.jit.ScriptModule.register_parameter">
-<span class="sig-name descname"><span class="pre">register_parameter</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">param</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L572"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.ScriptModule.register_parameter" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_parameter</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">param</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/module.py#L572"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.ScriptModule.register_parameter" title="Permalink to this definition">¶</a></dt>
 <dd><p>Add a parameter to the module.</p>
 <p>The parameter can be accessed as an attribute using given name.</p>
 <dl class="field-list simple">
@@ -1636,7 +1636,7 @@ <h1>ScriptModule<a class="headerlink" href="#scriptmodule" title="Permalink to t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.jit.ScriptModule.register_state_dict_post_hook">
-<span class="sig-name descname"><span class="pre">register_state_dict_post_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L2071"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.ScriptModule.register_state_dict_post_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_state_dict_post_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/module.py#L2071"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.ScriptModule.register_state_dict_post_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register a post-hook for the <a class="reference internal" href="torch.nn.Module.html#torch.nn.Module.state_dict" title="torch.nn.Module.state_dict"><code class="xref py py-meth docutils literal notranslate"><span class="pre">state_dict()</span></code></a> method.</p>
 <dl class="simple">
 <dt>It should have the following signature::</dt><dd><p>hook(module, state_dict, prefix, local_metadata) -&gt; None</p>
@@ -1647,7 +1647,7 @@ <h1>ScriptModule<a class="headerlink" href="#scriptmodule" title="Permalink to t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.jit.ScriptModule.register_state_dict_pre_hook">
-<span class="sig-name descname"><span class="pre">register_state_dict_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L2095"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.ScriptModule.register_state_dict_pre_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_state_dict_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/module.py#L2095"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.ScriptModule.register_state_dict_pre_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register a pre-hook for the <a class="reference internal" href="torch.nn.Module.html#torch.nn.Module.state_dict" title="torch.nn.Module.state_dict"><code class="xref py py-meth docutils literal notranslate"><span class="pre">state_dict()</span></code></a> method.</p>
 <dl class="simple">
 <dt>It should have the following signature::</dt><dd><p>hook(module, prefix, keep_vars) -&gt; None</p>
@@ -1659,7 +1659,7 @@ <h1>ScriptModule<a class="headerlink" href="#scriptmodule" title="Permalink to t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.jit.ScriptModule.requires_grad_">
-<span class="sig-name descname"><span class="pre">requires_grad_</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">requires_grad</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L2874"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.ScriptModule.requires_grad_" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">requires_grad_</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">requires_grad</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/module.py#L2874"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.ScriptModule.requires_grad_" title="Permalink to this definition">¶</a></dt>
 <dd><p>Change if autograd should record operations on parameters in this module.</p>
 <p>This method sets the parameters’ <code class="xref py py-attr docutils literal notranslate"><span class="pre">requires_grad</span></code> attributes
 in-place.</p>
@@ -1683,7 +1683,7 @@ <h1>ScriptModule<a class="headerlink" href="#scriptmodule" title="Permalink to t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.jit.ScriptModule.save">
-<span class="sig-name descname"><span class="pre">save</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">f</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/jit/_script.py#L746"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.ScriptModule.save" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">save</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">f</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/jit/_script.py#L746"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.ScriptModule.save" title="Permalink to this definition">¶</a></dt>
 <dd><p>Save with a file-like object.</p>
 <p>save(f, _extra_files={})</p>
 <p>See <a class="reference internal" href="torch.jit.save.html#torch.jit.save" title="torch.jit.save"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.jit.save</span></code></a> which accepts a file-like object.
@@ -1693,7 +1693,7 @@ <h1>ScriptModule<a class="headerlink" href="#scriptmodule" title="Permalink to t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.jit.ScriptModule.set_extra_state">
-<span class="sig-name descname"><span class="pre">set_extra_state</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L893"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.ScriptModule.set_extra_state" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">set_extra_state</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/module.py#L893"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.ScriptModule.set_extra_state" title="Permalink to this definition">¶</a></dt>
 <dd><p>Set extra state contained in the loaded <cite>state_dict</cite>.</p>
 <p>This function is called from <a class="reference internal" href="#torch.jit.ScriptModule.load_state_dict" title="torch.jit.ScriptModule.load_state_dict"><code class="xref py py-func docutils literal notranslate"><span class="pre">load_state_dict()</span></code></a> to handle any extra state
 found within the <cite>state_dict</cite>. Implement this function and a corresponding
@@ -1708,7 +1708,7 @@ <h1>ScriptModule<a class="headerlink" href="#scriptmodule" title="Permalink to t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.jit.ScriptModule.set_submodule">
-<span class="sig-name descname"><span class="pre">set_submodule</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">target</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">strict</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L719"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.ScriptModule.set_submodule" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">set_submodule</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">target</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">strict</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/module.py#L719"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.ScriptModule.set_submodule" title="Permalink to this definition">¶</a></dt>
 <dd><p>Set the submodule given by <code class="docutils literal notranslate"><span class="pre">target</span></code> if it exists, otherwise throw an error.</p>
 <div class="admonition note">
 <p class="admonition-title">Note</p>
@@ -1766,7 +1766,7 @@ <h1>ScriptModule<a class="headerlink" href="#scriptmodule" title="Permalink to t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.jit.ScriptModule.share_memory">
-<span class="sig-name descname"><span class="pre">share_memory</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L2925"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.ScriptModule.share_memory" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">share_memory</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/module.py#L2925"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.ScriptModule.share_memory" title="Permalink to this definition">¶</a></dt>
 <dd><p>See <a class="reference internal" href="torch.Tensor.share_memory_.html#torch.Tensor.share_memory_" title="torch.Tensor.share_memory_"><code class="xref py py-meth docutils literal notranslate"><span class="pre">torch.Tensor.share_memory_()</span></code></a>.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -1777,7 +1777,7 @@ <h1>ScriptModule<a class="headerlink" href="#scriptmodule" title="Permalink to t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.jit.ScriptModule.state_dict">
-<span class="sig-name descname"><span class="pre">state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">destination</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prefix</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">keep_vars</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L2152"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.ScriptModule.state_dict" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">destination</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prefix</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">keep_vars</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/module.py#L2152"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.ScriptModule.state_dict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return a dictionary containing references to the whole state of the module.</p>
 <p>Both parameters and persistent buffers (e.g. running averages) are
 included. Keys are corresponding parameter and buffer names.
@@ -1830,27 +1830,27 @@ <h1>ScriptModule<a class="headerlink" href="#scriptmodule" title="Permalink to t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.jit.ScriptModule.to">
-<span class="sig-name descname"><span class="pre">to</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L1226"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.ScriptModule.to" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">to</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/module.py#L1226"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.ScriptModule.to" title="Permalink to this definition">¶</a></dt>
 <dd><p>Move and/or cast the parameters and buffers.</p>
 <p>This can be called as</p>
 <dl class="py function">
 <dt class="sig sig-object py">
-<span class="sig-name descname"><span class="pre">to</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">non_blocking</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L1226"><span class="viewcode-link"><span class="pre">[source]</span></span></a></dt>
+<span class="sig-name descname"><span class="pre">to</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">non_blocking</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/module.py#L1226"><span class="viewcode-link"><span class="pre">[source]</span></span></a></dt>
 <dd></dd></dl>
 
 <dl class="py function">
 <dt class="sig sig-object py">
-<span class="sig-name descname"><span class="pre">to</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dtype</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">non_blocking</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L1226"><span class="viewcode-link"><span class="pre">[source]</span></span></a></dt>
+<span class="sig-name descname"><span class="pre">to</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dtype</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">non_blocking</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/module.py#L1226"><span class="viewcode-link"><span class="pre">[source]</span></span></a></dt>
 <dd></dd></dl>
 
 <dl class="py function">
 <dt class="sig sig-object py">
-<span class="sig-name descname"><span class="pre">to</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">non_blocking</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L1226"><span class="viewcode-link"><span class="pre">[source]</span></span></a></dt>
+<span class="sig-name descname"><span class="pre">to</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">non_blocking</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/module.py#L1226"><span class="viewcode-link"><span class="pre">[source]</span></span></a></dt>
 <dd></dd></dl>
 
 <dl class="py function">
 <dt class="sig sig-object py">
-<span class="sig-name descname"><span class="pre">to</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">memory_format</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.channels_last</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L1226"><span class="viewcode-link"><span class="pre">[source]</span></span></a></dt>
+<span class="sig-name descname"><span class="pre">to</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">memory_format</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.channels_last</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/module.py#L1226"><span class="viewcode-link"><span class="pre">[source]</span></span></a></dt>
 <dd></dd></dl>
 
 <p>Its signature is similar to <a class="reference internal" href="torch.Tensor.to.html#torch.Tensor.to" title="torch.Tensor.to"><code class="xref py py-meth docutils literal notranslate"><span class="pre">torch.Tensor.to()</span></code></a>, but only accepts
@@ -1929,7 +1929,7 @@ <h1>ScriptModule<a class="headerlink" href="#scriptmodule" title="Permalink to t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.jit.ScriptModule.to_empty">
-<span class="sig-name descname"><span class="pre">to_empty</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">recurse</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L1191"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.ScriptModule.to_empty" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">to_empty</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">recurse</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/module.py#L1191"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.ScriptModule.to_empty" title="Permalink to this definition">¶</a></dt>
 <dd><p>Move the parameters and buffers to the specified device without copying storage.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -1951,7 +1951,7 @@ <h1>ScriptModule<a class="headerlink" href="#scriptmodule" title="Permalink to t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.jit.ScriptModule.train">
-<span class="sig-name descname"><span class="pre">train</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L2834"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.ScriptModule.train" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">train</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/module.py#L2834"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.ScriptModule.train" title="Permalink to this definition">¶</a></dt>
 <dd><p>Set the module in training mode.</p>
 <p>This has an effect only on certain modules. See the documentation of
 particular modules for details of their behaviors in training/evaluation
@@ -1973,7 +1973,7 @@ <h1>ScriptModule<a class="headerlink" href="#scriptmodule" title="Permalink to t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.jit.ScriptModule.type">
-<span class="sig-name descname"><span class="pre">type</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dst_type</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L1133"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.ScriptModule.type" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">type</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dst_type</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/module.py#L1133"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.ScriptModule.type" title="Permalink to this definition">¶</a></dt>
 <dd><p>Casts all parameters and buffers to <code class="xref py py-attr docutils literal notranslate"><span class="pre">dst_type</span></code>.</p>
 <div class="admonition note">
 <p class="admonition-title">Note</p>
@@ -1994,7 +1994,7 @@ <h1>ScriptModule<a class="headerlink" href="#scriptmodule" title="Permalink to t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.jit.ScriptModule.xpu">
-<span class="sig-name descname"><span class="pre">xpu</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L1084"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.ScriptModule.xpu" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">xpu</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/module.py#L1084"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.ScriptModule.xpu" title="Permalink to this definition">¶</a></dt>
 <dd><p>Move all model parameters and buffers to the XPU.</p>
 <p>This also makes associated parameters and buffers different objects. So
 it should be called before constructing optimizer if the module will
@@ -2019,7 +2019,7 @@ <h1>ScriptModule<a class="headerlink" href="#scriptmodule" title="Permalink to t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.jit.ScriptModule.zero_grad">
-<span class="sig-name descname"><span class="pre">zero_grad</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">set_to_none</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L2897"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.ScriptModule.zero_grad" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">zero_grad</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">set_to_none</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/module.py#L2897"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.ScriptModule.zero_grad" title="Permalink to this definition">¶</a></dt>
 <dd><p>Reset gradients of all model parameters.</p>
 <p>See similar function under <a class="reference internal" href="../optim.html#torch.optim.Optimizer" title="torch.optim.Optimizer"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.optim.Optimizer</span></code></a> for more context.</p>
 <dl class="field-list simple">
diff --git a/main/generated/torch.jit.annotate.html b/main/generated/torch.jit.annotate.html
index c93df25909b..1ffde3779e0 100644
--- a/main/generated/torch.jit.annotate.html
+++ b/main/generated/torch.jit.annotate.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.jit.annotate<a class="headerlink" href="#torch-jit-annotate" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.jit.annotate">
-<span class="sig-prename descclassname"><span class="pre">torch.jit.</span></span><span class="sig-name descname"><span class="pre">annotate</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">the_type</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">the_value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/jit.html#annotate"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/jit/__init__.py#L129"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.annotate" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.jit.</span></span><span class="sig-name descname"><span class="pre">annotate</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">the_type</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">the_value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/jit.html#annotate"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/jit/__init__.py#L129"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.annotate" title="Permalink to this definition">¶</a></dt>
 <dd><p>Use to give type of <cite>the_value</cite> in TorchScript compiler.</p>
 <p>This method is a pass-through function that returns <cite>the_value</cite>, used to hint TorchScript
 compiler the type of <cite>the_value</cite>. It is a no-op when running outside of TorchScript.</p>
diff --git a/main/generated/torch.jit.enable_onednn_fusion.html b/main/generated/torch.jit.enable_onednn_fusion.html
index 7174a2d2eca..5e03430f5d9 100644
--- a/main/generated/torch.jit.enable_onednn_fusion.html
+++ b/main/generated/torch.jit.enable_onednn_fusion.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.jit.enable_onednn_fusion<a class="headerlink" href="#torch-jit-enable-onednn-fusion" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.jit.enable_onednn_fusion">
-<span class="sig-prename descclassname"><span class="pre">torch.jit.</span></span><span class="sig-name descname"><span class="pre">enable_onednn_fusion</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">enabled</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/jit.html#enable_onednn_fusion"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/jit/__init__.py#L282"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.enable_onednn_fusion" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.jit.</span></span><span class="sig-name descname"><span class="pre">enable_onednn_fusion</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">enabled</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/jit.html#enable_onednn_fusion"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/jit/__init__.py#L282"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.enable_onednn_fusion" title="Permalink to this definition">¶</a></dt>
 <dd><p>Enable or disables onednn JIT fusion based on the parameter <cite>enabled</cite>.</p>
 <dl class="field-list simple">
 </dl>
diff --git a/main/generated/torch.jit.fork.html b/main/generated/torch.jit.fork.html
index 0992042968d..a4d0805cfa1 100644
--- a/main/generated/torch.jit.fork.html
+++ b/main/generated/torch.jit.fork.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.jit.fork<a class="headerlink" href="#torch-jit-fork" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.jit.fork">
-<span class="sig-prename descclassname"><span class="pre">torch.jit.</span></span><span class="sig-name descname"><span class="pre">fork</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">func</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/jit/_async.html#fork"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/jit/_async.py#L21"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.fork" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.jit.</span></span><span class="sig-name descname"><span class="pre">fork</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">func</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/jit/_async.html#fork"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/jit/_async.py#L21"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.fork" title="Permalink to this definition">¶</a></dt>
 <dd><p>Create an asynchronous task executing <cite>func</cite> and a reference to the value of the result of this execution.</p>
 <p><cite>fork</cite> will return immediately, so the return value of <cite>func</cite> may not have been computed yet. To force completion
 of the task and access the return value invoke <cite>torch.jit.wait</cite> on the Future. <cite>fork</cite> invoked
diff --git a/main/generated/torch.jit.freeze.html b/main/generated/torch.jit.freeze.html
index 79d6a0b39c9..edfd8f687b9 100644
--- a/main/generated/torch.jit.freeze.html
+++ b/main/generated/torch.jit.freeze.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.jit.freeze<a class="headerlink" href="#torch-jit-freeze" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.jit.freeze">
-<span class="sig-prename descclassname"><span class="pre">torch.jit.</span></span><span class="sig-name descname"><span class="pre">freeze</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mod</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">preserved_attrs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">optimize_numerics</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/jit/_freeze.html#freeze"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/jit/_freeze.py#L14"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.freeze" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.jit.</span></span><span class="sig-name descname"><span class="pre">freeze</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mod</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">preserved_attrs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">optimize_numerics</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/jit/_freeze.html#freeze"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/jit/_freeze.py#L14"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.freeze" title="Permalink to this definition">¶</a></dt>
 <dd><p>Freeze ScriptModule, inline submodules, and attributes as constants.</p>
 <p>Freezing a <a class="reference internal" href="torch.jit.ScriptModule.html#torch.jit.ScriptModule" title="torch.jit.ScriptModule"><code class="xref py py-class docutils literal notranslate"><span class="pre">ScriptModule</span></code></a> will clone it and attempt to inline the cloned
 module’s submodules, parameters, and attributes as constants in the TorchScript IR Graph.
diff --git a/main/generated/torch.jit.ignore.html b/main/generated/torch.jit.ignore.html
index e1259d45917..984b97d40e4 100644
--- a/main/generated/torch.jit.ignore.html
+++ b/main/generated/torch.jit.ignore.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.jit.ignore<a class="headerlink" href="#torch-jit-ignore" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.jit.ignore">
-<span class="sig-prename descclassname"><span class="pre">torch.jit.</span></span><span class="sig-name descname"><span class="pre">ignore</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">drop</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/_jit_internal.html#ignore"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/_jit_internal.py#L781"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.ignore" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.jit.</span></span><span class="sig-name descname"><span class="pre">ignore</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">drop</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/_jit_internal.html#ignore"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/_jit_internal.py#L781"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.ignore" title="Permalink to this definition">¶</a></dt>
 <dd><p>This decorator indicates to the compiler that a function or method should
 be ignored and left as a Python function. This allows you to leave code in
 your model that is not yet TorchScript compatible. If called from TorchScript,
diff --git a/main/generated/torch.jit.interface.html b/main/generated/torch.jit.interface.html
index 8dac9fe63d2..e235c6e771a 100644
--- a/main/generated/torch.jit.interface.html
+++ b/main/generated/torch.jit.interface.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.jit.interface<a class="headerlink" href="#torch-jit-interface" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.jit.interface">
-<span class="sig-prename descclassname"><span class="pre">torch.jit.</span></span><span class="sig-name descname"><span class="pre">interface</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">obj</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/jit/_script.html#interface"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/jit/_script.py#L1530"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.interface" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.jit.</span></span><span class="sig-name descname"><span class="pre">interface</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">obj</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/jit/_script.html#interface"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/jit/_script.py#L1530"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.interface" title="Permalink to this definition">¶</a></dt>
 <dd><p>Decorate to annotate classes or modules of different types.</p>
 <p>This decorator can be used to define an interface that can be used to annotate
 classes or modules of different types. This can be used for to annotate a submodule
diff --git a/main/generated/torch.jit.isinstance.html b/main/generated/torch.jit.isinstance.html
index 67a201daabd..02d3b193d3d 100644
--- a/main/generated/torch.jit.isinstance.html
+++ b/main/generated/torch.jit.isinstance.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.jit.isinstance<a class="headerlink" href="#torch-jit-isinstance" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.jit.isinstance">
-<span class="sig-prename descclassname"><span class="pre">torch.jit.</span></span><span class="sig-name descname"><span class="pre">isinstance</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">obj</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target_type</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/jit.html#isinstance"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/jit/__init__.py#L199"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.isinstance" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.jit.</span></span><span class="sig-name descname"><span class="pre">isinstance</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">obj</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target_type</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/jit.html#isinstance"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/jit/__init__.py#L199"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.isinstance" title="Permalink to this definition">¶</a></dt>
 <dd><p>Provide container type refinement in TorchScript.</p>
 <p>It can refine parameterized containers of the List, Dict, Tuple, and Optional types. E.g. <code class="docutils literal notranslate"><span class="pre">List[str]</span></code>,
 <code class="docutils literal notranslate"><span class="pre">Dict[str,</span> <span class="pre">List[torch.Tensor]]</span></code>, <code class="docutils literal notranslate"><span class="pre">Optional[Tuple[int,str,int]]</span></code>. It can also
diff --git a/main/generated/torch.jit.load.html b/main/generated/torch.jit.load.html
index c7058663541..557ccef05ca 100644
--- a/main/generated/torch.jit.load.html
+++ b/main/generated/torch.jit.load.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.jit.load<a class="headerlink" href="#torch-jit-load" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.jit.load">
-<span class="sig-prename descclassname"><span class="pre">torch.jit.</span></span><span class="sig-name descname"><span class="pre">load</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">f</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">map_location</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_extra_files</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_restore_shapes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/jit/_serialization.html#load"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/jit/_serialization.py#L90"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.load" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.jit.</span></span><span class="sig-name descname"><span class="pre">load</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">f</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">map_location</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_extra_files</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_restore_shapes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/jit/_serialization.html#load"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/jit/_serialization.py#L90"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.load" title="Permalink to this definition">¶</a></dt>
 <dd><p>Load a <a class="reference internal" href="torch.jit.ScriptModule.html#torch.jit.ScriptModule" title="torch.jit.ScriptModule"><code class="xref py py-class docutils literal notranslate"><span class="pre">ScriptModule</span></code></a> or <a class="reference internal" href="torch.jit.ScriptFunction.html#torch.jit.ScriptFunction" title="torch.jit.ScriptFunction"><code class="xref py py-class docutils literal notranslate"><span class="pre">ScriptFunction</span></code></a> previously saved with <a class="reference internal" href="torch.jit.save.html#torch.jit.save" title="torch.jit.save"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.jit.save</span></code></a>.</p>
 <p>All previously saved modules, no matter their device, are first loaded onto CPU,
 and then are moved to the devices they were saved from. If this fails (e.g.
diff --git a/main/generated/torch.jit.onednn_fusion_enabled.html b/main/generated/torch.jit.onednn_fusion_enabled.html
index 86b34fff878..1cde32da086 100644
--- a/main/generated/torch.jit.onednn_fusion_enabled.html
+++ b/main/generated/torch.jit.onednn_fusion_enabled.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.jit.onednn_fusion_enabled<a class="headerlink" href="#torch-jit-onednn-fusion-enabled" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.jit.onednn_fusion_enabled">
-<span class="sig-prename descclassname"><span class="pre">torch.jit.</span></span><span class="sig-name descname"><span class="pre">onednn_fusion_enabled</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/jit.html#onednn_fusion_enabled"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/jit/__init__.py#L287"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.onednn_fusion_enabled" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.jit.</span></span><span class="sig-name descname"><span class="pre">onednn_fusion_enabled</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/jit.html#onednn_fusion_enabled"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/jit/__init__.py#L287"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.onednn_fusion_enabled" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return whether onednn JIT fusion is enabled.</p>
 </dd></dl>
 
diff --git a/main/generated/torch.jit.optimize_for_inference.html b/main/generated/torch.jit.optimize_for_inference.html
index da3b7ff58d9..7a165c57cca 100644
--- a/main/generated/torch.jit.optimize_for_inference.html
+++ b/main/generated/torch.jit.optimize_for_inference.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.jit.optimize_for_inference<a class="headerlink" href="#torch-jit-optimize-for-inference" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.jit.optimize_for_inference">
-<span class="sig-prename descclassname"><span class="pre">torch.jit.</span></span><span class="sig-name descname"><span class="pre">optimize_for_inference</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mod</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">other_methods</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/jit/_freeze.html#optimize_for_inference"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/jit/_freeze.py#L185"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.optimize_for_inference" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.jit.</span></span><span class="sig-name descname"><span class="pre">optimize_for_inference</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mod</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">other_methods</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/jit/_freeze.html#optimize_for_inference"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/jit/_freeze.py#L185"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.optimize_for_inference" title="Permalink to this definition">¶</a></dt>
 <dd><p>Perform a set of optimization passes to optimize a model for the purposes of inference.</p>
 <p>If the model is not already frozen, optimize_for_inference
 will invoke <cite>torch.jit.freeze</cite> automatically.</p>
diff --git a/main/generated/torch.jit.save.html b/main/generated/torch.jit.save.html
index 09bca609116..dcd6b1f0472 100644
--- a/main/generated/torch.jit.save.html
+++ b/main/generated/torch.jit.save.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.jit.save<a class="headerlink" href="#torch-jit-save" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.jit.save">
-<span class="sig-prename descclassname"><span class="pre">torch.jit.</span></span><span class="sig-name descname"><span class="pre">save</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">m</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">f</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_extra_files</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/jit/_serialization.html#save"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/jit/_serialization.py#L21"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.save" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.jit.</span></span><span class="sig-name descname"><span class="pre">save</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">m</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">f</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_extra_files</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/jit/_serialization.html#save"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/jit/_serialization.py#L21"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.save" title="Permalink to this definition">¶</a></dt>
 <dd><p>Save an offline version of this module for use in a separate process.</p>
 <p>The saved module serializes all of the methods, submodules, parameters, and
 attributes of this module. It can be loaded into the C++ API using
diff --git a/main/generated/torch.jit.script.html b/main/generated/torch.jit.script.html
index f75a2f9363f..2f6bdfd2549 100644
--- a/main/generated/torch.jit.script.html
+++ b/main/generated/torch.jit.script.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.jit.script<a class="headerlink" href="#torch-jit-script" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.jit.script">
-<span class="sig-prename descclassname"><span class="pre">torch.jit.</span></span><span class="sig-name descname"><span class="pre">script</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">obj</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">optimize</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_frames_up</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_rcb</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">example_inputs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/jit/_script.html#script"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/jit/_script.py#L1225"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.script" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.jit.</span></span><span class="sig-name descname"><span class="pre">script</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">obj</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">optimize</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_frames_up</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_rcb</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">example_inputs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/jit/_script.html#script"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/jit/_script.py#L1225"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.script" title="Permalink to this definition">¶</a></dt>
 <dd><p>Script the function.</p>
 <p>Scripting a function or <code class="docutils literal notranslate"><span class="pre">nn.Module</span></code> will inspect the source code, compile
 it as TorchScript code using the TorchScript compiler, and return a <a class="reference internal" href="torch.jit.ScriptModule.html#torch.jit.ScriptModule" title="torch.jit.ScriptModule"><code class="xref py py-class docutils literal notranslate"><span class="pre">ScriptModule</span></code></a> or
diff --git a/main/generated/torch.jit.script_if_tracing.html b/main/generated/torch.jit.script_if_tracing.html
index e21e0e83945..f5e20b43ca5 100644
--- a/main/generated/torch.jit.script_if_tracing.html
+++ b/main/generated/torch.jit.script_if_tracing.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.jit.script_if_tracing<a class="headerlink" href="#torch-jit-script-if-tracing" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.jit.script_if_tracing">
-<span class="sig-prename descclassname"><span class="pre">torch.jit.</span></span><span class="sig-name descname"><span class="pre">script_if_tracing</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">fn</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/jit.html#script_if_tracing"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/jit/__init__.py#L177"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.script_if_tracing" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.jit.</span></span><span class="sig-name descname"><span class="pre">script_if_tracing</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">fn</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/jit.html#script_if_tracing"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/jit/__init__.py#L177"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.script_if_tracing" title="Permalink to this definition">¶</a></dt>
 <dd><p>Compiles <code class="docutils literal notranslate"><span class="pre">fn</span></code> when it is first called during tracing.</p>
 <p><code class="docutils literal notranslate"><span class="pre">torch.jit.script</span></code> has a non-negligible start up time when it is first called due to
 lazy-initializations of many compiler builtins. Therefore you should not use
diff --git a/main/generated/torch.jit.set_fusion_strategy.html b/main/generated/torch.jit.set_fusion_strategy.html
index dbdf86def4a..91c88987f7a 100644
--- a/main/generated/torch.jit.set_fusion_strategy.html
+++ b/main/generated/torch.jit.set_fusion_strategy.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.jit.set_fusion_strategy<a class="headerlink" href="#torch-jit-set-fusion-strategy" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.jit.set_fusion_strategy">
-<span class="sig-prename descclassname"><span class="pre">torch.jit.</span></span><span class="sig-name descname"><span class="pre">set_fusion_strategy</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">strategy</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/jit/_fuser.html#set_fusion_strategy"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/jit/_fuser.py#L130"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.set_fusion_strategy" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.jit.</span></span><span class="sig-name descname"><span class="pre">set_fusion_strategy</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">strategy</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/jit/_fuser.html#set_fusion_strategy"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/jit/_fuser.py#L130"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.set_fusion_strategy" title="Permalink to this definition">¶</a></dt>
 <dd><p>Set the type and number of specializations that can occur during fusion.</p>
 <p>Usage: provide a list of pairs (type, depth) where type is one of “STATIC” or “DYNAMIC”
 and depth is an integer.</p>
diff --git a/main/generated/torch.jit.strict_fusion.html b/main/generated/torch.jit.strict_fusion.html
index 5b965d0d57e..79a45ea9bd4 100644
--- a/main/generated/torch.jit.strict_fusion.html
+++ b/main/generated/torch.jit.strict_fusion.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>strict_fusion<a class="headerlink" href="#strict-fusion" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.jit.strict_fusion">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.jit.</span></span><span class="sig-name descname"><span class="pre">strict_fusion</span></span><a class="reference internal" href="../_modules/torch/jit.html#strict_fusion"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/jit/__init__.py#L242"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.strict_fusion" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.jit.</span></span><span class="sig-name descname"><span class="pre">strict_fusion</span></span><a class="reference internal" href="../_modules/torch/jit.html#strict_fusion"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/jit/__init__.py#L242"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.strict_fusion" title="Permalink to this definition">¶</a></dt>
 <dd><p>Give errors if not all nodes have been fused in inference, or symbolically differentiated in training.</p>
 <p>Example:
 Forcing fusion of additions.</p>
diff --git a/main/generated/torch.jit.trace.html b/main/generated/torch.jit.trace.html
index 0edf21d14c0..cc284476864 100644
--- a/main/generated/torch.jit.trace.html
+++ b/main/generated/torch.jit.trace.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.jit.trace<a class="headerlink" href="#torch-jit-trace" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.jit.trace">
-<span class="sig-prename descclassname"><span class="pre">torch.jit.</span></span><span class="sig-name descname"><span class="pre">trace</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">func</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">example_inputs=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">optimize=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">check_trace=True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">check_inputs=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">check_tolerance=1e-05</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">strict=True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_force_outplace=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_module_class=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_compilation_unit=&lt;torch.jit.CompilationUnit</span> <span class="pre">object&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">example_kwarg_inputs=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_store_inputs=True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/jit/_trace.html#trace"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/jit/_trace.py#L825"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.trace" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.jit.</span></span><span class="sig-name descname"><span class="pre">trace</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">func</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">example_inputs=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">optimize=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">check_trace=True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">check_inputs=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">check_tolerance=1e-05</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">strict=True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_force_outplace=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_module_class=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_compilation_unit=&lt;torch.jit.CompilationUnit</span> <span class="pre">object&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">example_kwarg_inputs=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_store_inputs=True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/jit/_trace.html#trace"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/jit/_trace.py#L825"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.trace" title="Permalink to this definition">¶</a></dt>
 <dd><p>Trace a function and return an executable  or <a class="reference internal" href="torch.jit.ScriptFunction.html#torch.jit.ScriptFunction" title="torch.jit.ScriptFunction"><code class="xref py py-class docutils literal notranslate"><span class="pre">ScriptFunction</span></code></a> that will be optimized using just-in-time compilation.</p>
 <p>Tracing is ideal for code that operates only on
 <code class="docutils literal notranslate"><span class="pre">Tensor</span></code>\s and lists, dictionaries, and
diff --git a/main/generated/torch.jit.trace_module.html b/main/generated/torch.jit.trace_module.html
index 1374a889161..5c4d952eeb5 100644
--- a/main/generated/torch.jit.trace_module.html
+++ b/main/generated/torch.jit.trace_module.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.jit.trace_module<a class="headerlink" href="#torch-jit-trace-module" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.jit.trace_module">
-<span class="sig-prename descclassname"><span class="pre">torch.jit.</span></span><span class="sig-name descname"><span class="pre">trace_module</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mod</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inputs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">optimize=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">check_trace=True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">check_inputs=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">check_tolerance=1e-05</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">strict=True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_force_outplace=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_module_class=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_compilation_unit=&lt;torch.jit.CompilationUnit</span> <span class="pre">object&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">example_inputs_is_kwarg=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_store_inputs=True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/jit/_trace.html#trace_module"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/jit/_trace.py#L1120"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.trace_module" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.jit.</span></span><span class="sig-name descname"><span class="pre">trace_module</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mod</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inputs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">optimize=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">check_trace=True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">check_inputs=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">check_tolerance=1e-05</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">strict=True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_force_outplace=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_module_class=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_compilation_unit=&lt;torch.jit.CompilationUnit</span> <span class="pre">object&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">example_inputs_is_kwarg=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_store_inputs=True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/jit/_trace.html#trace_module"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/jit/_trace.py#L1120"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.trace_module" title="Permalink to this definition">¶</a></dt>
 <dd><p>Trace a module and return an executable <a class="reference internal" href="torch.jit.ScriptModule.html#torch.jit.ScriptModule" title="torch.jit.ScriptModule"><code class="xref py py-class docutils literal notranslate"><span class="pre">ScriptModule</span></code></a> that will be optimized using just-in-time compilation.</p>
 <p>When a module is passed to <a class="reference internal" href="torch.jit.trace.html#torch.jit.trace" title="torch.jit.trace"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.jit.trace</span></code></a>, only
 the <code class="docutils literal notranslate"><span class="pre">forward</span></code> method is run and traced. With <code class="docutils literal notranslate"><span class="pre">trace_module</span></code>, you can specify a dictionary of
diff --git a/main/generated/torch.jit.unused.html b/main/generated/torch.jit.unused.html
index 21e9784fa0c..5d186ae5492 100644
--- a/main/generated/torch.jit.unused.html
+++ b/main/generated/torch.jit.unused.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.jit.unused<a class="headerlink" href="#torch-jit-unused" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.jit.unused">
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+<span class="sig-prename descclassname"><span class="pre">torch.jit.</span></span><span class="sig-name descname"><span class="pre">unused</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">fn</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/_jit_internal.html#unused"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/_jit_internal.py#L715"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.unused" title="Permalink to this definition">¶</a></dt>
 <dd><p>This decorator indicates to the compiler that a function or method should
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+<span class="sig-prename descclassname"><span class="pre">torch.jit.</span></span><span class="sig-name descname"><span class="pre">wait</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">future</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/jit/_async.html#wait"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/jit/_async.py#L102"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.wait" title="Permalink to this definition">¶</a></dt>
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@@ -612,7 +612,7 @@
 <h1>torch.load<a class="headerlink" href="#torch-load" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.load">
-<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">load</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">f</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">map_location</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pickle_module</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">pickle</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weights_only</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mmap</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">pickle_load_args</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/serialization.html#load"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/serialization.py#L1229"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.load" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">load</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">f</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">map_location</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pickle_module</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">pickle</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weights_only</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mmap</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">pickle_load_args</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/serialization.html#load"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/serialization.py#L1229"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.load" title="Permalink to this definition">¶</a></dt>
 <dd><p>Loads an object saved with <a class="reference internal" href="torch.save.html#torch.save" title="torch.save"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.save()</span></code></a> from a file.</p>
 <p><a class="reference internal" href="#torch.load" title="torch.load"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.load()</span></code></a> uses Python’s unpickling facilities but treats storages,
 which underlie tensors, specially. They are first deserialized on the
diff --git a/main/generated/torch.lobpcg.html b/main/generated/torch.lobpcg.html
index 52f0e85e7d7..f008a07bb2c 100644
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+++ b/main/generated/torch.lobpcg.html
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@@ -612,7 +612,7 @@
 <h1>torch.lobpcg<a class="headerlink" href="#torch-lobpcg" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.lobpcg">
-<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">lobpcg</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">A</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">k</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">B</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">iK</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">niter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">largest</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">method</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tracker</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ortho_iparams</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ortho_fparams</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ortho_bparams</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/_lobpcg.html#lobpcg"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/_lobpcg.py#L345"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.lobpcg" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">lobpcg</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">A</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">k</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">B</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">X</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">iK</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">niter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">largest</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">method</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tracker</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ortho_iparams</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ortho_fparams</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ortho_bparams</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/_lobpcg.html#lobpcg"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/_lobpcg.py#L345"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.lobpcg" title="Permalink to this definition">¶</a></dt>
 <dd><p>Find the k largest (or smallest) eigenvalues and the corresponding
 eigenvectors of a symmetric positive definite generalized
 eigenvalue problem using matrix-free LOBPCG methods.</p>
diff --git a/main/generated/torch.log.html b/main/generated/torch.log.html
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index 707760b5bfe..adff8adaeef 100644
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 <h1>torch.lu<a class="headerlink" href="#torch-lu" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.lu">
-<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">lu</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/_jit_internal.py#L613"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.lu" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">lu</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/_jit_internal.py#L613"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.lu" title="Permalink to this definition">¶</a></dt>
 <dd><p>Computes the LU factorization of a matrix or batches of matrices
 <code class="xref py py-attr docutils literal notranslate"><span class="pre">A</span></code>. Returns a tuple containing the LU factorization and
 pivots of <code class="xref py py-attr docutils literal notranslate"><span class="pre">A</span></code>.  Pivoting is done if <code class="xref py py-attr docutils literal notranslate"><span class="pre">pivot</span></code> is set to
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@@ -612,7 +612,7 @@
 <h1>torch.manual_seed<a class="headerlink" href="#torch-manual-seed" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.manual_seed">
-<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">manual_seed</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">seed</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/random.html#manual_seed"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/random.py#L32"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.manual_seed" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">manual_seed</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">seed</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/random.html#manual_seed"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/random.py#L32"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.manual_seed" title="Permalink to this definition">¶</a></dt>
 <dd><p>Sets the seed for generating random numbers on all devices. Returns a
 <cite>torch.Generator</cite> object.</p>
 <dl class="field-list simple">
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@@ -612,7 +612,7 @@
 <h1>torch.meshgrid<a class="headerlink" href="#torch-meshgrid" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.meshgrid">
-<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">meshgrid</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">tensors</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">indexing</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/functional.html#meshgrid"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/functional.py#L431"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.meshgrid" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">meshgrid</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">tensors</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">indexing</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/functional.html#meshgrid"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/functional.py#L431"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.meshgrid" title="Permalink to this definition">¶</a></dt>
 <dd><p>Creates grids of coordinates specified by the 1D inputs in <cite>attr</cite>:tensors.</p>
 <p>This is helpful when you want to visualize data over some
 range of inputs. See below for a plotting example.</p>
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@@ -612,7 +612,7 @@
 <h1>torch.mps.current_allocated_memory<a class="headerlink" href="#torch-mps-current-allocated-memory" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.mps.current_allocated_memory">
-<span class="sig-prename descclassname"><span class="pre">torch.mps.</span></span><span class="sig-name descname"><span class="pre">current_allocated_memory</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/mps.html#current_allocated_memory"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/mps/__init__.py#L113"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.mps.current_allocated_memory" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.mps.</span></span><span class="sig-name descname"><span class="pre">current_allocated_memory</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/mps.html#current_allocated_memory"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/mps/__init__.py#L113"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.mps.current_allocated_memory" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns the current GPU memory occupied by tensors in bytes.</p>
 <div class="admonition note">
 <p class="admonition-title">Note</p>
diff --git a/main/generated/torch.mps.device_count.html b/main/generated/torch.mps.device_count.html
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 <h1>torch.mps.device_count<a class="headerlink" href="#torch-mps-device-count" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.mps.device_count">
-<span class="sig-prename descclassname"><span class="pre">torch.mps.</span></span><span class="sig-name descname"><span class="pre">device_count</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/mps.html#device_count"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/mps/__init__.py#L26"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.mps.device_count" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.mps.</span></span><span class="sig-name descname"><span class="pre">device_count</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/mps.html#device_count"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/mps/__init__.py#L26"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.mps.device_count" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns the number of available MPS devices.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
diff --git a/main/generated/torch.mps.driver_allocated_memory.html b/main/generated/torch.mps.driver_allocated_memory.html
index c8f456fff1d..37dcc4148b6 100644
--- a/main/generated/torch.mps.driver_allocated_memory.html
+++ b/main/generated/torch.mps.driver_allocated_memory.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.mps.driver_allocated_memory<a class="headerlink" href="#torch-mps-driver-allocated-memory" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.mps.driver_allocated_memory">
-<span class="sig-prename descclassname"><span class="pre">torch.mps.</span></span><span class="sig-name descname"><span class="pre">driver_allocated_memory</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/mps.html#driver_allocated_memory"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/mps/__init__.py#L123"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.mps.driver_allocated_memory" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.mps.</span></span><span class="sig-name descname"><span class="pre">driver_allocated_memory</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/mps.html#driver_allocated_memory"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/mps/__init__.py#L123"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.mps.driver_allocated_memory" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns total GPU memory allocated by Metal driver for the process in bytes.</p>
 <div class="admonition note">
 <p class="admonition-title">Note</p>
diff --git a/main/generated/torch.mps.empty_cache.html b/main/generated/torch.mps.empty_cache.html
index a7519e46a35..7f18c380615 100644
--- a/main/generated/torch.mps.empty_cache.html
+++ b/main/generated/torch.mps.empty_cache.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.mps.empty_cache<a class="headerlink" href="#torch-mps-empty-cache" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.mps.empty_cache">
-<span class="sig-prename descclassname"><span class="pre">torch.mps.</span></span><span class="sig-name descname"><span class="pre">empty_cache</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/mps.html#empty_cache"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/mps/__init__.py#L81"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.mps.empty_cache" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.mps.</span></span><span class="sig-name descname"><span class="pre">empty_cache</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/mps.html#empty_cache"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/mps/__init__.py#L81"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.mps.empty_cache" title="Permalink to this definition">¶</a></dt>
 <dd><p>Releases all unoccupied cached memory currently held by the caching
 allocator so that those can be used in other GPU applications.</p>
 <dl class="field-list simple">
diff --git a/main/generated/torch.mps.event.Event.html b/main/generated/torch.mps.event.Event.html
index 24c4691e233..da41e6723cf 100644
--- a/main/generated/torch.mps.event.Event.html
+++ b/main/generated/torch.mps.event.Event.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>Event<a class="headerlink" href="#event" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.mps.event.Event">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.mps.event.</span></span><span class="sig-name descname"><span class="pre">Event</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">enable_timing</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/mps/event.html#Event"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/mps/event.py#L4"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.mps.event.Event" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.mps.event.</span></span><span class="sig-name descname"><span class="pre">Event</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">enable_timing</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/mps/event.html#Event"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/mps/event.py#L4"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.mps.event.Event" title="Permalink to this definition">¶</a></dt>
 <dd><p>Wrapper around an MPS event.</p>
 <p>MPS events are synchronization markers that can be used to monitor the
 device’s progress, to accurately measure timing, and to synchronize MPS streams.</p>
@@ -624,7 +624,7 @@ <h1>Event<a class="headerlink" href="#event" title="Permalink to this heading">
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.mps.event.Event.elapsed_time">
-<span class="sig-name descname"><span class="pre">elapsed_time</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">end_event</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/mps/event.html#Event.elapsed_time"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/mps/event.py#L41"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.mps.event.Event.elapsed_time" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">elapsed_time</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">end_event</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/mps/event.html#Event.elapsed_time"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/mps/event.py#L41"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.mps.event.Event.elapsed_time" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns the time elapsed in milliseconds after the event was
 recorded and before the end_event was recorded.</p>
 <dl class="field-list simple">
@@ -636,7 +636,7 @@ <h1>Event<a class="headerlink" href="#event" title="Permalink to this heading">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.mps.event.Event.query">
-<span class="sig-name descname"><span class="pre">query</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/mps/event.html#Event.query"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/mps/event.py#L31"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.mps.event.Event.query" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">query</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/mps/event.html#Event.query"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/mps/event.py#L31"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.mps.event.Event.query" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns True if all work currently captured by event has completed.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -647,7 +647,7 @@ <h1>Event<a class="headerlink" href="#event" title="Permalink to this heading">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.mps.event.Event.record">
-<span class="sig-name descname"><span class="pre">record</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/mps/event.html#Event.record"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/mps/event.py#L23"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.mps.event.Event.record" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">record</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/mps/event.html#Event.record"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/mps/event.py#L23"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.mps.event.Event.record" title="Permalink to this definition">¶</a></dt>
 <dd><p>Records the event in the default stream.</p>
 <dl class="field-list simple">
 </dl>
@@ -655,7 +655,7 @@ <h1>Event<a class="headerlink" href="#event" title="Permalink to this heading">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.mps.event.Event.synchronize">
-<span class="sig-name descname"><span class="pre">synchronize</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/mps/event.html#Event.synchronize"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/mps/event.py#L35"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.mps.event.Event.synchronize" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">synchronize</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/mps/event.html#Event.synchronize"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/mps/event.py#L35"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.mps.event.Event.synchronize" title="Permalink to this definition">¶</a></dt>
 <dd><p>Waits until the completion of all work currently captured in this event.
 This prevents the CPU thread from proceeding until the event completes.</p>
 <dl class="field-list simple">
@@ -664,7 +664,7 @@ <h1>Event<a class="headerlink" href="#event" title="Permalink to this heading">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.mps.event.Event.wait">
-<span class="sig-name descname"><span class="pre">wait</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/mps/event.html#Event.wait"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/mps/event.py#L27"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.mps.event.Event.wait" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">wait</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/mps/event.html#Event.wait"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/mps/event.py#L27"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.mps.event.Event.wait" title="Permalink to this definition">¶</a></dt>
 <dd><p>Makes all future work submitted to the default stream wait for this event.</p>
 <dl class="field-list simple">
 </dl>
diff --git a/main/generated/torch.mps.get_rng_state.html b/main/generated/torch.mps.get_rng_state.html
index ec63ce8f467..ec4acdf3e5a 100644
--- a/main/generated/torch.mps.get_rng_state.html
+++ b/main/generated/torch.mps.get_rng_state.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.mps.get_rng_state<a class="headerlink" href="#torch-mps-get-rng-state" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.mps.get_rng_state">
-<span class="sig-prename descclassname"><span class="pre">torch.mps.</span></span><span class="sig-name descname"><span class="pre">get_rng_state</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mps'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/mps.html#get_rng_state"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/mps/__init__.py#L36"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.mps.get_rng_state" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.mps.</span></span><span class="sig-name descname"><span class="pre">get_rng_state</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mps'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/mps.html#get_rng_state"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/mps/__init__.py#L36"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.mps.get_rng_state" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns the random number generator state as a ByteTensor.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.mps.manual_seed.html b/main/generated/torch.mps.manual_seed.html
index e238d5e45b9..4166b7cc560 100644
--- a/main/generated/torch.mps.manual_seed.html
+++ b/main/generated/torch.mps.manual_seed.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.mps.manual_seed<a class="headerlink" href="#torch-mps-manual-seed" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.mps.manual_seed">
-<span class="sig-prename descclassname"><span class="pre">torch.mps.</span></span><span class="sig-name descname"><span class="pre">manual_seed</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">seed</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/mps.html#manual_seed"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/mps/__init__.py#L60"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.mps.manual_seed" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.mps.</span></span><span class="sig-name descname"><span class="pre">manual_seed</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">seed</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/mps.html#manual_seed"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/mps/__init__.py#L60"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.mps.manual_seed" title="Permalink to this definition">¶</a></dt>
 <dd><p>Sets the seed for generating random numbers.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.mps.profiler.is_capturing_metal.html b/main/generated/torch.mps.profiler.is_capturing_metal.html
index 94c90d5d75d..c4fcbba1329 100644
--- a/main/generated/torch.mps.profiler.is_capturing_metal.html
+++ b/main/generated/torch.mps.profiler.is_capturing_metal.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.mps.profiler.is_capturing_metal<a class="headerlink" href="#torch-mps-profiler-is-capturing-metal" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.mps.profiler.is_capturing_metal">
-<span class="sig-prename descclassname"><span class="pre">torch.mps.profiler.</span></span><span class="sig-name descname"><span class="pre">is_capturing_metal</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/mps/profiler.html#is_capturing_metal"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/mps/profiler.py#L78"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.mps.profiler.is_capturing_metal" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.mps.profiler.</span></span><span class="sig-name descname"><span class="pre">is_capturing_metal</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/mps/profiler.html#is_capturing_metal"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/mps/profiler.py#L78"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.mps.profiler.is_capturing_metal" title="Permalink to this definition">¶</a></dt>
 <dd><p>Cheks if metal capture is in progress</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
diff --git a/main/generated/torch.mps.profiler.is_metal_capture_enabled.html b/main/generated/torch.mps.profiler.is_metal_capture_enabled.html
index 04afa894d59..4a369d61ced 100644
--- a/main/generated/torch.mps.profiler.is_metal_capture_enabled.html
+++ b/main/generated/torch.mps.profiler.is_metal_capture_enabled.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.mps.profiler.is_metal_capture_enabled<a class="headerlink" href="#torch-mps-profiler-is-metal-capture-enabled" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.mps.profiler.is_metal_capture_enabled">
-<span class="sig-prename descclassname"><span class="pre">torch.mps.profiler.</span></span><span class="sig-name descname"><span class="pre">is_metal_capture_enabled</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/mps/profiler.html#is_metal_capture_enabled"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/mps/profiler.py#L71"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.mps.profiler.is_metal_capture_enabled" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.mps.profiler.</span></span><span class="sig-name descname"><span class="pre">is_metal_capture_enabled</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/mps/profiler.html#is_metal_capture_enabled"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/mps/profiler.py#L71"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.mps.profiler.is_metal_capture_enabled" title="Permalink to this definition">¶</a></dt>
 <dd><p>Checks if <cite>metal_capture</cite> context manager is usable
 To enable metal capture, set MTL_CAPTURE_ENABLED envvar</p>
 <dl class="field-list simple">
diff --git a/main/generated/torch.mps.profiler.metal_capture.html b/main/generated/torch.mps.profiler.metal_capture.html
index 2fdc5f8960a..82ea3fa7092 100644
--- a/main/generated/torch.mps.profiler.metal_capture.html
+++ b/main/generated/torch.mps.profiler.metal_capture.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.mps.profiler.metal_capture<a class="headerlink" href="#torch-mps-profiler-metal-capture" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.mps.profiler.metal_capture">
-<span class="sig-prename descclassname"><span class="pre">torch.mps.profiler.</span></span><span class="sig-name descname"><span class="pre">metal_capture</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">fname</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/mps/profiler.html#metal_capture"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/mps/profiler.py#L83"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.mps.profiler.metal_capture" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.mps.profiler.</span></span><span class="sig-name descname"><span class="pre">metal_capture</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">fname</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/mps/profiler.html#metal_capture"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/mps/profiler.py#L83"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.mps.profiler.metal_capture" title="Permalink to this definition">¶</a></dt>
 <dd><p>Conext manager that enables capturing of Metal calls into gputrace</p>
 <dl class="field-list simple">
 </dl>
diff --git a/main/generated/torch.mps.profiler.profile.html b/main/generated/torch.mps.profiler.profile.html
index 6b7b58ae8fc..686db2ebd68 100644
--- a/main/generated/torch.mps.profiler.profile.html
+++ b/main/generated/torch.mps.profiler.profile.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.mps.profiler.profile<a class="headerlink" href="#torch-mps-profiler-profile" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.mps.profiler.profile">
-<span class="sig-prename descclassname"><span class="pre">torch.mps.profiler.</span></span><span class="sig-name descname"><span class="pre">profile</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'interval'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">wait_until_completed</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/mps/profiler.html#profile"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/mps/profiler.py#L46"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.mps.profiler.profile" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.mps.profiler.</span></span><span class="sig-name descname"><span class="pre">profile</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'interval'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">wait_until_completed</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/mps/profiler.html#profile"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/mps/profiler.py#L46"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.mps.profiler.profile" title="Permalink to this definition">¶</a></dt>
 <dd><p>Context Manager to enabling generating OS Signpost tracing from MPS backend.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.mps.profiler.start.html b/main/generated/torch.mps.profiler.start.html
index f7ea816bf83..f82cc4e575b 100644
--- a/main/generated/torch.mps.profiler.start.html
+++ b/main/generated/torch.mps.profiler.start.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.mps.profiler.start<a class="headerlink" href="#torch-mps-profiler-start" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.mps.profiler.start">
-<span class="sig-prename descclassname"><span class="pre">torch.mps.profiler.</span></span><span class="sig-name descname"><span class="pre">start</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'interval'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">wait_until_completed</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/mps/profiler.html#start"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/mps/profiler.py#L17"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.mps.profiler.start" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.mps.profiler.</span></span><span class="sig-name descname"><span class="pre">start</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'interval'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">wait_until_completed</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/mps/profiler.html#start"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/mps/profiler.py#L17"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.mps.profiler.start" title="Permalink to this definition">¶</a></dt>
 <dd><p>Start OS Signpost tracing from MPS backend.</p>
 <p>The generated OS Signposts could be recorded and viewed in
 XCode Instruments Logging tool.</p>
diff --git a/main/generated/torch.mps.profiler.stop.html b/main/generated/torch.mps.profiler.stop.html
index 6898799f507..bf0c0fc670c 100644
--- a/main/generated/torch.mps.profiler.stop.html
+++ b/main/generated/torch.mps.profiler.stop.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.mps.profiler.stop<a class="headerlink" href="#torch-mps-profiler-stop" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.mps.profiler.stop">
-<span class="sig-prename descclassname"><span class="pre">torch.mps.profiler.</span></span><span class="sig-name descname"><span class="pre">stop</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/mps/profiler.html#stop"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/mps/profiler.py#L41"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.mps.profiler.stop" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.mps.profiler.</span></span><span class="sig-name descname"><span class="pre">stop</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/mps/profiler.html#stop"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/mps/profiler.py#L41"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.mps.profiler.stop" title="Permalink to this definition">¶</a></dt>
 <dd><p>Stops generating OS Signpost tracing from MPS backend.</p>
 </dd></dl>
 
diff --git a/main/generated/torch.mps.recommended_max_memory.html b/main/generated/torch.mps.recommended_max_memory.html
index 0d0c321b69c..08e16be7540 100644
--- a/main/generated/torch.mps.recommended_max_memory.html
+++ b/main/generated/torch.mps.recommended_max_memory.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.mps.recommended_max_memory<a class="headerlink" href="#torch-mps-recommended-max-memory" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.mps.recommended_max_memory">
-<span class="sig-prename descclassname"><span class="pre">torch.mps.</span></span><span class="sig-name descname"><span class="pre">recommended_max_memory</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/mps.html#recommended_max_memory"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/mps/__init__.py#L133"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.mps.recommended_max_memory" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.mps.</span></span><span class="sig-name descname"><span class="pre">recommended_max_memory</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/mps.html#recommended_max_memory"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/mps/__init__.py#L133"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.mps.recommended_max_memory" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns recommended max Working set size for GPU memory in bytes.</p>
 <div class="admonition note">
 <p class="admonition-title">Note</p>
diff --git a/main/generated/torch.mps.seed.html b/main/generated/torch.mps.seed.html
index 1a683b3aa1d..3321b8dea50 100644
--- a/main/generated/torch.mps.seed.html
+++ b/main/generated/torch.mps.seed.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.mps.seed<a class="headerlink" href="#torch-mps-seed" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.mps.seed">
-<span class="sig-prename descclassname"><span class="pre">torch.mps.</span></span><span class="sig-name descname"><span class="pre">seed</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/mps.html#seed"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/mps/__init__.py#L76"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.mps.seed" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.mps.</span></span><span class="sig-name descname"><span class="pre">seed</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/mps.html#seed"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/mps/__init__.py#L76"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.mps.seed" title="Permalink to this definition">¶</a></dt>
 <dd><p>Sets the seed for generating random numbers to a random number.</p>
 <dl class="field-list simple">
 </dl>
diff --git a/main/generated/torch.mps.set_per_process_memory_fraction.html b/main/generated/torch.mps.set_per_process_memory_fraction.html
index 4e12d4fc2dc..bb8ebddeead 100644
--- a/main/generated/torch.mps.set_per_process_memory_fraction.html
+++ b/main/generated/torch.mps.set_per_process_memory_fraction.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.mps.set_per_process_memory_fraction<a class="headerlink" href="#torch-mps-set-per-process-memory-fraction" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.mps.set_per_process_memory_fraction">
-<span class="sig-prename descclassname"><span class="pre">torch.mps.</span></span><span class="sig-name descname"><span class="pre">set_per_process_memory_fraction</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">fraction</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/mps.html#set_per_process_memory_fraction"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/mps/__init__.py#L88"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.mps.set_per_process_memory_fraction" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.mps.</span></span><span class="sig-name descname"><span class="pre">set_per_process_memory_fraction</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">fraction</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/mps.html#set_per_process_memory_fraction"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/mps/__init__.py#L88"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.mps.set_per_process_memory_fraction" title="Permalink to this definition">¶</a></dt>
 <dd><p>Set memory fraction for limiting process’s memory allocation on MPS device.
 The allowed value equals the fraction multiplied by recommended maximum device memory
 (obtained from Metal API device.recommendedMaxWorkingSetSize).
diff --git a/main/generated/torch.mps.set_rng_state.html b/main/generated/torch.mps.set_rng_state.html
index 403f0187949..9c728c6e21a 100644
--- a/main/generated/torch.mps.set_rng_state.html
+++ b/main/generated/torch.mps.set_rng_state.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.mps.set_rng_state<a class="headerlink" href="#torch-mps-set-rng-state" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.mps.set_rng_state">
-<span class="sig-prename descclassname"><span class="pre">torch.mps.</span></span><span class="sig-name descname"><span class="pre">set_rng_state</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">new_state</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mps'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/mps.html#set_rng_state"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/mps/__init__.py#L46"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.mps.set_rng_state" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.mps.</span></span><span class="sig-name descname"><span class="pre">set_rng_state</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">new_state</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mps'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/mps.html#set_rng_state"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/mps/__init__.py#L46"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.mps.set_rng_state" title="Permalink to this definition">¶</a></dt>
 <dd><p>Sets the random number generator state.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.mps.synchronize.html b/main/generated/torch.mps.synchronize.html
index 62c4eefde4f..4a05d2e020f 100644
--- a/main/generated/torch.mps.synchronize.html
+++ b/main/generated/torch.mps.synchronize.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.mps.synchronize<a class="headerlink" href="#torch-mps-synchronize" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.mps.synchronize">
-<span class="sig-prename descclassname"><span class="pre">torch.mps.</span></span><span class="sig-name descname"><span class="pre">synchronize</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/mps.html#synchronize"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/mps/__init__.py#L31"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.mps.synchronize" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.mps.</span></span><span class="sig-name descname"><span class="pre">synchronize</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/mps.html#synchronize"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/mps/__init__.py#L31"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.mps.synchronize" title="Permalink to this definition">¶</a></dt>
 <dd><p>Waits for all kernels in all streams on a MPS device to complete.</p>
 <dl class="field-list simple">
 </dl>
diff --git a/main/generated/torch.msort.html b/main/generated/torch.msort.html
index 09bc3bfe16c..c38c3a41ec5 100644
--- a/main/generated/torch.msort.html
+++ b/main/generated/torch.msort.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.mtia.DeferredMtiaCallError.html b/main/generated/torch.mtia.DeferredMtiaCallError.html
index 14eeab5ca64..c0fd12a43e2 100644
--- a/main/generated/torch.mtia.DeferredMtiaCallError.html
+++ b/main/generated/torch.mtia.DeferredMtiaCallError.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.mtia.DeferredMtiaCallError<a class="headerlink" href="#torch-mtia-deferredmtiacallerror" title="Permalink to this heading">¶</a></h1>
 <dl class="py exception">
 <dt class="sig sig-object py" id="torch.mtia.DeferredMtiaCallError">
-<em class="property"><span class="pre">exception</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.mtia.</span></span><span class="sig-name descname"><span class="pre">DeferredMtiaCallError</span></span><a class="reference internal" href="../_modules/torch/mtia.html#DeferredMtiaCallError"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/mtia/__init__.py#L97"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.mtia.DeferredMtiaCallError" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">exception</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.mtia.</span></span><span class="sig-name descname"><span class="pre">DeferredMtiaCallError</span></span><a class="reference internal" href="../_modules/torch/mtia.html#DeferredMtiaCallError"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/mtia/__init__.py#L97"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.mtia.DeferredMtiaCallError" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 </div>
diff --git a/main/generated/torch.mtia.Event.html b/main/generated/torch.mtia.Event.html
index 1bc3551ea65..735a38f14d3 100644
--- a/main/generated/torch.mtia.Event.html
+++ b/main/generated/torch.mtia.Event.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.mtia.Stream.html b/main/generated/torch.mtia.Stream.html
index 0fbcb9d9dff..5770a179d9b 100644
--- a/main/generated/torch.mtia.Stream.html
+++ b/main/generated/torch.mtia.Stream.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.mtia.StreamContext.html b/main/generated/torch.mtia.StreamContext.html
index 805ca7c0609..110586069ad 100644
--- a/main/generated/torch.mtia.StreamContext.html
+++ b/main/generated/torch.mtia.StreamContext.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>StreamContext<a class="headerlink" href="#streamcontext" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.mtia.StreamContext">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.mtia.</span></span><span class="sig-name descname"><span class="pre">StreamContext</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">stream</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/mtia.html#StreamContext"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/mtia/__init__.py#L242"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.mtia.StreamContext" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.mtia.</span></span><span class="sig-name descname"><span class="pre">StreamContext</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">stream</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/mtia.html#StreamContext"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/mtia/__init__.py#L242"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.mtia.StreamContext" title="Permalink to this definition">¶</a></dt>
 <dd><p>Context-manager that selects a given stream.</p>
 <p>All MTIA kernels queued within its context will be enqueued on a selected
 stream.</p>
diff --git a/main/generated/torch.mtia.current_device.html b/main/generated/torch.mtia.current_device.html
index e78024d2fae..eb5ccd9beda 100644
--- a/main/generated/torch.mtia.current_device.html
+++ b/main/generated/torch.mtia.current_device.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.mtia.current_device<a class="headerlink" href="#torch-mtia-current-device" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.mtia.current_device">
-<span class="sig-prename descclassname"><span class="pre">torch.mtia.</span></span><span class="sig-name descname"><span class="pre">current_device</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/mtia.html#current_device"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/mtia/__init__.py#L126"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.mtia.current_device" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.mtia.</span></span><span class="sig-name descname"><span class="pre">current_device</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/mtia.html#current_device"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/mtia/__init__.py#L126"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.mtia.current_device" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the index of a currently selected device.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
diff --git a/main/generated/torch.mtia.current_stream.html b/main/generated/torch.mtia.current_stream.html
index 4f9232f13df..ff4229e8af0 100644
--- a/main/generated/torch.mtia.current_stream.html
+++ b/main/generated/torch.mtia.current_stream.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.mtia.current_stream<a class="headerlink" href="#torch-mtia-current-stream" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.mtia.current_stream">
-<span class="sig-prename descclassname"><span class="pre">torch.mtia.</span></span><span class="sig-name descname"><span class="pre">current_stream</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/mtia.html#current_stream"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/mtia/__init__.py#L131"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.mtia.current_stream" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.mtia.</span></span><span class="sig-name descname"><span class="pre">current_stream</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/mtia.html#current_stream"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/mtia/__init__.py#L131"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.mtia.current_stream" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the currently selected <a class="reference internal" href="torch.mtia.Stream.html#torch.mtia.Stream" title="torch.mtia.Stream"><code class="xref py py-class docutils literal notranslate"><span class="pre">Stream</span></code></a> for a given device.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.mtia.default_stream.html b/main/generated/torch.mtia.default_stream.html
index f908789ba6f..5dcfd68f067 100644
--- a/main/generated/torch.mtia.default_stream.html
+++ b/main/generated/torch.mtia.default_stream.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.mtia.default_stream<a class="headerlink" href="#torch-mtia-default-stream" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.mtia.default_stream">
-<span class="sig-prename descclassname"><span class="pre">torch.mtia.</span></span><span class="sig-name descname"><span class="pre">default_stream</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/mtia.html#default_stream"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/mtia/__init__.py#L143"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.mtia.default_stream" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.mtia.</span></span><span class="sig-name descname"><span class="pre">default_stream</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/mtia.html#default_stream"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/mtia/__init__.py#L143"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.mtia.default_stream" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the default <a class="reference internal" href="torch.mtia.Stream.html#torch.mtia.Stream" title="torch.mtia.Stream"><code class="xref py py-class docutils literal notranslate"><span class="pre">Stream</span></code></a> for a given device.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.mtia.device.html b/main/generated/torch.mtia.device.html
index ac77ffad7e2..efb9a06cdac 100644
--- a/main/generated/torch.mtia.device.html
+++ b/main/generated/torch.mtia.device.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>device<a class="headerlink" href="#device" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.mtia.device">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.mtia.</span></span><span class="sig-name descname"><span class="pre">device</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/mtia.html#device"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/mtia/__init__.py#L222"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.mtia.device" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.mtia.</span></span><span class="sig-name descname"><span class="pre">device</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/mtia.html#device"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/mtia/__init__.py#L222"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.mtia.device" title="Permalink to this definition">¶</a></dt>
 <dd><p>Context-manager that changes the selected device.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.mtia.device_count.html b/main/generated/torch.mtia.device_count.html
index be229671ec8..889e2d53f73 100644
--- a/main/generated/torch.mtia.device_count.html
+++ b/main/generated/torch.mtia.device_count.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.mtia.device_count<a class="headerlink" href="#torch-mtia-device-count" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.mtia.device_count">
-<span class="sig-prename descclassname"><span class="pre">torch.mtia.</span></span><span class="sig-name descname"><span class="pre">device_count</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/mtia.html#device_count"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/mtia/__init__.py#L120"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.mtia.device_count" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.mtia.</span></span><span class="sig-name descname"><span class="pre">device_count</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/mtia.html#device_count"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/mtia/__init__.py#L120"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.mtia.device_count" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the number of MTIA devices available.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
diff --git a/main/generated/torch.mtia.empty_cache.html b/main/generated/torch.mtia.empty_cache.html
index f9173f525b3..bfca1d807ef 100644
--- a/main/generated/torch.mtia.empty_cache.html
+++ b/main/generated/torch.mtia.empty_cache.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.mtia.empty_cache<a class="headerlink" href="#torch-mtia-empty-cache" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.mtia.empty_cache">
-<span class="sig-prename descclassname"><span class="pre">torch.mtia.</span></span><span class="sig-name descname"><span class="pre">empty_cache</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/mtia.html#empty_cache"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/mtia/__init__.py#L191"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.mtia.empty_cache" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.mtia.</span></span><span class="sig-name descname"><span class="pre">empty_cache</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/mtia.html#empty_cache"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/mtia/__init__.py#L191"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.mtia.empty_cache" title="Permalink to this definition">¶</a></dt>
 <dd><p>Empty the MTIA device cache.</p>
 <dl class="field-list simple">
 </dl>
diff --git a/main/generated/torch.mtia.get_device_capability.html b/main/generated/torch.mtia.get_device_capability.html
index ab9bed93c90..e570a32c7c5 100644
--- a/main/generated/torch.mtia.get_device_capability.html
+++ b/main/generated/torch.mtia.get_device_capability.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.mtia.get_device_capability<a class="headerlink" href="#torch-mtia-get-device-capability" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.mtia.get_device_capability">
-<span class="sig-prename descclassname"><span class="pre">torch.mtia.</span></span><span class="sig-name descname"><span class="pre">get_device_capability</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/mtia.html#get_device_capability"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/mtia/__init__.py#L180"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.mtia.get_device_capability" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.mtia.</span></span><span class="sig-name descname"><span class="pre">get_device_capability</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/mtia.html#get_device_capability"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/mtia/__init__.py#L180"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.mtia.get_device_capability" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return capability of a given device as a tuple of (major version, minor version).</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.mtia.get_rng_state.html b/main/generated/torch.mtia.get_rng_state.html
index a07c8672b1b..86d94b9f5bc 100644
--- a/main/generated/torch.mtia.get_rng_state.html
+++ b/main/generated/torch.mtia.get_rng_state.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.mtia.get_rng_state<a class="headerlink" href="#torch-mtia-get-rng-state" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.mtia.get_rng_state">
-<span class="sig-prename descclassname"><span class="pre">torch.mtia.</span></span><span class="sig-name descname"><span class="pre">get_rng_state</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mtia'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/mtia.html#get_rng_state"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/mtia/__init__.py#L311"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.mtia.get_rng_state" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.mtia.</span></span><span class="sig-name descname"><span class="pre">get_rng_state</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mtia'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/mtia.html#get_rng_state"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/mtia/__init__.py#L311"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.mtia.get_rng_state" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns the random number generator state as a ByteTensor.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.mtia.init.html b/main/generated/torch.mtia.init.html
index 2c7e3a15aae..8cc8e7f6714 100644
--- a/main/generated/torch.mtia.init.html
+++ b/main/generated/torch.mtia.init.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.mtia.init<a class="headerlink" href="#torch-mtia-init" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.mtia.init">
-<span class="sig-prename descclassname"><span class="pre">torch.mtia.</span></span><span class="sig-name descname"><span class="pre">init</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/mtia.html#init"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/mtia/__init__.py#L33"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.mtia.init" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.mtia.</span></span><span class="sig-name descname"><span class="pre">init</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/mtia.html#init"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/mtia/__init__.py#L33"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.mtia.init" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 </div>
diff --git a/main/generated/torch.mtia.is_available.html b/main/generated/torch.mtia.is_available.html
index 7eefabd6b19..ce4eecf8ba9 100644
--- a/main/generated/torch.mtia.is_available.html
+++ b/main/generated/torch.mtia.is_available.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.mtia.is_available<a class="headerlink" href="#torch-mtia-is-available" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.mtia.is_available">
-<span class="sig-prename descclassname"><span class="pre">torch.mtia.</span></span><span class="sig-name descname"><span class="pre">is_available</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/mtia.html#is_available"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/mtia/__init__.py#L106"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.mtia.is_available" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.mtia.</span></span><span class="sig-name descname"><span class="pre">is_available</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/mtia.html#is_available"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/mtia/__init__.py#L106"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.mtia.is_available" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return true if MTIA device is available</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
diff --git a/main/generated/torch.mtia.is_initialized.html b/main/generated/torch.mtia.is_initialized.html
index a6a82136c63..8d12bf6817f 100644
--- a/main/generated/torch.mtia.is_initialized.html
+++ b/main/generated/torch.mtia.is_initialized.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.mtia.is_initialized<a class="headerlink" href="#torch-mtia-is-initialized" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.mtia.is_initialized">
-<span class="sig-prename descclassname"><span class="pre">torch.mtia.</span></span><span class="sig-name descname"><span class="pre">is_initialized</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/mtia.html#is_initialized"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/mtia/__init__.py#L37"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.mtia.is_initialized" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.mtia.</span></span><span class="sig-name descname"><span class="pre">is_initialized</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/mtia.html#is_initialized"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/mtia/__init__.py#L37"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.mtia.is_initialized" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return whether PyTorch’s MTIA state has been initialized.</p>
 </dd></dl>
 
diff --git a/main/generated/torch.mtia.memory.memory_stats.html b/main/generated/torch.mtia.memory.memory_stats.html
index a4f50dc76ee..0824ee4d18d 100644
--- a/main/generated/torch.mtia.memory.memory_stats.html
+++ b/main/generated/torch.mtia.memory.memory_stats.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.mtia.memory.memory_stats<a class="headerlink" href="#torch-mtia-memory-memory-stats" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.mtia.memory.memory_stats">
-<span class="sig-prename descclassname"><span class="pre">torch.mtia.memory.</span></span><span class="sig-name descname"><span class="pre">memory_stats</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/mtia/memory.html#memory_stats"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/mtia/memory.py#L13"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.mtia.memory.memory_stats" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.mtia.memory.</span></span><span class="sig-name descname"><span class="pre">memory_stats</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/mtia/memory.html#memory_stats"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/mtia/memory.py#L13"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.mtia.memory.memory_stats" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return a dictionary of MTIA memory allocator statistics for a given device.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.mtia.memory_stats.html b/main/generated/torch.mtia.memory_stats.html
index 8e641b61163..d16469d9848 100644
--- a/main/generated/torch.mtia.memory_stats.html
+++ b/main/generated/torch.mtia.memory_stats.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.mtia.memory_stats<a class="headerlink" href="#torch-mtia-memory-stats" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.mtia.memory_stats">
-<span class="sig-prename descclassname"><span class="pre">torch.mtia.</span></span><span class="sig-name descname"><span class="pre">memory_stats</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/mtia/memory.html#memory_stats"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/mtia/memory.py#L13"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.mtia.memory_stats" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.mtia.</span></span><span class="sig-name descname"><span class="pre">memory_stats</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/mtia/memory.html#memory_stats"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/mtia/memory.py#L13"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.mtia.memory_stats" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return a dictionary of MTIA memory allocator statistics for a given device.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.mtia.record_memory_history.html b/main/generated/torch.mtia.record_memory_history.html
index c61fbf3a49a..20b67868bde 100644
--- a/main/generated/torch.mtia.record_memory_history.html
+++ b/main/generated/torch.mtia.record_memory_history.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.mtia.record_memory_history<a class="headerlink" href="#torch-mtia-record-memory-history" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.mtia.record_memory_history">
-<span class="sig-prename descclassname"><span class="pre">torch.mtia.</span></span><span class="sig-name descname"><span class="pre">record_memory_history</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">enabled</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'all'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stacks</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'python'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_entries</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/mtia.html#record_memory_history"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/mtia/__init__.py#L155"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.mtia.record_memory_history" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.mtia.</span></span><span class="sig-name descname"><span class="pre">record_memory_history</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">enabled</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'all'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stacks</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'python'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_entries</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/mtia.html#record_memory_history"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/mtia/__init__.py#L155"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.mtia.record_memory_history" title="Permalink to this definition">¶</a></dt>
 <dd><p>Enable/Disable the memory profiler on MTIA allocator</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.mtia.set_device.html b/main/generated/torch.mtia.set_device.html
index 1099fdd5d24..16896dd586c 100644
--- a/main/generated/torch.mtia.set_device.html
+++ b/main/generated/torch.mtia.set_device.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.mtia.set_device<a class="headerlink" href="#torch-mtia-set-device" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.mtia.set_device">
-<span class="sig-prename descclassname"><span class="pre">torch.mtia.</span></span><span class="sig-name descname"><span class="pre">set_device</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/mtia.html#set_device"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/mtia/__init__.py#L210"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.mtia.set_device" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.mtia.</span></span><span class="sig-name descname"><span class="pre">set_device</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/mtia.html#set_device"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/mtia/__init__.py#L210"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.mtia.set_device" title="Permalink to this definition">¶</a></dt>
 <dd><p>Set the current device.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.mtia.set_rng_state.html b/main/generated/torch.mtia.set_rng_state.html
index 624ec796f3b..5269a0c4a19 100644
--- a/main/generated/torch.mtia.set_rng_state.html
+++ b/main/generated/torch.mtia.set_rng_state.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.mtia.set_rng_state<a class="headerlink" href="#torch-mtia-set-rng-state" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.mtia.set_rng_state">
-<span class="sig-prename descclassname"><span class="pre">torch.mtia.</span></span><span class="sig-name descname"><span class="pre">set_rng_state</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">new_state</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mtia'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/mtia.html#set_rng_state"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/mtia/__init__.py#L326"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.mtia.set_rng_state" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.mtia.</span></span><span class="sig-name descname"><span class="pre">set_rng_state</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">new_state</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mtia'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/mtia.html#set_rng_state"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/mtia/__init__.py#L326"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.mtia.set_rng_state" title="Permalink to this definition">¶</a></dt>
 <dd><p>Sets the random number generator state.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.mtia.set_stream.html b/main/generated/torch.mtia.set_stream.html
index 1520a346514..1aa8c8bf0ad 100644
--- a/main/generated/torch.mtia.set_stream.html
+++ b/main/generated/torch.mtia.set_stream.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.mtia.set_stream<a class="headerlink" href="#torch-mtia-set-stream" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.mtia.set_stream">
-<span class="sig-prename descclassname"><span class="pre">torch.mtia.</span></span><span class="sig-name descname"><span class="pre">set_stream</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">stream</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/mtia.html#set_stream"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/mtia/__init__.py#L196"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.mtia.set_stream" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.mtia.</span></span><span class="sig-name descname"><span class="pre">set_stream</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">stream</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/mtia.html#set_stream"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/mtia/__init__.py#L196"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.mtia.set_stream" title="Permalink to this definition">¶</a></dt>
 <dd><dl class="simple">
 <dt>Set the current stream.This is a wrapper API to set the stream.</dt><dd><p>Usage of this function is discouraged in favor of the <code class="docutils literal notranslate"><span class="pre">stream</span></code>
 context manager.</p>
diff --git a/main/generated/torch.mtia.snapshot.html b/main/generated/torch.mtia.snapshot.html
index 9fb6ff4b2ee..9267ba68a0e 100644
--- a/main/generated/torch.mtia.snapshot.html
+++ b/main/generated/torch.mtia.snapshot.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.mtia.snapshot<a class="headerlink" href="#torch-mtia-snapshot" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.mtia.snapshot">
-<span class="sig-prename descclassname"><span class="pre">torch.mtia.</span></span><span class="sig-name descname"><span class="pre">snapshot</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/mtia.html#snapshot"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/mtia/__init__.py#L174"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.mtia.snapshot" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.mtia.</span></span><span class="sig-name descname"><span class="pre">snapshot</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/mtia.html#snapshot"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/mtia/__init__.py#L174"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.mtia.snapshot" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return a dictionary of MTIA memory allocator history</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
diff --git a/main/generated/torch.mtia.stream.html b/main/generated/torch.mtia.stream.html
index 5c81aa1831a..97ce6ba37b1 100644
--- a/main/generated/torch.mtia.stream.html
+++ b/main/generated/torch.mtia.stream.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.mtia.stream<a class="headerlink" href="#torch-mtia-stream" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.mtia.stream">
-<span class="sig-prename descclassname"><span class="pre">torch.mtia.</span></span><span class="sig-name descname"><span class="pre">stream</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">stream</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/mtia.html#stream"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/mtia/__init__.py#L300"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.mtia.stream" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.mtia.</span></span><span class="sig-name descname"><span class="pre">stream</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">stream</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/mtia.html#stream"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/mtia/__init__.py#L300"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.mtia.stream" title="Permalink to this definition">¶</a></dt>
 <dd><p>Wrap around the Context-manager StreamContext that selects a given stream.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.mtia.synchronize.html b/main/generated/torch.mtia.synchronize.html
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 <h1>torch.mtia.synchronize<a class="headerlink" href="#torch-mtia-synchronize" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
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-<span class="sig-prename descclassname"><span class="pre">torch.mtia.</span></span><span class="sig-name descname"><span class="pre">synchronize</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/mtia.html#synchronize"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/mtia/__init__.py#L114"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.mtia.synchronize" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.mtia.</span></span><span class="sig-name descname"><span class="pre">synchronize</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/mtia.html#synchronize"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/mtia/__init__.py#L114"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.mtia.synchronize" title="Permalink to this definition">¶</a></dt>
 <dd><p>Waits for all jobs in all streams on a MTIA device to complete.</p>
 <dl class="field-list simple">
 </dl>
diff --git a/main/generated/torch.mul.html b/main/generated/torch.mul.html
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@@ -612,7 +612,7 @@
 <h1>AdaptiveAvgPool1d<a class="headerlink" href="#adaptiveavgpool1d" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.AdaptiveAvgPool1d">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">AdaptiveAvgPool1d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">output_size</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/pooling.html#AdaptiveAvgPool1d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/pooling.py#L1391"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.AdaptiveAvgPool1d" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">AdaptiveAvgPool1d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">output_size</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/pooling.html#AdaptiveAvgPool1d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/pooling.py#L1391"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.AdaptiveAvgPool1d" title="Permalink to this definition">¶</a></dt>
 <dd><p>Applies a 1D adaptive average pooling over an input signal composed of several input planes.</p>
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@@ -612,7 +612,7 @@
 <h1>AdaptiveAvgPool2d<a class="headerlink" href="#adaptiveavgpool2d" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.AdaptiveAvgPool2d">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">AdaptiveAvgPool2d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">output_size</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/pooling.html#AdaptiveAvgPool2d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/pooling.py#L1419"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.AdaptiveAvgPool2d" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">AdaptiveAvgPool2d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">output_size</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/pooling.html#AdaptiveAvgPool2d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/pooling.py#L1419"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.AdaptiveAvgPool2d" title="Permalink to this definition">¶</a></dt>
 <dd><p>Applies a 2D adaptive average pooling over an input signal composed of several input planes.</p>
 <p>The output is of size H x W, for any input size.
 The number of output features is equal to the number of input planes.</p>
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@@ -612,7 +612,7 @@
 <h1>AdaptiveAvgPool3d<a class="headerlink" href="#adaptiveavgpool3d" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.AdaptiveAvgPool3d">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">AdaptiveAvgPool3d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">output_size</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/pooling.html#AdaptiveAvgPool3d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/pooling.py#L1458"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.AdaptiveAvgPool3d" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">AdaptiveAvgPool3d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">output_size</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/pooling.html#AdaptiveAvgPool3d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/pooling.py#L1458"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.AdaptiveAvgPool3d" title="Permalink to this definition">¶</a></dt>
 <dd><p>Applies a 3D adaptive average pooling over an input signal composed of several input planes.</p>
 <p>The output is of size D x H x W, for any input size.
 The number of output features is equal to the number of input planes.</p>
diff --git a/main/generated/torch.nn.AdaptiveLogSoftmaxWithLoss.html b/main/generated/torch.nn.AdaptiveLogSoftmaxWithLoss.html
index d277d95a247..8834b9a638a 100644
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@@ -300,7 +300,7 @@
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+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
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@@ -612,7 +612,7 @@
 <h1>AdaptiveLogSoftmaxWithLoss<a class="headerlink" href="#adaptivelogsoftmaxwithloss" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.AdaptiveLogSoftmaxWithLoss">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">AdaptiveLogSoftmaxWithLoss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">in_features</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_classes</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cutoffs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">div_value</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">4.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">head_bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/adaptive.html#AdaptiveLogSoftmaxWithLoss"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/adaptive.py#L20"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.AdaptiveLogSoftmaxWithLoss" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">AdaptiveLogSoftmaxWithLoss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">in_features</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_classes</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cutoffs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">div_value</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">4.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">head_bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/adaptive.html#AdaptiveLogSoftmaxWithLoss"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/adaptive.py#L20"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.AdaptiveLogSoftmaxWithLoss" title="Permalink to this definition">¶</a></dt>
 <dd><p>Efficient softmax approximation.</p>
 <p>As described in
 <a class="reference external" href="https://arxiv.org/abs/1609.04309">Efficient softmax approximation for GPUs by Edouard Grave, Armand Joulin,
@@ -706,7 +706,7 @@ <h1>AdaptiveLogSoftmaxWithLoss<a class="headerlink" href="#adaptivelogsoftmaxwit
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.AdaptiveLogSoftmaxWithLoss.log_prob">
-<span class="sig-name descname"><span class="pre">log_prob</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/adaptive.html#AdaptiveLogSoftmaxWithLoss.log_prob"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/adaptive.py#L279"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.AdaptiveLogSoftmaxWithLoss.log_prob" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">log_prob</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/adaptive.html#AdaptiveLogSoftmaxWithLoss.log_prob"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/adaptive.py#L279"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.AdaptiveLogSoftmaxWithLoss.log_prob" title="Permalink to this definition">¶</a></dt>
 <dd><p>Compute log probabilities for all <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mtext mathvariant="monospace">n_classes</mtext></mrow><annotation encoding="application/x-tex">\texttt{n\_classes}</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.7063em;vertical-align:-0.0951em;"></span><span class="mord text"><span class="mord texttt">n_classes</span></span></span></span></span></span>.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -732,7 +732,7 @@ <h1>AdaptiveLogSoftmaxWithLoss<a class="headerlink" href="#adaptivelogsoftmaxwit
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.AdaptiveLogSoftmaxWithLoss.predict">
-<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/adaptive.html#AdaptiveLogSoftmaxWithLoss.predict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/adaptive.py#L298"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.AdaptiveLogSoftmaxWithLoss.predict" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">predict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/adaptive.html#AdaptiveLogSoftmaxWithLoss.predict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/adaptive.py#L298"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.AdaptiveLogSoftmaxWithLoss.predict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the class with the highest probability for each example in the input minibatch.</p>
 <p>This is equivalent to <code class="docutils literal notranslate"><span class="pre">self.log_prob(input).argmax(dim=1)</span></code>, but is more efficient in some cases.</p>
 <dl class="field-list simple">
diff --git a/main/generated/torch.nn.AdaptiveMaxPool1d.html b/main/generated/torch.nn.AdaptiveMaxPool1d.html
index 796c50115e8..08b7a83927b 100644
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@@ -300,7 +300,7 @@
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+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
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     <div id="searchBox">
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@@ -612,7 +612,7 @@
 <h1>AdaptiveMaxPool1d<a class="headerlink" href="#adaptivemaxpool1d" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.AdaptiveMaxPool1d">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">AdaptiveMaxPool1d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">output_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_indices</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/pooling.html#AdaptiveMaxPool1d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/pooling.py#L1265"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.AdaptiveMaxPool1d" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">AdaptiveMaxPool1d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">output_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_indices</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/pooling.html#AdaptiveMaxPool1d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/pooling.py#L1265"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.AdaptiveMaxPool1d" title="Permalink to this definition">¶</a></dt>
 <dd><p>Applies a 1D adaptive max pooling over an input signal composed of several input planes.</p>
 <p>The output size is <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><msub><mi>L</mi><mrow><mi>o</mi><mi>u</mi><mi>t</mi></mrow></msub></mrow><annotation encoding="application/x-tex">L_{out}</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.8333em;vertical-align:-0.15em;"></span><span class="mord"><span class="mord mathnormal">L</span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.2806em;"><span style="top:-2.55em;margin-left:0em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mord mathnormal mtight">o</span><span class="mord mathnormal mtight">u</span><span class="mord mathnormal mtight">t</span></span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.15em;"><span></span></span></span></span></span></span></span></span></span></span>, for any input size.
 The number of output features is equal to the number of input planes.</p>
diff --git a/main/generated/torch.nn.AdaptiveMaxPool2d.html b/main/generated/torch.nn.AdaptiveMaxPool2d.html
index dae045b8722..fb908fc8092 100644
--- a/main/generated/torch.nn.AdaptiveMaxPool2d.html
+++ b/main/generated/torch.nn.AdaptiveMaxPool2d.html
@@ -300,7 +300,7 @@
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     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
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     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>AdaptiveMaxPool2d<a class="headerlink" href="#adaptivemaxpool2d" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.AdaptiveMaxPool2d">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">AdaptiveMaxPool2d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">output_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_indices</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/pooling.html#AdaptiveMaxPool2d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/pooling.py#L1295"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.AdaptiveMaxPool2d" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">AdaptiveMaxPool2d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">output_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_indices</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/pooling.html#AdaptiveMaxPool2d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/pooling.py#L1295"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.AdaptiveMaxPool2d" title="Permalink to this definition">¶</a></dt>
 <dd><p>Applies a 2D adaptive max pooling over an input signal composed of several input planes.</p>
 <p>The output is of size <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><msub><mi>H</mi><mrow><mi>o</mi><mi>u</mi><mi>t</mi></mrow></msub><mo>×</mo><msub><mi>W</mi><mrow><mi>o</mi><mi>u</mi><mi>t</mi></mrow></msub></mrow><annotation encoding="application/x-tex">H_{out} \times W_{out}</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.8333em;vertical-align:-0.15em;"></span><span class="mord"><span class="mord mathnormal" style="margin-right:0.08125em;">H</span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.2806em;"><span style="top:-2.55em;margin-left:-0.0813em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mord mathnormal mtight">o</span><span class="mord mathnormal mtight">u</span><span class="mord mathnormal mtight">t</span></span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.15em;"><span></span></span></span></span></span></span><span class="mspace" style="margin-right:0.2222em;"></span><span class="mbin">×</span><span class="mspace" style="margin-right:0.2222em;"></span></span><span class="base"><span class="strut" style="height:0.8333em;vertical-align:-0.15em;"></span><span class="mord"><span class="mord mathnormal" style="margin-right:0.13889em;">W</span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.2806em;"><span style="top:-2.55em;margin-left:-0.1389em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mord mathnormal mtight">o</span><span class="mord mathnormal mtight">u</span><span class="mord mathnormal mtight">t</span></span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.15em;"><span></span></span></span></span></span></span></span></span></span></span>, for any input size.
 The number of output features is equal to the number of input planes.</p>
diff --git a/main/generated/torch.nn.AdaptiveMaxPool3d.html b/main/generated/torch.nn.AdaptiveMaxPool3d.html
index 05b24e4f12a..e1f659c87d8 100644
--- a/main/generated/torch.nn.AdaptiveMaxPool3d.html
+++ b/main/generated/torch.nn.AdaptiveMaxPool3d.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
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@@ -612,7 +612,7 @@
 <h1>AdaptiveMaxPool3d<a class="headerlink" href="#adaptivemaxpool3d" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.AdaptiveMaxPool3d">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">AdaptiveMaxPool3d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">output_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_indices</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/pooling.html#AdaptiveMaxPool3d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/pooling.py#L1337"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.AdaptiveMaxPool3d" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">AdaptiveMaxPool3d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">output_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_indices</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/pooling.html#AdaptiveMaxPool3d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/pooling.py#L1337"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.AdaptiveMaxPool3d" title="Permalink to this definition">¶</a></dt>
 <dd><p>Applies a 3D adaptive max pooling over an input signal composed of several input planes.</p>
 <p>The output is of size <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><msub><mi>D</mi><mrow><mi>o</mi><mi>u</mi><mi>t</mi></mrow></msub><mo>×</mo><msub><mi>H</mi><mrow><mi>o</mi><mi>u</mi><mi>t</mi></mrow></msub><mo>×</mo><msub><mi>W</mi><mrow><mi>o</mi><mi>u</mi><mi>t</mi></mrow></msub></mrow><annotation encoding="application/x-tex">D_{out} \times H_{out} \times W_{out}</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.8333em;vertical-align:-0.15em;"></span><span class="mord"><span class="mord mathnormal" style="margin-right:0.02778em;">D</span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.2806em;"><span style="top:-2.55em;margin-left:-0.0278em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mord mathnormal mtight">o</span><span class="mord mathnormal mtight">u</span><span class="mord mathnormal mtight">t</span></span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.15em;"><span></span></span></span></span></span></span><span class="mspace" style="margin-right:0.2222em;"></span><span class="mbin">×</span><span class="mspace" style="margin-right:0.2222em;"></span></span><span class="base"><span class="strut" style="height:0.8333em;vertical-align:-0.15em;"></span><span class="mord"><span class="mord mathnormal" style="margin-right:0.08125em;">H</span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.2806em;"><span style="top:-2.55em;margin-left:-0.0813em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mord mathnormal mtight">o</span><span class="mord mathnormal mtight">u</span><span class="mord mathnormal mtight">t</span></span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.15em;"><span></span></span></span></span></span></span><span class="mspace" style="margin-right:0.2222em;"></span><span class="mbin">×</span><span class="mspace" style="margin-right:0.2222em;"></span></span><span class="base"><span class="strut" style="height:0.8333em;vertical-align:-0.15em;"></span><span class="mord"><span class="mord mathnormal" style="margin-right:0.13889em;">W</span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.2806em;"><span style="top:-2.55em;margin-left:-0.1389em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mord mathnormal mtight">o</span><span class="mord mathnormal mtight">u</span><span class="mord mathnormal mtight">t</span></span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.15em;"><span></span></span></span></span></span></span></span></span></span></span>, for any input size.
 The number of output features is equal to the number of input planes.</p>
diff --git a/main/generated/torch.nn.AlphaDropout.html b/main/generated/torch.nn.AlphaDropout.html
index cffac252a0a..c85c979e944 100644
--- a/main/generated/torch.nn.AlphaDropout.html
+++ b/main/generated/torch.nn.AlphaDropout.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
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+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>AlphaDropout<a class="headerlink" href="#alphadropout" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.AlphaDropout">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">AlphaDropout</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">p</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/dropout.html#AlphaDropout"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/dropout.py#L215"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.AlphaDropout" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">AlphaDropout</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">p</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/dropout.html#AlphaDropout"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/dropout.py#L215"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.AlphaDropout" title="Permalink to this definition">¶</a></dt>
 <dd><p>Applies Alpha Dropout over the input.</p>
 <p>Alpha Dropout is a type of Dropout that maintains the self-normalizing
 property.
diff --git a/main/generated/torch.nn.AvgPool1d.html b/main/generated/torch.nn.AvgPool1d.html
index 75938e1194c..8701f2994bc 100644
--- a/main/generated/torch.nn.AvgPool1d.html
+++ b/main/generated/torch.nn.AvgPool1d.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>AvgPool1d<a class="headerlink" href="#avgpool1d" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.AvgPool1d">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">AvgPool1d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ceil_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">count_include_pad</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/pooling.html#AvgPool1d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/pooling.py#L580"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.AvgPool1d" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">AvgPool1d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ceil_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">count_include_pad</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/pooling.html#AvgPool1d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/pooling.py#L580"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.AvgPool1d" title="Permalink to this definition">¶</a></dt>
 <dd><p>Applies a 1D average pooling over an input signal composed of several input planes.</p>
 <p>In the simplest case, the output value of the layer with input size <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mo stretchy="false">(</mo><mi>N</mi><mo separator="true">,</mo><mi>C</mi><mo separator="true">,</mo><mi>L</mi><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">(N, C, L)</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mopen">(</span><span class="mord mathnormal" style="margin-right:0.10903em;">N</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord mathnormal" style="margin-right:0.07153em;">C</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord mathnormal">L</span><span class="mclose">)</span></span></span></span></span>,
 output <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mo stretchy="false">(</mo><mi>N</mi><mo separator="true">,</mo><mi>C</mi><mo separator="true">,</mo><msub><mi>L</mi><mrow><mi>o</mi><mi>u</mi><mi>t</mi></mrow></msub><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">(N, C, L_{out})</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mopen">(</span><span class="mord mathnormal" style="margin-right:0.10903em;">N</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord mathnormal" style="margin-right:0.07153em;">C</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord"><span class="mord mathnormal">L</span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.2806em;"><span style="top:-2.55em;margin-left:0em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mord mathnormal mtight">o</span><span class="mord mathnormal mtight">u</span><span class="mord mathnormal mtight">t</span></span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.15em;"><span></span></span></span></span></span></span><span class="mclose">)</span></span></span></span></span> and <code class="xref py py-attr docutils literal notranslate"><span class="pre">kernel_size</span></code> <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>k</mi></mrow><annotation encoding="application/x-tex">k</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.6944em;"></span><span class="mord mathnormal" style="margin-right:0.03148em;">k</span></span></span></span></span>
diff --git a/main/generated/torch.nn.AvgPool2d.html b/main/generated/torch.nn.AvgPool2d.html
index 136ad59fff7..1862bf1105d 100644
--- a/main/generated/torch.nn.AvgPool2d.html
+++ b/main/generated/torch.nn.AvgPool2d.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>AvgPool2d<a class="headerlink" href="#avgpool2d" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.AvgPool2d">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">AvgPool2d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ceil_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">count_include_pad</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">divisor_override</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/pooling.html#AvgPool2d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/pooling.py#L661"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.AvgPool2d" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">AvgPool2d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ceil_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">count_include_pad</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">divisor_override</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/pooling.html#AvgPool2d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/pooling.py#L661"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.AvgPool2d" title="Permalink to this definition">¶</a></dt>
 <dd><p>Applies a 2D average pooling over an input signal composed of several input planes.</p>
 <p>In the simplest case, the output value of the layer with input size <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mo stretchy="false">(</mo><mi>N</mi><mo separator="true">,</mo><mi>C</mi><mo separator="true">,</mo><mi>H</mi><mo separator="true">,</mo><mi>W</mi><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">(N, C, H, W)</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mopen">(</span><span class="mord mathnormal" style="margin-right:0.10903em;">N</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord mathnormal" style="margin-right:0.07153em;">C</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord mathnormal" style="margin-right:0.08125em;">H</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord mathnormal" style="margin-right:0.13889em;">W</span><span class="mclose">)</span></span></span></span></span>,
 output <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mo stretchy="false">(</mo><mi>N</mi><mo separator="true">,</mo><mi>C</mi><mo separator="true">,</mo><msub><mi>H</mi><mrow><mi>o</mi><mi>u</mi><mi>t</mi></mrow></msub><mo separator="true">,</mo><msub><mi>W</mi><mrow><mi>o</mi><mi>u</mi><mi>t</mi></mrow></msub><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">(N, C, H_{out}, W_{out})</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mopen">(</span><span class="mord mathnormal" style="margin-right:0.10903em;">N</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord mathnormal" style="margin-right:0.07153em;">C</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord"><span class="mord mathnormal" style="margin-right:0.08125em;">H</span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.2806em;"><span style="top:-2.55em;margin-left:-0.0813em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mord mathnormal mtight">o</span><span class="mord mathnormal mtight">u</span><span class="mord mathnormal mtight">t</span></span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.15em;"><span></span></span></span></span></span></span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord"><span class="mord mathnormal" style="margin-right:0.13889em;">W</span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.2806em;"><span style="top:-2.55em;margin-left:-0.1389em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mord mathnormal mtight">o</span><span class="mord mathnormal mtight">u</span><span class="mord mathnormal mtight">t</span></span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.15em;"><span></span></span></span></span></span></span><span class="mclose">)</span></span></span></span></span> and <code class="xref py py-attr docutils literal notranslate"><span class="pre">kernel_size</span></code> <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mo stretchy="false">(</mo><mi>k</mi><mi>H</mi><mo separator="true">,</mo><mi>k</mi><mi>W</mi><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">(kH, kW)</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mopen">(</span><span class="mord mathnormal" style="margin-right:0.03148em;">k</span><span class="mord mathnormal" style="margin-right:0.08125em;">H</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord mathnormal" style="margin-right:0.13889em;">kW</span><span class="mclose">)</span></span></span></span></span>
diff --git a/main/generated/torch.nn.AvgPool3d.html b/main/generated/torch.nn.AvgPool3d.html
index d23d2cc1ddd..512088b7c2e 100644
--- a/main/generated/torch.nn.AvgPool3d.html
+++ b/main/generated/torch.nn.AvgPool3d.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>AvgPool3d<a class="headerlink" href="#avgpool3d" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.AvgPool3d">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">AvgPool3d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ceil_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">count_include_pad</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">divisor_override</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/pooling.html#AvgPool3d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/pooling.py#L767"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.AvgPool3d" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">AvgPool3d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ceil_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">count_include_pad</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">divisor_override</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/pooling.html#AvgPool3d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/pooling.py#L767"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.AvgPool3d" title="Permalink to this definition">¶</a></dt>
 <dd><p>Applies a 3D average pooling over an input signal composed of several input planes.</p>
 <p>In the simplest case, the output value of the layer with input size <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mo stretchy="false">(</mo><mi>N</mi><mo separator="true">,</mo><mi>C</mi><mo separator="true">,</mo><mi>D</mi><mo separator="true">,</mo><mi>H</mi><mo separator="true">,</mo><mi>W</mi><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">(N, C, D, H, W)</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mopen">(</span><span class="mord mathnormal" style="margin-right:0.10903em;">N</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord mathnormal" style="margin-right:0.07153em;">C</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord mathnormal" style="margin-right:0.02778em;">D</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord mathnormal" style="margin-right:0.08125em;">H</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord mathnormal" style="margin-right:0.13889em;">W</span><span class="mclose">)</span></span></span></span></span>,
 output <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mo stretchy="false">(</mo><mi>N</mi><mo separator="true">,</mo><mi>C</mi><mo separator="true">,</mo><msub><mi>D</mi><mrow><mi>o</mi><mi>u</mi><mi>t</mi></mrow></msub><mo separator="true">,</mo><msub><mi>H</mi><mrow><mi>o</mi><mi>u</mi><mi>t</mi></mrow></msub><mo separator="true">,</mo><msub><mi>W</mi><mrow><mi>o</mi><mi>u</mi><mi>t</mi></mrow></msub><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">(N, C, D_{out}, H_{out}, W_{out})</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mopen">(</span><span class="mord mathnormal" style="margin-right:0.10903em;">N</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord mathnormal" style="margin-right:0.07153em;">C</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord"><span class="mord mathnormal" style="margin-right:0.02778em;">D</span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.2806em;"><span style="top:-2.55em;margin-left:-0.0278em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mord mathnormal mtight">o</span><span class="mord mathnormal mtight">u</span><span class="mord mathnormal mtight">t</span></span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.15em;"><span></span></span></span></span></span></span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord"><span class="mord mathnormal" style="margin-right:0.08125em;">H</span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.2806em;"><span style="top:-2.55em;margin-left:-0.0813em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mord mathnormal mtight">o</span><span class="mord mathnormal mtight">u</span><span class="mord mathnormal mtight">t</span></span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.15em;"><span></span></span></span></span></span></span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord"><span class="mord mathnormal" style="margin-right:0.13889em;">W</span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.2806em;"><span style="top:-2.55em;margin-left:-0.1389em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mord mathnormal mtight">o</span><span class="mord mathnormal mtight">u</span><span class="mord mathnormal mtight">t</span></span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.15em;"><span></span></span></span></span></span></span><span class="mclose">)</span></span></span></span></span> and <code class="xref py py-attr docutils literal notranslate"><span class="pre">kernel_size</span></code> <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mo stretchy="false">(</mo><mi>k</mi><mi>D</mi><mo separator="true">,</mo><mi>k</mi><mi>H</mi><mo separator="true">,</mo><mi>k</mi><mi>W</mi><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">(kD, kH, kW)</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mopen">(</span><span class="mord mathnormal" style="margin-right:0.03148em;">k</span><span class="mord mathnormal" style="margin-right:0.02778em;">D</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord mathnormal" style="margin-right:0.03148em;">k</span><span class="mord mathnormal" style="margin-right:0.08125em;">H</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord mathnormal" style="margin-right:0.13889em;">kW</span><span class="mclose">)</span></span></span></span></span>
diff --git a/main/generated/torch.nn.BCELoss.html b/main/generated/torch.nn.BCELoss.html
index 428aa540a3c..b718d4bbc90 100644
--- a/main/generated/torch.nn.BCELoss.html
+++ b/main/generated/torch.nn.BCELoss.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>BCELoss<a class="headerlink" href="#bceloss" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.BCELoss">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">BCELoss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">size_average</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduce</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mean'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/loss.html#BCELoss"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/loss.py#L613"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.BCELoss" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">BCELoss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">size_average</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduce</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mean'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/loss.html#BCELoss"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/loss.py#L613"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.BCELoss" title="Permalink to this definition">¶</a></dt>
 <dd><p>Creates a criterion that measures the Binary Cross Entropy between the target and
 the input probabilities:</p>
 <p>The unreduced (i.e. with <code class="xref py py-attr docutils literal notranslate"><span class="pre">reduction</span></code> set to <code class="docutils literal notranslate"><span class="pre">'none'</span></code>) loss can be described as:</p>
diff --git a/main/generated/torch.nn.BCEWithLogitsLoss.html b/main/generated/torch.nn.BCEWithLogitsLoss.html
index e54bbcb814d..8f3adc87311 100644
--- a/main/generated/torch.nn.BCEWithLogitsLoss.html
+++ b/main/generated/torch.nn.BCEWithLogitsLoss.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>BCEWithLogitsLoss<a class="headerlink" href="#bcewithlogitsloss" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.BCEWithLogitsLoss">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">BCEWithLogitsLoss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">size_average</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduce</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mean'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pos_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/loss.html#BCEWithLogitsLoss"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/loss.py#L704"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.BCEWithLogitsLoss" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">BCEWithLogitsLoss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">size_average</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduce</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mean'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pos_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/loss.html#BCEWithLogitsLoss"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/loss.py#L704"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.BCEWithLogitsLoss" title="Permalink to this definition">¶</a></dt>
 <dd><p>This loss combines a <cite>Sigmoid</cite> layer and the <cite>BCELoss</cite> in one single
 class. This version is more numerically stable than using a plain <cite>Sigmoid</cite>
 followed by a <cite>BCELoss</cite> as, by combining the operations into one layer,
diff --git a/main/generated/torch.nn.BatchNorm1d.html b/main/generated/torch.nn.BatchNorm1d.html
index 0593e46bafc..5111bf3401c 100644
--- a/main/generated/torch.nn.BatchNorm1d.html
+++ b/main/generated/torch.nn.BatchNorm1d.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>BatchNorm1d<a class="headerlink" href="#batchnorm1d" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.BatchNorm1d">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">BatchNorm1d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">num_features</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-05</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">momentum</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">affine</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">track_running_stats</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/batchnorm.html#BatchNorm1d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/batchnorm.py#L268"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.BatchNorm1d" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">BatchNorm1d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">num_features</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-05</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">momentum</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">affine</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">track_running_stats</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/batchnorm.html#BatchNorm1d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/batchnorm.py#L268"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.BatchNorm1d" title="Permalink to this definition">¶</a></dt>
 <dd><p>Applies Batch Normalization over a 2D or 3D input.</p>
 <p>Method described in the paper
 <a class="reference external" href="https://arxiv.org/abs/1502.03167">Batch Normalization: Accelerating Deep Network Training by Reducing
diff --git a/main/generated/torch.nn.BatchNorm2d.html b/main/generated/torch.nn.BatchNorm2d.html
index 999f775800b..19a388ccdc1 100644
--- a/main/generated/torch.nn.BatchNorm2d.html
+++ b/main/generated/torch.nn.BatchNorm2d.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>BatchNorm2d<a class="headerlink" href="#batchnorm2d" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.BatchNorm2d">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">BatchNorm2d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">num_features</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-05</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">momentum</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">affine</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">track_running_stats</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/batchnorm.html#BatchNorm2d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/batchnorm.py#L378"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.BatchNorm2d" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">BatchNorm2d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">num_features</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-05</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">momentum</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">affine</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">track_running_stats</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/batchnorm.html#BatchNorm2d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/batchnorm.py#L378"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.BatchNorm2d" title="Permalink to this definition">¶</a></dt>
 <dd><p>Applies Batch Normalization over a 4D input.</p>
 <p>4D is a mini-batch of 2D inputs
 with additional channel dimension. Method described in the paper
diff --git a/main/generated/torch.nn.BatchNorm3d.html b/main/generated/torch.nn.BatchNorm3d.html
index f5a0b4e5abb..4abd2e92c26 100644
--- a/main/generated/torch.nn.BatchNorm3d.html
+++ b/main/generated/torch.nn.BatchNorm3d.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>BatchNorm3d<a class="headerlink" href="#batchnorm3d" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.BatchNorm3d">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">BatchNorm3d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">num_features</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-05</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">momentum</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">affine</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">track_running_stats</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/batchnorm.html#BatchNorm3d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/batchnorm.py#L489"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.BatchNorm3d" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">BatchNorm3d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">num_features</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-05</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">momentum</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">affine</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">track_running_stats</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/batchnorm.html#BatchNorm3d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/batchnorm.py#L489"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.BatchNorm3d" title="Permalink to this definition">¶</a></dt>
 <dd><p>Applies Batch Normalization over a 5D input.</p>
 <p>5D is a mini-batch of 3D inputs with additional channel dimension as described in the paper
 <a class="reference external" href="https://arxiv.org/abs/1502.03167">Batch Normalization: Accelerating Deep Network Training by Reducing
diff --git a/main/generated/torch.nn.Bilinear.html b/main/generated/torch.nn.Bilinear.html
index 7ee01d78bca..633ea11194d 100644
--- a/main/generated/torch.nn.Bilinear.html
+++ b/main/generated/torch.nn.Bilinear.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>Bilinear<a class="headerlink" href="#bilinear" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.Bilinear">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">Bilinear</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">in1_features</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">in2_features</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out_features</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/linear.html#Bilinear"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/linear.py#L150"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Bilinear" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">Bilinear</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">in1_features</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">in2_features</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out_features</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/linear.html#Bilinear"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/linear.py#L150"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Bilinear" title="Permalink to this definition">¶</a></dt>
 <dd><p>Applies a bilinear transformation to the incoming data: <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>y</mi><mo>=</mo><msubsup><mi>x</mi><mn>1</mn><mi>T</mi></msubsup><mi>A</mi><msub><mi>x</mi><mn>2</mn></msub><mo>+</mo><mi>b</mi></mrow><annotation encoding="application/x-tex">y = x_1^T A x_2 + b</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.625em;vertical-align:-0.1944em;"></span><span class="mord mathnormal" style="margin-right:0.03588em;">y</span><span class="mspace" style="margin-right:0.2778em;"></span><span class="mrel">=</span><span class="mspace" style="margin-right:0.2778em;"></span></span><span class="base"><span class="strut" style="height:1.0894em;vertical-align:-0.2481em;"></span><span class="mord"><span class="mord mathnormal">x</span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.8413em;"><span style="top:-2.4519em;margin-left:0em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight">1</span></span></span><span style="top:-3.063em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mathnormal mtight" style="margin-right:0.13889em;">T</span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.2481em;"><span></span></span></span></span></span></span><span class="mord mathnormal">A</span><span class="mord"><span class="mord mathnormal">x</span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.3011em;"><span style="top:-2.55em;margin-left:0em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight">2</span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.15em;"><span></span></span></span></span></span></span><span class="mspace" style="margin-right:0.2222em;"></span><span class="mbin">+</span><span class="mspace" style="margin-right:0.2222em;"></span></span><span class="base"><span class="strut" style="height:0.6944em;"></span><span class="mord mathnormal">b</span></span></span></span></span>.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.nn.CELU.html b/main/generated/torch.nn.CELU.html
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           <div class="pytorch-left-menu-search">
             
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@@ -612,7 +612,7 @@
 <h1>CELU<a class="headerlink" href="#celu" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.CELU">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">CELU</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/activation.html#CELU"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/activation.py#L568"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.CELU" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">CELU</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/activation.html#CELU"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/activation.py#L568"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.CELU" title="Permalink to this definition">¶</a></dt>
 <dd><p>Applies the CELU function element-wise.</p>
 <div class="math">
 <span class="katex-display"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML" display="block"><semantics><mrow><mtext>CELU</mtext><mo stretchy="false">(</mo><mi>x</mi><mo stretchy="false">)</mo><mo>=</mo><mi>max</mi><mo>⁡</mo><mo stretchy="false">(</mo><mn>0</mn><mo separator="true">,</mo><mi>x</mi><mo stretchy="false">)</mo><mo>+</mo><mi>min</mi><mo>⁡</mo><mo stretchy="false">(</mo><mn>0</mn><mo separator="true">,</mo><mi>α</mi><mo>∗</mo><mo stretchy="false">(</mo><mi>exp</mi><mo>⁡</mo><mo stretchy="false">(</mo><mi>x</mi><mi mathvariant="normal">/</mi><mi>α</mi><mo stretchy="false">)</mo><mo>−</mo><mn>1</mn><mo stretchy="false">)</mo><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">\text{CELU}(x) = \max(0,x) + \min(0, \alpha * (\exp(x/\alpha) - 1))
diff --git a/main/generated/torch.nn.CTCLoss.html b/main/generated/torch.nn.CTCLoss.html
index de007c89f9f..c2c4191b07f 100644
--- a/main/generated/torch.nn.CTCLoss.html
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           <div class="pytorch-left-menu-search">
             
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     </div>
     <div id="searchBox">
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@@ -612,7 +612,7 @@
 <h1>CTCLoss<a class="headerlink" href="#ctcloss" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.CTCLoss">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">CTCLoss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">blank</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mean'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">zero_infinity</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/loss.html#CTCLoss"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/loss.py#L1830"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.CTCLoss" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">CTCLoss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">blank</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mean'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">zero_infinity</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/loss.html#CTCLoss"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/loss.py#L1830"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.CTCLoss" title="Permalink to this definition">¶</a></dt>
 <dd><p>The Connectionist Temporal Classification loss.</p>
 <p>Calculates loss between a continuous (unsegmented) time series and a target sequence. CTCLoss sums over the
 probability of possible alignments of input to target, producing a loss value which is differentiable
diff --git a/main/generated/torch.nn.ChannelShuffle.html b/main/generated/torch.nn.ChannelShuffle.html
index d3088ebab99..bf4e6df6994 100644
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@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
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     </div>
     <div id="searchBox">
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@@ -612,7 +612,7 @@
 <h1>ChannelShuffle<a class="headerlink" href="#channelshuffle" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.ChannelShuffle">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">ChannelShuffle</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">groups</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/channelshuffle.html#ChannelShuffle"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/channelshuffle.py#L10"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.ChannelShuffle" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">ChannelShuffle</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">groups</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/channelshuffle.html#ChannelShuffle"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/channelshuffle.py#L10"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.ChannelShuffle" title="Permalink to this definition">¶</a></dt>
 <dd><p>Divides and rearranges the channels in a tensor.</p>
 <p>This operation divides the channels in a tensor of shape <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mo stretchy="false">(</mo><mi>N</mi><mo separator="true">,</mo><mi>C</mi><mo separator="true">,</mo><mo>∗</mo><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">(N, C, *)</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mopen">(</span><span class="mord mathnormal" style="margin-right:0.10903em;">N</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord mathnormal" style="margin-right:0.07153em;">C</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord">∗</span><span class="mclose">)</span></span></span></span></span>
 into g groups as <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mo stretchy="false">(</mo><mi>N</mi><mo separator="true">,</mo><mfrac><mi>C</mi><mi>g</mi></mfrac><mo separator="true">,</mo><mi>g</mi><mo separator="true">,</mo><mo>∗</mo><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">(N, \frac{C}{g}, g, *)</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1.3534em;vertical-align:-0.4811em;"></span><span class="mopen">(</span><span class="mord mathnormal" style="margin-right:0.10903em;">N</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord"><span class="mopen nulldelimiter"></span><span class="mfrac"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.8723em;"><span style="top:-2.655em;"><span class="pstrut" style="height:3em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mord mathnormal mtight" style="margin-right:0.03588em;">g</span></span></span></span><span style="top:-3.23em;"><span class="pstrut" style="height:3em;"></span><span class="frac-line" style="border-bottom-width:0.04em;"></span></span><span style="top:-3.394em;"><span class="pstrut" style="height:3em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mord mathnormal mtight" style="margin-right:0.07153em;">C</span></span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.4811em;"><span></span></span></span></span></span><span class="mclose nulldelimiter"></span></span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord mathnormal" style="margin-right:0.03588em;">g</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord">∗</span><span class="mclose">)</span></span></span></span></span> and shuffles them,
diff --git a/main/generated/torch.nn.CircularPad1d.html b/main/generated/torch.nn.CircularPad1d.html
index 352cd565f20..dbadde4a541 100644
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           <div class="pytorch-left-menu-search">
             
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     <div id="searchBox">
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@@ -612,7 +612,7 @@
 <h1>CircularPad1d<a class="headerlink" href="#circularpad1d" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.CircularPad1d">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">CircularPad1d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">padding</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/padding.html#CircularPad1d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/padding.py#L48"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.CircularPad1d" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">CircularPad1d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">padding</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/padding.html#CircularPad1d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/padding.py#L48"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.CircularPad1d" title="Permalink to this definition">¶</a></dt>
 <dd><p>Pads the input tensor using circular padding of the input boundary.</p>
 <p>Tensor values at the beginning of the dimension are used to pad the end,
 and values at the end are used to pad the beginning. If negative padding is
diff --git a/main/generated/torch.nn.CircularPad2d.html b/main/generated/torch.nn.CircularPad2d.html
index e7299c42ea1..1ee232b1cd7 100644
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           <div class="pytorch-left-menu-search">
             
     <div class="version">
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     <div id="searchBox">
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@@ -612,7 +612,7 @@
 <h1>CircularPad2d<a class="headerlink" href="#circularpad2d" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.CircularPad2d">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">CircularPad2d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">padding</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/padding.html#CircularPad2d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/padding.py#L97"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.CircularPad2d" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">CircularPad2d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">padding</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/padding.html#CircularPad2d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/padding.py#L97"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.CircularPad2d" title="Permalink to this definition">¶</a></dt>
 <dd><p>Pads the input tensor using circular padding of the input boundary.</p>
 <p>Tensor values at the beginning of the dimension are used to pad the end,
 and values at the end are used to pad the beginning. If negative padding is
diff --git a/main/generated/torch.nn.CircularPad3d.html b/main/generated/torch.nn.CircularPad3d.html
index ba609259ab7..14bf6e98328 100644
--- a/main/generated/torch.nn.CircularPad3d.html
+++ b/main/generated/torch.nn.CircularPad3d.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>CircularPad3d<a class="headerlink" href="#circularpad3d" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.CircularPad3d">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">CircularPad3d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">padding</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/padding.html#CircularPad3d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/padding.py#L156"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.CircularPad3d" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">CircularPad3d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">padding</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/padding.html#CircularPad3d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/padding.py#L156"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.CircularPad3d" title="Permalink to this definition">¶</a></dt>
 <dd><p>Pads the input tensor using circular padding of the input boundary.</p>
 <p>Tensor values at the beginning of the dimension are used to pad the end,
 and values at the end are used to pad the beginning. If negative padding is
diff --git a/main/generated/torch.nn.ConstantPad1d.html b/main/generated/torch.nn.ConstantPad1d.html
index 9e3da17ecd1..40cbf2455c2 100644
--- a/main/generated/torch.nn.ConstantPad1d.html
+++ b/main/generated/torch.nn.ConstantPad1d.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>ConstantPad1d<a class="headerlink" href="#constantpad1d" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.ConstantPad1d">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">ConstantPad1d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">padding</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/padding.html#ConstantPad1d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/padding.py#L221"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.ConstantPad1d" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">ConstantPad1d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">padding</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/padding.html#ConstantPad1d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/padding.py#L221"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.ConstantPad1d" title="Permalink to this definition">¶</a></dt>
 <dd><p>Pads the input tensor boundaries with a constant value.</p>
 <p>For <cite>N</cite>-dimensional padding, use <a class="reference internal" href="torch.nn.functional.pad.html#torch.nn.functional.pad" title="torch.nn.functional.pad"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.nn.functional.pad()</span></code></a>.</p>
 <dl class="field-list simple">
diff --git a/main/generated/torch.nn.ConstantPad2d.html b/main/generated/torch.nn.ConstantPad2d.html
index 4c55faa09cd..24eb611547f 100644
--- a/main/generated/torch.nn.ConstantPad2d.html
+++ b/main/generated/torch.nn.ConstantPad2d.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>ConstantPad2d<a class="headerlink" href="#constantpad2d" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.ConstantPad2d">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">ConstantPad2d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">padding</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/padding.html#ConstantPad2d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/padding.py#L272"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.ConstantPad2d" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">ConstantPad2d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">padding</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/padding.html#ConstantPad2d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/padding.py#L272"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.ConstantPad2d" title="Permalink to this definition">¶</a></dt>
 <dd><p>Pads the input tensor boundaries with a constant value.</p>
 <p>For <cite>N</cite>-dimensional padding, use <a class="reference internal" href="torch.nn.functional.pad.html#torch.nn.functional.pad" title="torch.nn.functional.pad"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.nn.functional.pad()</span></code></a>.</p>
 <dl class="field-list simple">
diff --git a/main/generated/torch.nn.ConstantPad3d.html b/main/generated/torch.nn.ConstantPad3d.html
index ce6b8ca67d8..dd52de55723 100644
--- a/main/generated/torch.nn.ConstantPad3d.html
+++ b/main/generated/torch.nn.ConstantPad3d.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>ConstantPad3d<a class="headerlink" href="#constantpad3d" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.ConstantPad3d">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">ConstantPad3d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">padding</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/padding.html#ConstantPad3d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/padding.py#L323"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.ConstantPad3d" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">ConstantPad3d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">padding</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/padding.html#ConstantPad3d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/padding.py#L323"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.ConstantPad3d" title="Permalink to this definition">¶</a></dt>
 <dd><p>Pads the input tensor boundaries with a constant value.</p>
 <p>For <cite>N</cite>-dimensional padding, use <a class="reference internal" href="torch.nn.functional.pad.html#torch.nn.functional.pad" title="torch.nn.functional.pad"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.nn.functional.pad()</span></code></a>.</p>
 <dl class="field-list simple">
diff --git a/main/generated/torch.nn.Conv1d.html b/main/generated/torch.nn.Conv1d.html
index fce46ee87b6..812d32211c3 100644
--- a/main/generated/torch.nn.Conv1d.html
+++ b/main/generated/torch.nn.Conv1d.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>Conv1d<a class="headerlink" href="#conv1d" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.Conv1d">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">Conv1d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">in_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dilation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'zeros'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/conv.html#Conv1d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/conv.py#L214"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Conv1d" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">Conv1d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">in_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dilation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'zeros'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/conv.html#Conv1d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/conv.py#L214"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Conv1d" title="Permalink to this definition">¶</a></dt>
 <dd><p>Applies a 1D convolution over an input signal composed of several input
 planes.</p>
 <p>In the simplest case, the output value of the layer with input size
diff --git a/main/generated/torch.nn.Conv2d.html b/main/generated/torch.nn.Conv2d.html
index 32d31b2a5d4..3f19f3b3c24 100644
--- a/main/generated/torch.nn.Conv2d.html
+++ b/main/generated/torch.nn.Conv2d.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>Conv2d<a class="headerlink" href="#conv2d" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.Conv2d">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">Conv2d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">in_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dilation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'zeros'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/conv.html#Conv2d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/conv.py#L378"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Conv2d" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">Conv2d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">in_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dilation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'zeros'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/conv.html#Conv2d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/conv.py#L378"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Conv2d" title="Permalink to this definition">¶</a></dt>
 <dd><p>Applies a 2D convolution over an input signal composed of several input
 planes.</p>
 <p>In the simplest case, the output value of the layer with input size
diff --git a/main/generated/torch.nn.Conv3d.html b/main/generated/torch.nn.Conv3d.html
index 6782951a69f..d87f9dbf384 100644
--- a/main/generated/torch.nn.Conv3d.html
+++ b/main/generated/torch.nn.Conv3d.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>Conv3d<a class="headerlink" href="#conv3d" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.Conv3d">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">Conv3d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">in_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dilation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'zeros'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/conv.html#Conv3d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/conv.py#L557"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Conv3d" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">Conv3d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">in_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dilation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'zeros'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/conv.html#Conv3d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/conv.py#L557"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Conv3d" title="Permalink to this definition">¶</a></dt>
 <dd><p>Applies a 3D convolution over an input signal composed of several input
 planes.</p>
 <p>In the simplest case, the output value of the layer with input size <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mo stretchy="false">(</mo><mi>N</mi><mo separator="true">,</mo><msub><mi>C</mi><mrow><mi>i</mi><mi>n</mi></mrow></msub><mo separator="true">,</mo><mi>D</mi><mo separator="true">,</mo><mi>H</mi><mo separator="true">,</mo><mi>W</mi><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">(N, C_{in}, D, H, W)</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mopen">(</span><span class="mord mathnormal" style="margin-right:0.10903em;">N</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord"><span class="mord mathnormal" style="margin-right:0.07153em;">C</span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.3117em;"><span style="top:-2.55em;margin-left:-0.0715em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mord mathnormal mtight">in</span></span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.15em;"><span></span></span></span></span></span></span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord mathnormal" style="margin-right:0.02778em;">D</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord mathnormal" style="margin-right:0.08125em;">H</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord mathnormal" style="margin-right:0.13889em;">W</span><span class="mclose">)</span></span></span></span></span>
diff --git a/main/generated/torch.nn.ConvTranspose1d.html b/main/generated/torch.nn.ConvTranspose1d.html
index 217952a75a0..20d08392570 100644
--- a/main/generated/torch.nn.ConvTranspose1d.html
+++ b/main/generated/torch.nn.ConvTranspose1d.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>ConvTranspose1d<a class="headerlink" href="#convtranspose1d" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.ConvTranspose1d">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">ConvTranspose1d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">in_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dilation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'zeros'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/conv.html#ConvTranspose1d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/conv.py#L822"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.ConvTranspose1d" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">ConvTranspose1d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">in_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dilation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'zeros'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/conv.html#ConvTranspose1d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/conv.py#L822"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.ConvTranspose1d" title="Permalink to this definition">¶</a></dt>
 <dd><p>Applies a 1D transposed convolution operator over an input image
 composed of several input planes.</p>
 <p>This module can be seen as the gradient of Conv1d with respect to its input.
diff --git a/main/generated/torch.nn.ConvTranspose2d.html b/main/generated/torch.nn.ConvTranspose2d.html
index b73db7c7c4f..1ecd20f064a 100644
--- a/main/generated/torch.nn.ConvTranspose2d.html
+++ b/main/generated/torch.nn.ConvTranspose2d.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>ConvTranspose2d<a class="headerlink" href="#convtranspose2d" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.ConvTranspose2d">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">ConvTranspose2d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">in_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dilation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'zeros'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/conv.html#ConvTranspose2d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/conv.py#L986"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.ConvTranspose2d" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">ConvTranspose2d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">in_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dilation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'zeros'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/conv.html#ConvTranspose2d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/conv.py#L986"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.ConvTranspose2d" title="Permalink to this definition">¶</a></dt>
 <dd><p>Applies a 2D transposed convolution operator over an input image
 composed of several input planes.</p>
 <p>This module can be seen as the gradient of Conv2d with respect to its input.
diff --git a/main/generated/torch.nn.ConvTranspose3d.html b/main/generated/torch.nn.ConvTranspose3d.html
index 7f7a8a3430e..1856992b90c 100644
--- a/main/generated/torch.nn.ConvTranspose3d.html
+++ b/main/generated/torch.nn.ConvTranspose3d.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>ConvTranspose3d<a class="headerlink" href="#convtranspose3d" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.ConvTranspose3d">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">ConvTranspose3d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">in_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dilation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'zeros'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/conv.html#ConvTranspose3d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/conv.py#L1174"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.ConvTranspose3d" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">ConvTranspose3d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">in_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dilation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'zeros'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/conv.html#ConvTranspose3d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/conv.py#L1174"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.ConvTranspose3d" title="Permalink to this definition">¶</a></dt>
 <dd><p>Applies a 3D transposed convolution operator over an input image composed of several input
 planes.
 The transposed convolution operator multiplies each input value element-wise by a learnable kernel,
diff --git a/main/generated/torch.nn.CosineEmbeddingLoss.html b/main/generated/torch.nn.CosineEmbeddingLoss.html
index 73662b17d72..ce4d6ae57b1 100644
--- a/main/generated/torch.nn.CosineEmbeddingLoss.html
+++ b/main/generated/torch.nn.CosineEmbeddingLoss.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>CosineEmbeddingLoss<a class="headerlink" href="#cosineembeddingloss" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.CosineEmbeddingLoss">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">CosineEmbeddingLoss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">margin</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">size_average</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduce</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mean'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/loss.html#CosineEmbeddingLoss"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/loss.py#L1360"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.CosineEmbeddingLoss" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">CosineEmbeddingLoss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">margin</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">size_average</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduce</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mean'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/loss.html#CosineEmbeddingLoss"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/loss.py#L1360"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.CosineEmbeddingLoss" title="Permalink to this definition">¶</a></dt>
 <dd><p>Creates a criterion that measures the loss given input tensors
 <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><msub><mi>x</mi><mn>1</mn></msub></mrow><annotation encoding="application/x-tex">x_1</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.5806em;vertical-align:-0.15em;"></span><span class="mord"><span class="mord mathnormal">x</span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.3011em;"><span style="top:-2.55em;margin-left:0em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight">1</span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.15em;"><span></span></span></span></span></span></span></span></span></span></span>, <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><msub><mi>x</mi><mn>2</mn></msub></mrow><annotation encoding="application/x-tex">x_2</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.5806em;vertical-align:-0.15em;"></span><span class="mord"><span class="mord mathnormal">x</span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.3011em;"><span style="top:-2.55em;margin-left:0em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight">2</span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.15em;"><span></span></span></span></span></span></span></span></span></span></span> and a <cite>Tensor</cite> label <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>y</mi></mrow><annotation encoding="application/x-tex">y</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.625em;vertical-align:-0.1944em;"></span><span class="mord mathnormal" style="margin-right:0.03588em;">y</span></span></span></span></span> with values 1 or -1.
 Use (<span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>y</mi><mo>=</mo><mn>1</mn></mrow><annotation encoding="application/x-tex">y=1</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.625em;vertical-align:-0.1944em;"></span><span class="mord mathnormal" style="margin-right:0.03588em;">y</span><span class="mspace" style="margin-right:0.2778em;"></span><span class="mrel">=</span><span class="mspace" style="margin-right:0.2778em;"></span></span><span class="base"><span class="strut" style="height:0.6444em;"></span><span class="mord">1</span></span></span></span></span>) to maximize the cosine similarity of two inputs, and (<span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>y</mi><mo>=</mo><mo>−</mo><mn>1</mn></mrow><annotation encoding="application/x-tex">y=-1</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.625em;vertical-align:-0.1944em;"></span><span class="mord mathnormal" style="margin-right:0.03588em;">y</span><span class="mspace" style="margin-right:0.2778em;"></span><span class="mrel">=</span><span class="mspace" style="margin-right:0.2778em;"></span></span><span class="base"><span class="strut" style="height:0.7278em;vertical-align:-0.0833em;"></span><span class="mord">−</span><span class="mord">1</span></span></span></span></span>) otherwise.
diff --git a/main/generated/torch.nn.CosineSimilarity.html b/main/generated/torch.nn.CosineSimilarity.html
index adcef8637b4..c3b9221e933 100644
--- a/main/generated/torch.nn.CosineSimilarity.html
+++ b/main/generated/torch.nn.CosineSimilarity.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>CosineSimilarity<a class="headerlink" href="#cosinesimilarity" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.CosineSimilarity">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">CosineSimilarity</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-08</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/distance.html#CosineSimilarity"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/distance.py#L61"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.CosineSimilarity" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">CosineSimilarity</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-08</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/distance.html#CosineSimilarity"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/distance.py#L61"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.CosineSimilarity" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns cosine similarity between <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><msub><mi>x</mi><mn>1</mn></msub></mrow><annotation encoding="application/x-tex">x_1</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.5806em;vertical-align:-0.15em;"></span><span class="mord"><span class="mord mathnormal">x</span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.3011em;"><span style="top:-2.55em;margin-left:0em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight">1</span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.15em;"><span></span></span></span></span></span></span></span></span></span></span> and <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><msub><mi>x</mi><mn>2</mn></msub></mrow><annotation encoding="application/x-tex">x_2</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.5806em;vertical-align:-0.15em;"></span><span class="mord"><span class="mord mathnormal">x</span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.3011em;"><span style="top:-2.55em;margin-left:0em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight">2</span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.15em;"><span></span></span></span></span></span></span></span></span></span></span>, computed along <cite>dim</cite>.</p>
 <div class="math">
 <span class="katex-display"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML" display="block"><semantics><mrow><mtext>similarity</mtext><mo>=</mo><mfrac><mrow><msub><mi>x</mi><mn>1</mn></msub><mo>⋅</mo><msub><mi>x</mi><mn>2</mn></msub></mrow><mrow><mi>max</mi><mo>⁡</mo><mo stretchy="false">(</mo><mi mathvariant="normal">∥</mi><msub><mi>x</mi><mn>1</mn></msub><msub><mi mathvariant="normal">∥</mi><mn>2</mn></msub><mo>⋅</mo><mi mathvariant="normal">∥</mi><msub><mi>x</mi><mn>2</mn></msub><msub><mi mathvariant="normal">∥</mi><mn>2</mn></msub><mo separator="true">,</mo><mi>ϵ</mi><mo stretchy="false">)</mo></mrow></mfrac><mi mathvariant="normal">.</mi></mrow><annotation encoding="application/x-tex">\text{similarity} = \dfrac{x_1 \cdot x_2}{\max(\Vert x_1 \Vert _2 \cdot \Vert x_2 \Vert _2, \epsilon)}.
diff --git a/main/generated/torch.nn.CrossEntropyLoss.html b/main/generated/torch.nn.CrossEntropyLoss.html
index 58d3137f6ee..aec118df79c 100644
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+++ b/main/generated/torch.nn.CrossEntropyLoss.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
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@@ -612,7 +612,7 @@
 <h1>CrossEntropyLoss<a class="headerlink" href="#crossentropyloss" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.CrossEntropyLoss">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">CrossEntropyLoss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">size_average</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ignore_index</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-100</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduce</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mean'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">label_smoothing</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/loss.html#CrossEntropyLoss"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/loss.py#L1146"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.CrossEntropyLoss" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">CrossEntropyLoss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">size_average</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ignore_index</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-100</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduce</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mean'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">label_smoothing</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/loss.html#CrossEntropyLoss"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/loss.py#L1146"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.CrossEntropyLoss" title="Permalink to this definition">¶</a></dt>
 <dd><p>This criterion computes the cross entropy loss between input logits
 and target.</p>
 <p>It is useful when training a classification problem with <cite>C</cite> classes.
diff --git a/main/generated/torch.nn.DataParallel.html b/main/generated/torch.nn.DataParallel.html
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           <div class="pytorch-left-menu-search">
             
     <div class="version">
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@@ -612,7 +612,7 @@
 <h1>DataParallel<a class="headerlink" href="#dataparallel" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.DataParallel">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">DataParallel</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device_ids</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/parallel/data_parallel.html#DataParallel"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/parallel/data_parallel.py#L53"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.DataParallel" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">DataParallel</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device_ids</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/parallel/data_parallel.html#DataParallel"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/parallel/data_parallel.py#L53"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.DataParallel" title="Permalink to this definition">¶</a></dt>
 <dd><p>Implements data parallelism at the module level.</p>
 <p>This container parallelizes the application of the given <code class="xref py py-attr docutils literal notranslate"><span class="pre">module</span></code> by
 splitting the input across the specified devices by chunking in the batch
diff --git a/main/generated/torch.nn.Dropout.html b/main/generated/torch.nn.Dropout.html
index b3d5645e5d5..c32c3c49b4d 100644
--- a/main/generated/torch.nn.Dropout.html
+++ b/main/generated/torch.nn.Dropout.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
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     </div>
     <div id="searchBox">
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@@ -612,7 +612,7 @@
 <h1>Dropout<a class="headerlink" href="#dropout" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.Dropout">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">Dropout</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">p</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/dropout.html#Dropout"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/dropout.py#L35"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Dropout" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">Dropout</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">p</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/dropout.html#Dropout"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/dropout.py#L35"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Dropout" title="Permalink to this definition">¶</a></dt>
 <dd><p>During training, randomly zeroes some of the elements of the input tensor with probability <code class="xref py py-attr docutils literal notranslate"><span class="pre">p</span></code>.</p>
 <p>The zeroed elements are chosen independently for each forward call and are sampled from a Bernoulli distribution.</p>
 <p>Each channel will be zeroed out independently on every forward call.</p>
diff --git a/main/generated/torch.nn.Dropout1d.html b/main/generated/torch.nn.Dropout1d.html
index 753554ae165..abd278d13f4 100644
--- a/main/generated/torch.nn.Dropout1d.html
+++ b/main/generated/torch.nn.Dropout1d.html
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           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
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@@ -612,7 +612,7 @@
 <h1>Dropout1d<a class="headerlink" href="#dropout1d" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.Dropout1d">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">Dropout1d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">p</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/dropout.html#Dropout1d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/dropout.py#L73"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Dropout1d" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">Dropout1d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">p</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/dropout.html#Dropout1d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/dropout.py#L73"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Dropout1d" title="Permalink to this definition">¶</a></dt>
 <dd><p>Randomly zero out entire channels.</p>
 <p>A channel is a 1D feature map,
 e.g., the <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>j</mi></mrow><annotation encoding="application/x-tex">j</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.854em;vertical-align:-0.1944em;"></span><span class="mord mathnormal" style="margin-right:0.05724em;">j</span></span></span></span></span>-th channel of the <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>i</mi></mrow><annotation encoding="application/x-tex">i</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.6595em;"></span><span class="mord mathnormal">i</span></span></span></span></span>-th sample in the
diff --git a/main/generated/torch.nn.Dropout2d.html b/main/generated/torch.nn.Dropout2d.html
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           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
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@@ -612,7 +612,7 @@
 <h1>Dropout2d<a class="headerlink" href="#dropout2d" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.Dropout2d">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">Dropout2d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">p</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/dropout.html#Dropout2d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/dropout.py#L118"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Dropout2d" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">Dropout2d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">p</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/dropout.html#Dropout2d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/dropout.py#L118"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Dropout2d" title="Permalink to this definition">¶</a></dt>
 <dd><p>Randomly zero out entire channels.</p>
 <p>A channel is a 2D feature map,
 e.g., the <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>j</mi></mrow><annotation encoding="application/x-tex">j</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.854em;vertical-align:-0.1944em;"></span><span class="mord mathnormal" style="margin-right:0.05724em;">j</span></span></span></span></span>-th channel of the <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>i</mi></mrow><annotation encoding="application/x-tex">i</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.6595em;"></span><span class="mord mathnormal">i</span></span></span></span></span>-th sample in the
diff --git a/main/generated/torch.nn.Dropout3d.html b/main/generated/torch.nn.Dropout3d.html
index c04b22fc3b0..75cff6d9e15 100644
--- a/main/generated/torch.nn.Dropout3d.html
+++ b/main/generated/torch.nn.Dropout3d.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>Dropout3d<a class="headerlink" href="#dropout3d" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.Dropout3d">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">Dropout3d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">p</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/dropout.html#Dropout3d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/dropout.py#L170"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Dropout3d" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">Dropout3d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">p</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/dropout.html#Dropout3d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/dropout.py#L170"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Dropout3d" title="Permalink to this definition">¶</a></dt>
 <dd><p>Randomly zero out entire channels.</p>
 <p>A channel is a 3D feature map,
 e.g., the <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>j</mi></mrow><annotation encoding="application/x-tex">j</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.854em;vertical-align:-0.1944em;"></span><span class="mord mathnormal" style="margin-right:0.05724em;">j</span></span></span></span></span>-th channel of the <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>i</mi></mrow><annotation encoding="application/x-tex">i</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.6595em;"></span><span class="mord mathnormal">i</span></span></span></span></span>-th sample in the
diff --git a/main/generated/torch.nn.ELU.html b/main/generated/torch.nn.ELU.html
index 4bdf97271bc..e17cd328688 100644
--- a/main/generated/torch.nn.ELU.html
+++ b/main/generated/torch.nn.ELU.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>ELU<a class="headerlink" href="#elu" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.ELU">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">ELU</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/activation.html#ELU"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/activation.py#L520"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.ELU" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">ELU</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/activation.html#ELU"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/activation.py#L520"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.ELU" title="Permalink to this definition">¶</a></dt>
 <dd><p>Applies the Exponential Linear Unit (ELU) function, element-wise.</p>
 <p>Method described in the paper: <a class="reference external" href="https://arxiv.org/abs/1511.07289">Fast and Accurate Deep Network Learning by Exponential Linear
 Units (ELUs)</a>.</p>
diff --git a/main/generated/torch.nn.Embedding.html b/main/generated/torch.nn.Embedding.html
index da6c3dd9808..4ee5b75cc7f 100644
--- a/main/generated/torch.nn.Embedding.html
+++ b/main/generated/torch.nn.Embedding.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>Embedding<a class="headerlink" href="#embedding" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.Embedding">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">Embedding</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">num_embeddings</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">embedding_dim</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding_idx</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_norm</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">norm_type</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale_grad_by_freq</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sparse</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_freeze</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/sparse.html#Embedding"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/sparse.py#L15"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Embedding" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">Embedding</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">num_embeddings</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">embedding_dim</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding_idx</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_norm</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">norm_type</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale_grad_by_freq</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sparse</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_freeze</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/sparse.html#Embedding"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/sparse.py#L15"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Embedding" title="Permalink to this definition">¶</a></dt>
 <dd><p>A simple lookup table that stores embeddings of a fixed dictionary and size.</p>
 <p>This module is often used to store word embeddings and retrieve them using indices.
 The input to the module is a list of indices, and the output is the corresponding
@@ -718,7 +718,7 @@ <h1>Embedding<a class="headerlink" href="#embedding" title="Permalink to this he
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.Embedding.from_pretrained">
-<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_pretrained</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">embeddings</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">freeze</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding_idx</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_norm</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">norm_type</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale_grad_by_freq</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sparse</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/sparse.html#Embedding.from_pretrained"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/sparse.py#L214"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Embedding.from_pretrained" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_pretrained</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">embeddings</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">freeze</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding_idx</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_norm</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">norm_type</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale_grad_by_freq</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sparse</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/sparse.html#Embedding.from_pretrained"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/sparse.py#L214"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Embedding.from_pretrained" title="Permalink to this definition">¶</a></dt>
 <dd><p>Create Embedding instance from given 2-dimensional FloatTensor.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.nn.EmbeddingBag.html b/main/generated/torch.nn.EmbeddingBag.html
index c991cea3913..333c3bfd956 100644
--- a/main/generated/torch.nn.EmbeddingBag.html
+++ b/main/generated/torch.nn.EmbeddingBag.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>EmbeddingBag<a class="headerlink" href="#embeddingbag" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.EmbeddingBag">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">EmbeddingBag</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">num_embeddings</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">embedding_dim</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_norm</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">norm_type</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale_grad_by_freq</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mean'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sparse</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">include_last_offset</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding_idx</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/sparse.html#EmbeddingBag"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/sparse.py#L269"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.EmbeddingBag" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">EmbeddingBag</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">num_embeddings</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">embedding_dim</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_norm</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">norm_type</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale_grad_by_freq</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mean'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sparse</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">include_last_offset</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding_idx</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/sparse.html#EmbeddingBag"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/sparse.py#L269"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.EmbeddingBag" title="Permalink to this definition">¶</a></dt>
 <dd><p>Compute sums or means of ‘bags’ of embeddings, without instantiating the intermediate embeddings.</p>
 <p>For bags of constant length, no <code class="xref py py-attr docutils literal notranslate"><span class="pre">per_sample_weights</span></code>, no indices equal to <code class="xref py py-attr docutils literal notranslate"><span class="pre">padding_idx</span></code>,
 and with 2D inputs, this class</p>
@@ -693,7 +693,7 @@ <h1>EmbeddingBag<a class="headerlink" href="#embeddingbag" title="Permalink to t
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.EmbeddingBag.forward">
-<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">offsets</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">per_sample_weights</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/sparse.html#EmbeddingBag.forward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/sparse.py#L427"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.EmbeddingBag.forward" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">offsets</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">per_sample_weights</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/sparse.html#EmbeddingBag.forward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/sparse.py#L427"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.EmbeddingBag.forward" title="Permalink to this definition">¶</a></dt>
 <dd><p>Forward pass of EmbeddingBag.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -733,7 +733,7 @@ <h1>EmbeddingBag<a class="headerlink" href="#embeddingbag" title="Permalink to t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.EmbeddingBag.from_pretrained">
-<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_pretrained</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">embeddings</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">freeze</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_norm</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">norm_type</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale_grad_by_freq</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mean'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sparse</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">include_last_offset</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding_idx</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/sparse.html#EmbeddingBag.from_pretrained"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/sparse.py#L490"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.EmbeddingBag.from_pretrained" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_pretrained</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">embeddings</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">freeze</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_norm</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">norm_type</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale_grad_by_freq</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mean'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sparse</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">include_last_offset</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding_idx</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/sparse.html#EmbeddingBag.from_pretrained"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/sparse.py#L490"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.EmbeddingBag.from_pretrained" title="Permalink to this definition">¶</a></dt>
 <dd><p>Create EmbeddingBag instance from given 2-dimensional FloatTensor.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.nn.FeatureAlphaDropout.html b/main/generated/torch.nn.FeatureAlphaDropout.html
index 15fe422ba56..01fd76e3d53 100644
--- a/main/generated/torch.nn.FeatureAlphaDropout.html
+++ b/main/generated/torch.nn.FeatureAlphaDropout.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>FeatureAlphaDropout<a class="headerlink" href="#featurealphadropout" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.FeatureAlphaDropout">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">FeatureAlphaDropout</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">p</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/dropout.html#FeatureAlphaDropout"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/dropout.py#L257"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.FeatureAlphaDropout" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">FeatureAlphaDropout</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">p</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/dropout.html#FeatureAlphaDropout"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/dropout.py#L257"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.FeatureAlphaDropout" title="Permalink to this definition">¶</a></dt>
 <dd><p>Randomly masks out entire channels.</p>
 <p>A channel is a feature map,
 e.g. the <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>j</mi></mrow><annotation encoding="application/x-tex">j</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.854em;vertical-align:-0.1944em;"></span><span class="mord mathnormal" style="margin-right:0.05724em;">j</span></span></span></span></span>-th channel of the <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>i</mi></mrow><annotation encoding="application/x-tex">i</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.6595em;"></span><span class="mord mathnormal">i</span></span></span></span></span>-th sample in the batch input
diff --git a/main/generated/torch.nn.Flatten.html b/main/generated/torch.nn.Flatten.html
index 0de133972ee..c1f3f7e0508 100644
--- a/main/generated/torch.nn.Flatten.html
+++ b/main/generated/torch.nn.Flatten.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>Flatten<a class="headerlink" href="#flatten" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.Flatten">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">Flatten</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">start_dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">end_dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-1</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/flatten.html#Flatten"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/flatten.py#L13"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Flatten" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">Flatten</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">start_dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">end_dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-1</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/flatten.html#Flatten"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/flatten.py#L13"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Flatten" title="Permalink to this definition">¶</a></dt>
 <dd><p>Flattens a contiguous range of dims into a tensor.</p>
 <p>For use with <code class="xref py py-class docutils literal notranslate"><span class="pre">Sequential</span></code>, see <a class="reference internal" href="torch.flatten.html#torch.flatten" title="torch.flatten"><code class="xref py py-meth docutils literal notranslate"><span class="pre">torch.flatten()</span></code></a> for details.</p>
 <dl class="simple">
diff --git a/main/generated/torch.nn.Fold.html b/main/generated/torch.nn.Fold.html
index cbe3827f73f..793675542c8 100644
--- a/main/generated/torch.nn.Fold.html
+++ b/main/generated/torch.nn.Fold.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>Fold<a class="headerlink" href="#fold" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.Fold">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">Fold</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">output_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dilation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/fold.html#Fold"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/fold.py#L11"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Fold" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">Fold</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">output_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dilation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/fold.html#Fold"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/fold.py#L11"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Fold" title="Permalink to this definition">¶</a></dt>
 <dd><p>Combines an array of sliding local blocks into a large containing tensor.</p>
 <p>Consider a batched <code class="xref py py-attr docutils literal notranslate"><span class="pre">input</span></code> tensor containing sliding local blocks,
 e.g., patches of images, of shape <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mo stretchy="false">(</mo><mi>N</mi><mo separator="true">,</mo><mi>C</mi><mo>×</mo><mo>∏</mo><mo stretchy="false">(</mo><mtext>kernel_size</mtext><mo stretchy="false">)</mo><mo separator="true">,</mo><mi>L</mi><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">(N, C \times  \prod(\text{kernel\_size}), L)</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mopen">(</span><span class="mord mathnormal" style="margin-right:0.10903em;">N</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord mathnormal" style="margin-right:0.07153em;">C</span><span class="mspace" style="margin-right:0.2222em;"></span><span class="mbin">×</span><span class="mspace" style="margin-right:0.2222em;"></span></span><span class="base"><span class="strut" style="height:1.06em;vertical-align:-0.31em;"></span><span class="mop op-symbol small-op" style="position:relative;top:0em;">∏</span><span class="mopen">(</span><span class="mord text"><span class="mord">kernel_size</span></span><span class="mclose">)</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord mathnormal">L</span><span class="mclose">)</span></span></span></span></span>,
diff --git a/main/generated/torch.nn.FractionalMaxPool2d.html b/main/generated/torch.nn.FractionalMaxPool2d.html
index 3ea1ec4d4c1..4100a8010e7 100644
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+++ b/main/generated/torch.nn.FractionalMaxPool2d.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
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@@ -612,7 +612,7 @@
 <h1>FractionalMaxPool2d<a class="headerlink" href="#fractionalmaxpool2d" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.FractionalMaxPool2d">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">FractionalMaxPool2d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_ratio</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_indices</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_random_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/pooling.html#FractionalMaxPool2d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/pooling.py#L886"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.FractionalMaxPool2d" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">FractionalMaxPool2d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_ratio</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_indices</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_random_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/pooling.html#FractionalMaxPool2d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/pooling.py#L886"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.FractionalMaxPool2d" title="Permalink to this definition">¶</a></dt>
 <dd><p>Applies a 2D fractional max pooling over an input signal composed of several input planes.</p>
 <p>Fractional MaxPooling is described in detail in the paper <a class="reference external" href="https://arxiv.org/abs/1412.6071">Fractional MaxPooling</a> by Ben Graham</p>
 <p>The max-pooling operation is applied in <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>k</mi><mi>H</mi><mo>×</mo><mi>k</mi><mi>W</mi></mrow><annotation encoding="application/x-tex">kH \times kW</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.7778em;vertical-align:-0.0833em;"></span><span class="mord mathnormal" style="margin-right:0.03148em;">k</span><span class="mord mathnormal" style="margin-right:0.08125em;">H</span><span class="mspace" style="margin-right:0.2222em;"></span><span class="mbin">×</span><span class="mspace" style="margin-right:0.2222em;"></span></span><span class="base"><span class="strut" style="height:0.6944em;"></span><span class="mord mathnormal" style="margin-right:0.13889em;">kW</span></span></span></span></span> regions by a stochastic
diff --git a/main/generated/torch.nn.FractionalMaxPool3d.html b/main/generated/torch.nn.FractionalMaxPool3d.html
index 0c770427e4b..b68eca75dfb 100644
--- a/main/generated/torch.nn.FractionalMaxPool3d.html
+++ b/main/generated/torch.nn.FractionalMaxPool3d.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>FractionalMaxPool3d<a class="headerlink" href="#fractionalmaxpool3d" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.FractionalMaxPool3d">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">FractionalMaxPool3d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_ratio</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_indices</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_random_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/pooling.html#FractionalMaxPool3d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/pooling.py#L975"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.FractionalMaxPool3d" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">FractionalMaxPool3d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_ratio</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_indices</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_random_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/pooling.html#FractionalMaxPool3d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/pooling.py#L975"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.FractionalMaxPool3d" title="Permalink to this definition">¶</a></dt>
 <dd><p>Applies a 3D fractional max pooling over an input signal composed of several input planes.</p>
 <p>Fractional MaxPooling is described in detail in the paper <a class="reference external" href="https://arxiv.org/abs/1412.6071">Fractional MaxPooling</a> by Ben Graham</p>
 <p>The max-pooling operation is applied in <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>k</mi><mi>T</mi><mo>×</mo><mi>k</mi><mi>H</mi><mo>×</mo><mi>k</mi><mi>W</mi></mrow><annotation encoding="application/x-tex">kT \times kH \times kW</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.7778em;vertical-align:-0.0833em;"></span><span class="mord mathnormal" style="margin-right:0.03148em;">k</span><span class="mord mathnormal" style="margin-right:0.13889em;">T</span><span class="mspace" style="margin-right:0.2222em;"></span><span class="mbin">×</span><span class="mspace" style="margin-right:0.2222em;"></span></span><span class="base"><span class="strut" style="height:0.7778em;vertical-align:-0.0833em;"></span><span class="mord mathnormal" style="margin-right:0.03148em;">k</span><span class="mord mathnormal" style="margin-right:0.08125em;">H</span><span class="mspace" style="margin-right:0.2222em;"></span><span class="mbin">×</span><span class="mspace" style="margin-right:0.2222em;"></span></span><span class="base"><span class="strut" style="height:0.6944em;"></span><span class="mord mathnormal" style="margin-right:0.13889em;">kW</span></span></span></span></span> regions by a stochastic
diff --git a/main/generated/torch.nn.GELU.html b/main/generated/torch.nn.GELU.html
index 2aecd321d9b..44fa0bd1598 100644
--- a/main/generated/torch.nn.GELU.html
+++ b/main/generated/torch.nn.GELU.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>GELU<a class="headerlink" href="#gelu" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.GELU">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">GELU</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">approximate</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'none'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/activation.html#GELU"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/activation.py#L698"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.GELU" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">GELU</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">approximate</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'none'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/activation.html#GELU"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/activation.py#L698"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.GELU" title="Permalink to this definition">¶</a></dt>
 <dd><p>Applies the Gaussian Error Linear Units function.</p>
 <div class="math">
 <span class="katex-display"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML" display="block"><semantics><mrow><mtext>GELU</mtext><mo stretchy="false">(</mo><mi>x</mi><mo stretchy="false">)</mo><mo>=</mo><mi>x</mi><mo>∗</mo><mi mathvariant="normal">Φ</mi><mo stretchy="false">(</mo><mi>x</mi><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">\text{GELU}(x) = x * \Phi(x)
diff --git a/main/generated/torch.nn.GLU.html b/main/generated/torch.nn.GLU.html
index 776bfc635bc..81a348147bb 100644
--- a/main/generated/torch.nn.GLU.html
+++ b/main/generated/torch.nn.GLU.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>GLU<a class="headerlink" href="#glu" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.GLU">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">GLU</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-1</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/activation.html#GLU"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/activation.py#L663"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.GLU" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">GLU</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-1</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/activation.html#GLU"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/activation.py#L663"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.GLU" title="Permalink to this definition">¶</a></dt>
 <dd><p>Applies the gated linear unit function.</p>
 <p><span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mrow><mi>G</mi><mi>L</mi><mi>U</mi></mrow><mo stretchy="false">(</mo><mi>a</mi><mo separator="true">,</mo><mi>b</mi><mo stretchy="false">)</mo><mo>=</mo><mi>a</mi><mo>⊗</mo><mi>σ</mi><mo stretchy="false">(</mo><mi>b</mi><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">{GLU}(a, b)= a \otimes \sigma(b)</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mord"><span class="mord mathnormal">G</span><span class="mord mathnormal" style="margin-right:0.10903em;">LU</span></span><span class="mopen">(</span><span class="mord mathnormal">a</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord mathnormal">b</span><span class="mclose">)</span><span class="mspace" style="margin-right:0.2778em;"></span><span class="mrel">=</span><span class="mspace" style="margin-right:0.2778em;"></span></span><span class="base"><span class="strut" style="height:0.6667em;vertical-align:-0.0833em;"></span><span class="mord mathnormal">a</span><span class="mspace" style="margin-right:0.2222em;"></span><span class="mbin">⊗</span><span class="mspace" style="margin-right:0.2222em;"></span></span><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mord mathnormal" style="margin-right:0.03588em;">σ</span><span class="mopen">(</span><span class="mord mathnormal">b</span><span class="mclose">)</span></span></span></span></span> where <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>a</mi></mrow><annotation encoding="application/x-tex">a</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.4306em;"></span><span class="mord mathnormal">a</span></span></span></span></span> is the first half
 of the input matrices and <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>b</mi></mrow><annotation encoding="application/x-tex">b</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.6944em;"></span><span class="mord mathnormal">b</span></span></span></span></span> is the second half.</p>
diff --git a/main/generated/torch.nn.GRU.html b/main/generated/torch.nn.GRU.html
index 91bea1f8f20..94a71f77720 100644
--- a/main/generated/torch.nn.GRU.html
+++ b/main/generated/torch.nn.GRU.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
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@@ -612,7 +612,7 @@
 <h1>GRU<a class="headerlink" href="#gru" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.GRU">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">GRU</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">hidden_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_layers</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">batch_first</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dropout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bidirectional</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/rnn.html#GRU"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/rnn.py#L1162"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.GRU" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">GRU</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">hidden_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_layers</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">batch_first</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dropout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bidirectional</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/rnn.html#GRU"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/rnn.py#L1162"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.GRU" title="Permalink to this definition">¶</a></dt>
 <dd><p>Apply a multi-layer gated recurrent unit (GRU) RNN to an input sequence.
 For each element in the input sequence, each layer computes the following
 function:</p>
diff --git a/main/generated/torch.nn.GRUCell.html b/main/generated/torch.nn.GRUCell.html
index 81ba4f24c32..3a9c9f9e08c 100644
--- a/main/generated/torch.nn.GRUCell.html
+++ b/main/generated/torch.nn.GRUCell.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>GRUCell<a class="headerlink" href="#grucell" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.GRUCell">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">GRUCell</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">hidden_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/rnn.html#GRUCell"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/rnn.py#L1720"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.GRUCell" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">GRUCell</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">hidden_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/rnn.html#GRUCell"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/rnn.py#L1720"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.GRUCell" title="Permalink to this definition">¶</a></dt>
 <dd><p>A gated recurrent unit (GRU) cell.</p>
 <div class="math">
 <span class="katex-display"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML" display="block"><semantics><mtable rowspacing="0.16em" columnalign="left left" columnspacing="1em"><mtr><mtd><mstyle scriptlevel="0" displaystyle="false"><mrow><mi>r</mi><mo>=</mo><mi>σ</mi><mo stretchy="false">(</mo><msub><mi>W</mi><mrow><mi>i</mi><mi>r</mi></mrow></msub><mi>x</mi><mo>+</mo><msub><mi>b</mi><mrow><mi>i</mi><mi>r</mi></mrow></msub><mo>+</mo><msub><mi>W</mi><mrow><mi>h</mi><mi>r</mi></mrow></msub><mi>h</mi><mo>+</mo><msub><mi>b</mi><mrow><mi>h</mi><mi>r</mi></mrow></msub><mo stretchy="false">)</mo></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="false"><mrow><mi>z</mi><mo>=</mo><mi>σ</mi><mo stretchy="false">(</mo><msub><mi>W</mi><mrow><mi>i</mi><mi>z</mi></mrow></msub><mi>x</mi><mo>+</mo><msub><mi>b</mi><mrow><mi>i</mi><mi>z</mi></mrow></msub><mo>+</mo><msub><mi>W</mi><mrow><mi>h</mi><mi>z</mi></mrow></msub><mi>h</mi><mo>+</mo><msub><mi>b</mi><mrow><mi>h</mi><mi>z</mi></mrow></msub><mo stretchy="false">)</mo></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="false"><mrow><mi>n</mi><mo>=</mo><mi>tanh</mi><mo>⁡</mo><mo stretchy="false">(</mo><msub><mi>W</mi><mrow><mi>i</mi><mi>n</mi></mrow></msub><mi>x</mi><mo>+</mo><msub><mi>b</mi><mrow><mi>i</mi><mi>n</mi></mrow></msub><mo>+</mo><mi>r</mi><mo>⊙</mo><mo stretchy="false">(</mo><msub><mi>W</mi><mrow><mi>h</mi><mi>n</mi></mrow></msub><mi>h</mi><mo>+</mo><msub><mi>b</mi><mrow><mi>h</mi><mi>n</mi></mrow></msub><mo stretchy="false">)</mo><mo stretchy="false">)</mo></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="false"><mrow><msup><mi>h</mi><mo mathvariant="normal" lspace="0em" rspace="0em">′</mo></msup><mo>=</mo><mo stretchy="false">(</mo><mn>1</mn><mo>−</mo><mi>z</mi><mo stretchy="false">)</mo><mo>⊙</mo><mi>n</mi><mo>+</mo><mi>z</mi><mo>⊙</mo><mi>h</mi></mrow></mstyle></mtd></mtr></mtable><annotation encoding="application/x-tex">\begin{array}{ll}
diff --git a/main/generated/torch.nn.GaussianNLLLoss.html b/main/generated/torch.nn.GaussianNLLLoss.html
index d6b462e0716..b6709c44f78 100644
--- a/main/generated/torch.nn.GaussianNLLLoss.html
+++ b/main/generated/torch.nn.GaussianNLLLoss.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
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@@ -612,7 +612,7 @@
 <h1>GaussianNLLLoss<a class="headerlink" href="#gaussiannllloss" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.GaussianNLLLoss">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">GaussianNLLLoss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">full</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-06</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mean'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/loss.html#GaussianNLLLoss"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/loss.py#L364"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.GaussianNLLLoss" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">GaussianNLLLoss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">full</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-06</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mean'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/loss.html#GaussianNLLLoss"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/loss.py#L364"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.GaussianNLLLoss" title="Permalink to this definition">¶</a></dt>
 <dd><p>Gaussian negative log likelihood loss.</p>
 <p>The targets are treated as samples from Gaussian distributions with
 expectations and variances predicted by the neural network. For a
diff --git a/main/generated/torch.nn.GroupNorm.html b/main/generated/torch.nn.GroupNorm.html
index 994960994e8..ef8d10b319c 100644
--- a/main/generated/torch.nn.GroupNorm.html
+++ b/main/generated/torch.nn.GroupNorm.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>GroupNorm<a class="headerlink" href="#groupnorm" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.GroupNorm">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">GroupNorm</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">num_groups</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-05</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">affine</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/normalization.html#GroupNorm"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/normalization.py#L228"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.GroupNorm" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">GroupNorm</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">num_groups</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-05</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">affine</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/normalization.html#GroupNorm"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/normalization.py#L228"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.GroupNorm" title="Permalink to this definition">¶</a></dt>
 <dd><p>Applies Group Normalization over a mini-batch of inputs.</p>
 <p>This layer implements the operation as described in
 the paper <a class="reference external" href="https://arxiv.org/abs/1803.08494">Group Normalization</a></p>
diff --git a/main/generated/torch.nn.Hardshrink.html b/main/generated/torch.nn.Hardshrink.html
index f6c40b1a92c..83239c2af72 100644
--- a/main/generated/torch.nn.Hardshrink.html
+++ b/main/generated/torch.nn.Hardshrink.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>Hardshrink<a class="headerlink" href="#hardshrink" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.Hardshrink">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">Hardshrink</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">lambd</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.5</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/activation.html#Hardshrink"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/activation.py#L740"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Hardshrink" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">Hardshrink</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">lambd</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.5</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/activation.html#Hardshrink"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/activation.py#L740"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Hardshrink" title="Permalink to this definition">¶</a></dt>
 <dd><p>Applies the Hard Shrinkage (Hardshrink) function element-wise.</p>
 <p>Hardshrink is defined as:</p>
 <div class="math">
diff --git a/main/generated/torch.nn.Hardsigmoid.html b/main/generated/torch.nn.Hardsigmoid.html
index b41982ba0a6..d39905f7230 100644
--- a/main/generated/torch.nn.Hardsigmoid.html
+++ b/main/generated/torch.nn.Hardsigmoid.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>Hardsigmoid<a class="headerlink" href="#hardsigmoid" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.Hardsigmoid">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">Hardsigmoid</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/activation.html#Hardsigmoid"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/activation.py#L330"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Hardsigmoid" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">Hardsigmoid</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/activation.html#Hardsigmoid"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/activation.py#L330"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Hardsigmoid" title="Permalink to this definition">¶</a></dt>
 <dd><p>Applies the Hardsigmoid function element-wise.</p>
 <p>Hardsigmoid is defined as:</p>
 <div class="math">
diff --git a/main/generated/torch.nn.Hardswish.html b/main/generated/torch.nn.Hardswish.html
index ffa93f763c3..fb18c665c85 100644
--- a/main/generated/torch.nn.Hardswish.html
+++ b/main/generated/torch.nn.Hardswish.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>Hardswish<a class="headerlink" href="#hardswish" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.Hardswish">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">Hardswish</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/activation.html#Hardswish"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/activation.py#L478"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Hardswish" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">Hardswish</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/activation.html#Hardswish"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/activation.py#L478"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Hardswish" title="Permalink to this definition">¶</a></dt>
 <dd><p>Applies the Hardswish function, element-wise.</p>
 <p>Method described in the paper: <a class="reference external" href="https://arxiv.org/abs/1905.02244">Searching for MobileNetV3</a>.</p>
 <p>Hardswish is defined as:</p>
diff --git a/main/generated/torch.nn.Hardtanh.html b/main/generated/torch.nn.Hardtanh.html
index da53269646c..246193a3c6b 100644
--- a/main/generated/torch.nn.Hardtanh.html
+++ b/main/generated/torch.nn.Hardtanh.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>Hardtanh<a class="headerlink" href="#hardtanh" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.Hardtanh">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">Hardtanh</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">min_val</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_val</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_value</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_value</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/activation.html#Hardtanh"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/activation.py#L200"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Hardtanh" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">Hardtanh</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">min_val</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_val</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_value</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_value</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/activation.html#Hardtanh"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/activation.py#L200"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Hardtanh" title="Permalink to this definition">¶</a></dt>
 <dd><p>Applies the HardTanh function element-wise.</p>
 <p>HardTanh is defined as:</p>
 <div class="math">
diff --git a/main/generated/torch.nn.HingeEmbeddingLoss.html b/main/generated/torch.nn.HingeEmbeddingLoss.html
index 39c73680b37..4841e32d2cf 100644
--- a/main/generated/torch.nn.HingeEmbeddingLoss.html
+++ b/main/generated/torch.nn.HingeEmbeddingLoss.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>HingeEmbeddingLoss<a class="headerlink" href="#hingeembeddingloss" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.HingeEmbeddingLoss">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">HingeEmbeddingLoss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">margin</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">size_average</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduce</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mean'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/loss.html#HingeEmbeddingLoss"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/loss.py#L830"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.HingeEmbeddingLoss" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">HingeEmbeddingLoss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">margin</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">size_average</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduce</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mean'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/loss.html#HingeEmbeddingLoss"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/loss.py#L830"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.HingeEmbeddingLoss" title="Permalink to this definition">¶</a></dt>
 <dd><p>Measures the loss given an input tensor <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>x</mi></mrow><annotation encoding="application/x-tex">x</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.4306em;"></span><span class="mord mathnormal">x</span></span></span></span></span> and a labels tensor <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>y</mi></mrow><annotation encoding="application/x-tex">y</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.625em;vertical-align:-0.1944em;"></span><span class="mord mathnormal" style="margin-right:0.03588em;">y</span></span></span></span></span>
 (containing 1 or -1).
 This is usually used for measuring whether two inputs are similar or
diff --git a/main/generated/torch.nn.HuberLoss.html b/main/generated/torch.nn.HuberLoss.html
index f6f3adfa0e1..bd44dae509e 100644
--- a/main/generated/torch.nn.HuberLoss.html
+++ b/main/generated/torch.nn.HuberLoss.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>HuberLoss<a class="headerlink" href="#huberloss" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.HuberLoss">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">HuberLoss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">reduction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mean'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">delta</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/loss.html#HuberLoss"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/loss.py#L1045"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.HuberLoss" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">HuberLoss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">reduction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mean'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">delta</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/loss.html#HuberLoss"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/loss.py#L1045"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.HuberLoss" title="Permalink to this definition">¶</a></dt>
 <dd><p>Creates a criterion that uses a squared term if the absolute
 element-wise error falls below delta and a delta-scaled L1 term otherwise.
 This loss combines advantages of both <a class="reference internal" href="torch.nn.L1Loss.html#torch.nn.L1Loss" title="torch.nn.L1Loss"><code class="xref py py-class docutils literal notranslate"><span class="pre">L1Loss</span></code></a> and <a class="reference internal" href="torch.nn.MSELoss.html#torch.nn.MSELoss" title="torch.nn.MSELoss"><code class="xref py py-class docutils literal notranslate"><span class="pre">MSELoss</span></code></a>; the
diff --git a/main/generated/torch.nn.Identity.html b/main/generated/torch.nn.Identity.html
index 2a68a68923a..b8f0f18ec37 100644
--- a/main/generated/torch.nn.Identity.html
+++ b/main/generated/torch.nn.Identity.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>Identity<a class="headerlink" href="#identity" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.Identity">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">Identity</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/linear.html#Identity"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/linear.py#L22"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Identity" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">Identity</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/linear.html#Identity"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/linear.py#L22"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Identity" title="Permalink to this definition">¶</a></dt>
 <dd><p>A placeholder identity operator that is argument-insensitive.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.nn.InstanceNorm1d.html b/main/generated/torch.nn.InstanceNorm1d.html
index decf168103d..c95e779d5d8 100644
--- a/main/generated/torch.nn.InstanceNorm1d.html
+++ b/main/generated/torch.nn.InstanceNorm1d.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>InstanceNorm1d<a class="headerlink" href="#instancenorm1d" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.InstanceNorm1d">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">InstanceNorm1d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">num_features</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-05</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">momentum</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">affine</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">track_running_stats</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/instancenorm.html#InstanceNorm1d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/instancenorm.py#L127"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.InstanceNorm1d" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">InstanceNorm1d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">num_features</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-05</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">momentum</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">affine</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">track_running_stats</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/instancenorm.html#InstanceNorm1d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/instancenorm.py#L127"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.InstanceNorm1d" title="Permalink to this definition">¶</a></dt>
 <dd><p>Applies Instance Normalization.</p>
 <p>This operation applies Instance Normalization
 over a 2D (unbatched) or 3D (batched) input as described in the paper
diff --git a/main/generated/torch.nn.InstanceNorm2d.html b/main/generated/torch.nn.InstanceNorm2d.html
index 3ec91efb1d9..9003f40450b 100644
--- a/main/generated/torch.nn.InstanceNorm2d.html
+++ b/main/generated/torch.nn.InstanceNorm2d.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>InstanceNorm2d<a class="headerlink" href="#instancenorm2d" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.InstanceNorm2d">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">InstanceNorm2d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">num_features</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-05</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">momentum</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">affine</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">track_running_stats</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/instancenorm.html#InstanceNorm2d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/instancenorm.py#L241"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.InstanceNorm2d" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">InstanceNorm2d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">num_features</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-05</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">momentum</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">affine</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">track_running_stats</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/instancenorm.html#InstanceNorm2d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/instancenorm.py#L241"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.InstanceNorm2d" title="Permalink to this definition">¶</a></dt>
 <dd><p>Applies Instance Normalization.</p>
 <p>This operation applies Instance Normalization
 over a 4D input (a mini-batch of 2D inputs
diff --git a/main/generated/torch.nn.InstanceNorm3d.html b/main/generated/torch.nn.InstanceNorm3d.html
index 22c260155f9..18832b5a39f 100644
--- a/main/generated/torch.nn.InstanceNorm3d.html
+++ b/main/generated/torch.nn.InstanceNorm3d.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>InstanceNorm3d<a class="headerlink" href="#instancenorm3d" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.InstanceNorm3d">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">InstanceNorm3d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">num_features</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-05</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">momentum</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">affine</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">track_running_stats</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/instancenorm.html#InstanceNorm3d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/instancenorm.py#L358"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.InstanceNorm3d" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">InstanceNorm3d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">num_features</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-05</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">momentum</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">affine</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">track_running_stats</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/instancenorm.html#InstanceNorm3d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/instancenorm.py#L358"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.InstanceNorm3d" title="Permalink to this definition">¶</a></dt>
 <dd><p>Applies Instance Normalization.</p>
 <p>This operation applies Instance Normalization
 over a 5D input (a mini-batch of 3D inputs with additional channel dimension) as described in the paper
diff --git a/main/generated/torch.nn.KLDivLoss.html b/main/generated/torch.nn.KLDivLoss.html
index 73367bd9898..851c573bae3 100644
--- a/main/generated/torch.nn.KLDivLoss.html
+++ b/main/generated/torch.nn.KLDivLoss.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>KLDivLoss<a class="headerlink" href="#kldivloss" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.KLDivLoss">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">KLDivLoss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">size_average</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduce</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mean'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">log_target</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/loss.html#KLDivLoss"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/loss.py#L449"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.KLDivLoss" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">KLDivLoss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">size_average</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduce</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mean'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">log_target</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/loss.html#KLDivLoss"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/loss.py#L449"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.KLDivLoss" title="Permalink to this definition">¶</a></dt>
 <dd><p>The Kullback-Leibler divergence loss.</p>
 <p>For tensors of the same shape <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><msub><mi>y</mi><mtext>pred</mtext></msub><mo separator="true">,</mo><mtext> </mtext><msub><mi>y</mi><mtext>true</mtext></msub></mrow><annotation encoding="application/x-tex">y_{\text{pred}},\ y_{\text{true}}</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.7167em;vertical-align:-0.2861em;"></span><span class="mord"><span class="mord mathnormal" style="margin-right:0.03588em;">y</span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.3361em;"><span style="top:-2.55em;margin-left:-0.0359em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mord text mtight"><span class="mord mtight">pred</span></span></span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.2861em;"><span></span></span></span></span></span></span><span class="mpunct">,</span><span class="mspace"> </span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord"><span class="mord mathnormal" style="margin-right:0.03588em;">y</span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.2806em;"><span style="top:-2.55em;margin-left:-0.0359em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mord text mtight"><span class="mord mtight">true</span></span></span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.15em;"><span></span></span></span></span></span></span></span></span></span></span>,
 where <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><msub><mi>y</mi><mtext>pred</mtext></msub></mrow><annotation encoding="application/x-tex">y_{\text{pred}}</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.7167em;vertical-align:-0.2861em;"></span><span class="mord"><span class="mord mathnormal" style="margin-right:0.03588em;">y</span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.3361em;"><span style="top:-2.55em;margin-left:-0.0359em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mord text mtight"><span class="mord mtight">pred</span></span></span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.2861em;"><span></span></span></span></span></span></span></span></span></span></span> is the <code class="xref py py-attr docutils literal notranslate"><span class="pre">input</span></code> and <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><msub><mi>y</mi><mtext>true</mtext></msub></mrow><annotation encoding="application/x-tex">y_{\text{true}}</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.625em;vertical-align:-0.1944em;"></span><span class="mord"><span class="mord mathnormal" style="margin-right:0.03588em;">y</span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.2806em;"><span style="top:-2.55em;margin-left:-0.0359em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mord text mtight"><span class="mord mtight">true</span></span></span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.15em;"><span></span></span></span></span></span></span></span></span></span></span> is the
diff --git a/main/generated/torch.nn.L1Loss.html b/main/generated/torch.nn.L1Loss.html
index 561b33c8a31..c564a4a7e47 100644
--- a/main/generated/torch.nn.L1Loss.html
+++ b/main/generated/torch.nn.L1Loss.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>L1Loss<a class="headerlink" href="#l1loss" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.L1Loss">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">L1Loss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">size_average</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduce</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mean'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/loss.html#L1Loss"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/loss.py#L62"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.L1Loss" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">L1Loss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">size_average</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduce</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mean'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/loss.html#L1Loss"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/loss.py#L62"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.L1Loss" title="Permalink to this definition">¶</a></dt>
 <dd><p>Creates a criterion that measures the mean absolute error (MAE) between each element in
 the input <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>x</mi></mrow><annotation encoding="application/x-tex">x</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.4306em;"></span><span class="mord mathnormal">x</span></span></span></span></span> and target <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>y</mi></mrow><annotation encoding="application/x-tex">y</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.625em;vertical-align:-0.1944em;"></span><span class="mord mathnormal" style="margin-right:0.03588em;">y</span></span></span></span></span>.</p>
 <p>The unreduced (i.e. with <code class="xref py py-attr docutils literal notranslate"><span class="pre">reduction</span></code> set to <code class="docutils literal notranslate"><span class="pre">'none'</span></code>) loss can be described as:</p>
diff --git a/main/generated/torch.nn.LPPool1d.html b/main/generated/torch.nn.LPPool1d.html
index f7d57d9379b..0a735db8d2a 100644
--- a/main/generated/torch.nn.LPPool1d.html
+++ b/main/generated/torch.nn.LPPool1d.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>LPPool1d<a class="headerlink" href="#lppool1d" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.LPPool1d">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">LPPool1d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">norm_type</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ceil_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/pooling.html#LPPool1d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/pooling.py#L1090"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.LPPool1d" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">LPPool1d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">norm_type</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ceil_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/pooling.html#LPPool1d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/pooling.py#L1090"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.LPPool1d" title="Permalink to this definition">¶</a></dt>
 <dd><p>Applies a 1D power-average pooling over an input signal composed of several input planes.</p>
 <p>On each window, the function computed is:</p>
 <div class="math">
diff --git a/main/generated/torch.nn.LPPool2d.html b/main/generated/torch.nn.LPPool2d.html
index 68f59be504d..4981db155e8 100644
--- a/main/generated/torch.nn.LPPool2d.html
+++ b/main/generated/torch.nn.LPPool2d.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>LPPool2d<a class="headerlink" href="#lppool2d" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.LPPool2d">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">LPPool2d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">norm_type</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ceil_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/pooling.html#LPPool2d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/pooling.py#L1132"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.LPPool2d" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">LPPool2d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">norm_type</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ceil_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/pooling.html#LPPool2d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/pooling.py#L1132"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.LPPool2d" title="Permalink to this definition">¶</a></dt>
 <dd><p>Applies a 2D power-average pooling over an input signal composed of several input planes.</p>
 <p>On each window, the function computed is:</p>
 <div class="math">
diff --git a/main/generated/torch.nn.LPPool3d.html b/main/generated/torch.nn.LPPool3d.html
index 29510b65d54..c6851869124 100644
--- a/main/generated/torch.nn.LPPool3d.html
+++ b/main/generated/torch.nn.LPPool3d.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>LPPool3d<a class="headerlink" href="#lppool3d" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.LPPool3d">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">LPPool3d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">norm_type</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ceil_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/pooling.html#LPPool3d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/pooling.py#L1187"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.LPPool3d" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">LPPool3d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">norm_type</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ceil_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/pooling.html#LPPool3d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/pooling.py#L1187"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.LPPool3d" title="Permalink to this definition">¶</a></dt>
 <dd><p>Applies a 3D power-average pooling over an input signal composed of several input planes.</p>
 <p>On each window, the function computed is:</p>
 <div class="math">
diff --git a/main/generated/torch.nn.LSTM.html b/main/generated/torch.nn.LSTM.html
index 1530fb710b4..543a5e46734 100644
--- a/main/generated/torch.nn.LSTM.html
+++ b/main/generated/torch.nn.LSTM.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>LSTM<a class="headerlink" href="#lstm" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.LSTM">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">LSTM</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">hidden_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_layers</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">batch_first</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dropout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bidirectional</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">proj_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/rnn.html#LSTM"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/rnn.py#L795"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.LSTM" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">LSTM</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">hidden_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_layers</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">batch_first</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dropout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bidirectional</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">proj_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/rnn.html#LSTM"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/rnn.py#L795"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.LSTM" title="Permalink to this definition">¶</a></dt>
 <dd><p>Apply a multi-layer long short-term memory (LSTM) RNN to an input sequence.
 For each element in the input sequence, each layer computes the following
 function:</p>
diff --git a/main/generated/torch.nn.LSTMCell.html b/main/generated/torch.nn.LSTMCell.html
index e0edb325396..4af8e62749d 100644
--- a/main/generated/torch.nn.LSTMCell.html
+++ b/main/generated/torch.nn.LSTMCell.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>LSTMCell<a class="headerlink" href="#lstmcell" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.LSTMCell">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">LSTMCell</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">hidden_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/rnn.html#LSTMCell"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/rnn.py#L1610"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.LSTMCell" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">LSTMCell</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">hidden_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/rnn.html#LSTMCell"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/rnn.py#L1610"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.LSTMCell" title="Permalink to this definition">¶</a></dt>
 <dd><p>A long short-term memory (LSTM) cell.</p>
 <div class="math">
 <span class="katex-display"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML" display="block"><semantics><mtable rowspacing="0.16em" columnalign="left left" columnspacing="1em"><mtr><mtd><mstyle scriptlevel="0" displaystyle="false"><mrow><mi>i</mi><mo>=</mo><mi>σ</mi><mo stretchy="false">(</mo><msub><mi>W</mi><mrow><mi>i</mi><mi>i</mi></mrow></msub><mi>x</mi><mo>+</mo><msub><mi>b</mi><mrow><mi>i</mi><mi>i</mi></mrow></msub><mo>+</mo><msub><mi>W</mi><mrow><mi>h</mi><mi>i</mi></mrow></msub><mi>h</mi><mo>+</mo><msub><mi>b</mi><mrow><mi>h</mi><mi>i</mi></mrow></msub><mo stretchy="false">)</mo></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="false"><mrow><mi>f</mi><mo>=</mo><mi>σ</mi><mo stretchy="false">(</mo><msub><mi>W</mi><mrow><mi>i</mi><mi>f</mi></mrow></msub><mi>x</mi><mo>+</mo><msub><mi>b</mi><mrow><mi>i</mi><mi>f</mi></mrow></msub><mo>+</mo><msub><mi>W</mi><mrow><mi>h</mi><mi>f</mi></mrow></msub><mi>h</mi><mo>+</mo><msub><mi>b</mi><mrow><mi>h</mi><mi>f</mi></mrow></msub><mo stretchy="false">)</mo></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="false"><mrow><mi>g</mi><mo>=</mo><mi>tanh</mi><mo>⁡</mo><mo stretchy="false">(</mo><msub><mi>W</mi><mrow><mi>i</mi><mi>g</mi></mrow></msub><mi>x</mi><mo>+</mo><msub><mi>b</mi><mrow><mi>i</mi><mi>g</mi></mrow></msub><mo>+</mo><msub><mi>W</mi><mrow><mi>h</mi><mi>g</mi></mrow></msub><mi>h</mi><mo>+</mo><msub><mi>b</mi><mrow><mi>h</mi><mi>g</mi></mrow></msub><mo stretchy="false">)</mo></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="false"><mrow><mi>o</mi><mo>=</mo><mi>σ</mi><mo stretchy="false">(</mo><msub><mi>W</mi><mrow><mi>i</mi><mi>o</mi></mrow></msub><mi>x</mi><mo>+</mo><msub><mi>b</mi><mrow><mi>i</mi><mi>o</mi></mrow></msub><mo>+</mo><msub><mi>W</mi><mrow><mi>h</mi><mi>o</mi></mrow></msub><mi>h</mi><mo>+</mo><msub><mi>b</mi><mrow><mi>h</mi><mi>o</mi></mrow></msub><mo stretchy="false">)</mo></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="false"><mrow><msup><mi>c</mi><mo mathvariant="normal" lspace="0em" rspace="0em">′</mo></msup><mo>=</mo><mi>f</mi><mo>⊙</mo><mi>c</mi><mo>+</mo><mi>i</mi><mo>⊙</mo><mi>g</mi></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="false"><mrow><msup><mi>h</mi><mo mathvariant="normal" lspace="0em" rspace="0em">′</mo></msup><mo>=</mo><mi>o</mi><mo>⊙</mo><mi>tanh</mi><mo>⁡</mo><mo stretchy="false">(</mo><msup><mi>c</mi><mo mathvariant="normal" lspace="0em" rspace="0em">′</mo></msup><mo stretchy="false">)</mo></mrow></mstyle></mtd></mtr></mtable><annotation encoding="application/x-tex">\begin{array}{ll}
diff --git a/main/generated/torch.nn.LayerNorm.html b/main/generated/torch.nn.LayerNorm.html
index 1e6961d438d..76a868055c4 100644
--- a/main/generated/torch.nn.LayerNorm.html
+++ b/main/generated/torch.nn.LayerNorm.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>LayerNorm<a class="headerlink" href="#layernorm" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.LayerNorm">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">LayerNorm</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">normalized_shape</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-05</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">elementwise_affine</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/normalization.html#LayerNorm"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/normalization.py#L94"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.LayerNorm" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">LayerNorm</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">normalized_shape</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-05</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">elementwise_affine</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/normalization.html#LayerNorm"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/normalization.py#L94"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.LayerNorm" title="Permalink to this definition">¶</a></dt>
 <dd><p>Applies Layer Normalization over a mini-batch of inputs.</p>
 <p>This layer implements the operation as described in
 the paper <a class="reference external" href="https://arxiv.org/abs/1607.06450">Layer Normalization</a></p>
diff --git a/main/generated/torch.nn.LazyBatchNorm1d.html b/main/generated/torch.nn.LazyBatchNorm1d.html
index 5eaa9cefe45..57d218dc6e6 100644
--- a/main/generated/torch.nn.LazyBatchNorm1d.html
+++ b/main/generated/torch.nn.LazyBatchNorm1d.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>LazyBatchNorm1d<a class="headerlink" href="#lazybatchnorm1d" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.LazyBatchNorm1d">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">LazyBatchNorm1d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-05</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">momentum</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">affine</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">track_running_stats</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/batchnorm.html#LazyBatchNorm1d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/batchnorm.py#L344"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.LazyBatchNorm1d" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">LazyBatchNorm1d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-05</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">momentum</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">affine</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">track_running_stats</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/batchnorm.html#LazyBatchNorm1d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/batchnorm.py#L344"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.LazyBatchNorm1d" title="Permalink to this definition">¶</a></dt>
 <dd><p>A <a class="reference internal" href="torch.nn.BatchNorm1d.html#torch.nn.BatchNorm1d" title="torch.nn.BatchNorm1d"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.nn.BatchNorm1d</span></code></a> module with lazy initialization.</p>
 <p>Lazy initialization based on the <code class="docutils literal notranslate"><span class="pre">num_features</span></code> argument of the <a class="reference internal" href="torch.nn.BatchNorm1d.html#torch.nn.BatchNorm1d" title="torch.nn.BatchNorm1d"><code class="xref py py-class docutils literal notranslate"><span class="pre">BatchNorm1d</span></code></a> that is inferred
 from the <code class="docutils literal notranslate"><span class="pre">input.size(1)</span></code>.
@@ -641,7 +641,7 @@ <h1>LazyBatchNorm1d<a class="headerlink" href="#lazybatchnorm1d" title="Permalin
 </dl>
 <dl class="py attribute">
 <dt class="sig sig-object py" id="torch.nn.LazyBatchNorm1d.cls_to_become">
-<span class="sig-name descname"><span class="pre">cls_to_become</span></span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/batchnorm.py#L268"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.LazyBatchNorm1d.cls_to_become" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">cls_to_become</span></span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/batchnorm.py#L268"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.LazyBatchNorm1d.cls_to_become" title="Permalink to this definition">¶</a></dt>
 <dd><p>alias of <a class="reference internal" href="torch.nn.BatchNorm1d.html#torch.nn.BatchNorm1d" title="torch.nn.modules.batchnorm.BatchNorm1d"><code class="xref py py-class docutils literal notranslate"><span class="pre">BatchNorm1d</span></code></a></p>
 </dd></dl>
 
diff --git a/main/generated/torch.nn.LazyBatchNorm2d.html b/main/generated/torch.nn.LazyBatchNorm2d.html
index c9ace864fa0..0d1ce6c377e 100644
--- a/main/generated/torch.nn.LazyBatchNorm2d.html
+++ b/main/generated/torch.nn.LazyBatchNorm2d.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>LazyBatchNorm2d<a class="headerlink" href="#lazybatchnorm2d" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.LazyBatchNorm2d">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">LazyBatchNorm2d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-05</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">momentum</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">affine</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">track_running_stats</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/batchnorm.html#LazyBatchNorm2d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/batchnorm.py#L455"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.LazyBatchNorm2d" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">LazyBatchNorm2d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-05</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">momentum</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">affine</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">track_running_stats</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/batchnorm.html#LazyBatchNorm2d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/batchnorm.py#L455"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.LazyBatchNorm2d" title="Permalink to this definition">¶</a></dt>
 <dd><p>A <a class="reference internal" href="torch.nn.BatchNorm2d.html#torch.nn.BatchNorm2d" title="torch.nn.BatchNorm2d"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.nn.BatchNorm2d</span></code></a> module with lazy initialization.</p>
 <p>Lazy initialization is done for the <code class="docutils literal notranslate"><span class="pre">num_features</span></code> argument of the <a class="reference internal" href="torch.nn.BatchNorm2d.html#torch.nn.BatchNorm2d" title="torch.nn.BatchNorm2d"><code class="xref py py-class docutils literal notranslate"><span class="pre">BatchNorm2d</span></code></a> that is inferred
 from the <code class="docutils literal notranslate"><span class="pre">input.size(1)</span></code>.
@@ -641,7 +641,7 @@ <h1>LazyBatchNorm2d<a class="headerlink" href="#lazybatchnorm2d" title="Permalin
 </dl>
 <dl class="py attribute">
 <dt class="sig sig-object py" id="torch.nn.LazyBatchNorm2d.cls_to_become">
-<span class="sig-name descname"><span class="pre">cls_to_become</span></span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/batchnorm.py#L378"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.LazyBatchNorm2d.cls_to_become" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">cls_to_become</span></span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/batchnorm.py#L378"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.LazyBatchNorm2d.cls_to_become" title="Permalink to this definition">¶</a></dt>
 <dd><p>alias of <a class="reference internal" href="torch.nn.BatchNorm2d.html#torch.nn.BatchNorm2d" title="torch.nn.modules.batchnorm.BatchNorm2d"><code class="xref py py-class docutils literal notranslate"><span class="pre">BatchNorm2d</span></code></a></p>
 </dd></dl>
 
diff --git a/main/generated/torch.nn.LazyBatchNorm3d.html b/main/generated/torch.nn.LazyBatchNorm3d.html
index a7a6e3321e5..f22f40430b7 100644
--- a/main/generated/torch.nn.LazyBatchNorm3d.html
+++ b/main/generated/torch.nn.LazyBatchNorm3d.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>LazyBatchNorm3d<a class="headerlink" href="#lazybatchnorm3d" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.LazyBatchNorm3d">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">LazyBatchNorm3d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-05</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">momentum</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">affine</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">track_running_stats</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/batchnorm.html#LazyBatchNorm3d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/batchnorm.py#L566"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.LazyBatchNorm3d" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">LazyBatchNorm3d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-05</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">momentum</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">affine</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">track_running_stats</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/batchnorm.html#LazyBatchNorm3d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/batchnorm.py#L566"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.LazyBatchNorm3d" title="Permalink to this definition">¶</a></dt>
 <dd><p>A <a class="reference internal" href="torch.nn.BatchNorm3d.html#torch.nn.BatchNorm3d" title="torch.nn.BatchNorm3d"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.nn.BatchNorm3d</span></code></a> module with lazy initialization.</p>
 <p>Lazy initialization is done for the <code class="docutils literal notranslate"><span class="pre">num_features</span></code> argument of the <a class="reference internal" href="torch.nn.BatchNorm3d.html#torch.nn.BatchNorm3d" title="torch.nn.BatchNorm3d"><code class="xref py py-class docutils literal notranslate"><span class="pre">BatchNorm3d</span></code></a> that is inferred
 from the <code class="docutils literal notranslate"><span class="pre">input.size(1)</span></code>.
@@ -641,7 +641,7 @@ <h1>LazyBatchNorm3d<a class="headerlink" href="#lazybatchnorm3d" title="Permalin
 </dl>
 <dl class="py attribute">
 <dt class="sig sig-object py" id="torch.nn.LazyBatchNorm3d.cls_to_become">
-<span class="sig-name descname"><span class="pre">cls_to_become</span></span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/batchnorm.py#L489"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.LazyBatchNorm3d.cls_to_become" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">cls_to_become</span></span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/batchnorm.py#L489"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.LazyBatchNorm3d.cls_to_become" title="Permalink to this definition">¶</a></dt>
 <dd><p>alias of <a class="reference internal" href="torch.nn.BatchNorm3d.html#torch.nn.BatchNorm3d" title="torch.nn.modules.batchnorm.BatchNorm3d"><code class="xref py py-class docutils literal notranslate"><span class="pre">BatchNorm3d</span></code></a></p>
 </dd></dl>
 
diff --git a/main/generated/torch.nn.LazyConv1d.html b/main/generated/torch.nn.LazyConv1d.html
index 80ce4890ea7..2b599592947 100644
--- a/main/generated/torch.nn.LazyConv1d.html
+++ b/main/generated/torch.nn.LazyConv1d.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>LazyConv1d<a class="headerlink" href="#lazyconv1d" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.LazyConv1d">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">LazyConv1d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">out_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dilation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'zeros'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/conv.html#LazyConv1d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/conv.py#L1455"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.LazyConv1d" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">LazyConv1d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">out_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dilation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'zeros'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/conv.html#LazyConv1d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/conv.py#L1455"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.LazyConv1d" title="Permalink to this definition">¶</a></dt>
 <dd><p>A <a class="reference internal" href="torch.nn.Conv1d.html#torch.nn.Conv1d" title="torch.nn.Conv1d"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.nn.Conv1d</span></code></a> module with lazy initialization of the <code class="docutils literal notranslate"><span class="pre">in_channels</span></code> argument.</p>
 <p>The <code class="docutils literal notranslate"><span class="pre">in_channels</span></code> argument of the <a class="reference internal" href="torch.nn.Conv1d.html#torch.nn.Conv1d" title="torch.nn.Conv1d"><code class="xref py py-class docutils literal notranslate"><span class="pre">Conv1d</span></code></a> is inferred from the <code class="docutils literal notranslate"><span class="pre">input.size(1)</span></code>.
 The attributes that will be lazily initialized are <cite>weight</cite> and <cite>bias</cite>.</p>
@@ -643,7 +643,7 @@ <h1>LazyConv1d<a class="headerlink" href="#lazyconv1d" title="Permalink to this
 </div>
 <dl class="py attribute">
 <dt class="sig sig-object py" id="torch.nn.LazyConv1d.cls_to_become">
-<span class="sig-name descname"><span class="pre">cls_to_become</span></span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/conv.py#L214"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.LazyConv1d.cls_to_become" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">cls_to_become</span></span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/conv.py#L214"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.LazyConv1d.cls_to_become" title="Permalink to this definition">¶</a></dt>
 <dd><p>alias of <a class="reference internal" href="torch.nn.Conv1d.html#torch.nn.Conv1d" title="torch.nn.modules.conv.Conv1d"><code class="xref py py-class docutils literal notranslate"><span class="pre">Conv1d</span></code></a></p>
 </dd></dl>
 
diff --git a/main/generated/torch.nn.LazyConv2d.html b/main/generated/torch.nn.LazyConv2d.html
index 17822960b9d..6864287099d 100644
--- a/main/generated/torch.nn.LazyConv2d.html
+++ b/main/generated/torch.nn.LazyConv2d.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>LazyConv2d<a class="headerlink" href="#lazyconv2d" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.LazyConv2d">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">LazyConv2d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">out_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dilation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'zeros'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/conv.html#LazyConv2d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/conv.py#L1524"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.LazyConv2d" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">LazyConv2d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">out_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dilation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'zeros'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/conv.html#LazyConv2d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/conv.py#L1524"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.LazyConv2d" title="Permalink to this definition">¶</a></dt>
 <dd><p>A <a class="reference internal" href="torch.nn.Conv2d.html#torch.nn.Conv2d" title="torch.nn.Conv2d"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.nn.Conv2d</span></code></a> module with lazy initialization of the <code class="docutils literal notranslate"><span class="pre">in_channels</span></code> argument.</p>
 <p>The <code class="docutils literal notranslate"><span class="pre">in_channels</span></code> argument of the <a class="reference internal" href="torch.nn.Conv2d.html#torch.nn.Conv2d" title="torch.nn.Conv2d"><code class="xref py py-class docutils literal notranslate"><span class="pre">Conv2d</span></code></a> that is inferred from the <code class="docutils literal notranslate"><span class="pre">input.size(1)</span></code>.
 The attributes that will be lazily initialized are <cite>weight</cite> and <cite>bias</cite>.</p>
@@ -643,7 +643,7 @@ <h1>LazyConv2d<a class="headerlink" href="#lazyconv2d" title="Permalink to this
 </div>
 <dl class="py attribute">
 <dt class="sig sig-object py" id="torch.nn.LazyConv2d.cls_to_become">
-<span class="sig-name descname"><span class="pre">cls_to_become</span></span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/conv.py#L378"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.LazyConv2d.cls_to_become" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">cls_to_become</span></span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/conv.py#L378"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.LazyConv2d.cls_to_become" title="Permalink to this definition">¶</a></dt>
 <dd><p>alias of <a class="reference internal" href="torch.nn.Conv2d.html#torch.nn.Conv2d" title="torch.nn.modules.conv.Conv2d"><code class="xref py py-class docutils literal notranslate"><span class="pre">Conv2d</span></code></a></p>
 </dd></dl>
 
diff --git a/main/generated/torch.nn.LazyConv3d.html b/main/generated/torch.nn.LazyConv3d.html
index e1087b55f8e..2862d10d735 100644
--- a/main/generated/torch.nn.LazyConv3d.html
+++ b/main/generated/torch.nn.LazyConv3d.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>LazyConv3d<a class="headerlink" href="#lazyconv3d" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.LazyConv3d">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">LazyConv3d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">out_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dilation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'zeros'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/conv.html#LazyConv3d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/conv.py#L1593"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.LazyConv3d" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">LazyConv3d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">out_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dilation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'zeros'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/conv.html#LazyConv3d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/conv.py#L1593"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.LazyConv3d" title="Permalink to this definition">¶</a></dt>
 <dd><p>A <a class="reference internal" href="torch.nn.Conv3d.html#torch.nn.Conv3d" title="torch.nn.Conv3d"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.nn.Conv3d</span></code></a> module with lazy initialization of the <code class="docutils literal notranslate"><span class="pre">in_channels</span></code> argument.</p>
 <p>The <code class="docutils literal notranslate"><span class="pre">in_channels</span></code> argument of the <a class="reference internal" href="torch.nn.Conv3d.html#torch.nn.Conv3d" title="torch.nn.Conv3d"><code class="xref py py-class docutils literal notranslate"><span class="pre">Conv3d</span></code></a> that is inferred from
 the <code class="docutils literal notranslate"><span class="pre">input.size(1)</span></code>.
@@ -644,7 +644,7 @@ <h1>LazyConv3d<a class="headerlink" href="#lazyconv3d" title="Permalink to this
 </div>
 <dl class="py attribute">
 <dt class="sig sig-object py" id="torch.nn.LazyConv3d.cls_to_become">
-<span class="sig-name descname"><span class="pre">cls_to_become</span></span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/conv.py#L557"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.LazyConv3d.cls_to_become" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">cls_to_become</span></span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/conv.py#L557"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.LazyConv3d.cls_to_become" title="Permalink to this definition">¶</a></dt>
 <dd><p>alias of <a class="reference internal" href="torch.nn.Conv3d.html#torch.nn.Conv3d" title="torch.nn.modules.conv.Conv3d"><code class="xref py py-class docutils literal notranslate"><span class="pre">Conv3d</span></code></a></p>
 </dd></dl>
 
diff --git a/main/generated/torch.nn.LazyConvTranspose1d.html b/main/generated/torch.nn.LazyConvTranspose1d.html
index dcacba709ae..607e2e442a9 100644
--- a/main/generated/torch.nn.LazyConvTranspose1d.html
+++ b/main/generated/torch.nn.LazyConvTranspose1d.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>LazyConvTranspose1d<a class="headerlink" href="#lazyconvtranspose1d" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.LazyConvTranspose1d">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">LazyConvTranspose1d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">out_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dilation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'zeros'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/conv.html#LazyConvTranspose1d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/conv.py#L1663"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.LazyConvTranspose1d" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">LazyConvTranspose1d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">out_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dilation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'zeros'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/conv.html#LazyConvTranspose1d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/conv.py#L1663"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.LazyConvTranspose1d" title="Permalink to this definition">¶</a></dt>
 <dd><p>A <a class="reference internal" href="torch.nn.ConvTranspose1d.html#torch.nn.ConvTranspose1d" title="torch.nn.ConvTranspose1d"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.nn.ConvTranspose1d</span></code></a> module with lazy initialization of the <code class="docutils literal notranslate"><span class="pre">in_channels</span></code> argument.</p>
 <p>The <code class="docutils literal notranslate"><span class="pre">in_channels</span></code> argument of the <a class="reference internal" href="torch.nn.ConvTranspose1d.html#torch.nn.ConvTranspose1d" title="torch.nn.ConvTranspose1d"><code class="xref py py-class docutils literal notranslate"><span class="pre">ConvTranspose1d</span></code></a> that is inferred from
 the <code class="docutils literal notranslate"><span class="pre">input.size(1)</span></code>.
@@ -641,7 +641,7 @@ <h1>LazyConvTranspose1d<a class="headerlink" href="#lazyconvtranspose1d" title="
 </div>
 <dl class="py attribute">
 <dt class="sig sig-object py" id="torch.nn.LazyConvTranspose1d.cls_to_become">
-<span class="sig-name descname"><span class="pre">cls_to_become</span></span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/conv.py#L822"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.LazyConvTranspose1d.cls_to_become" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">cls_to_become</span></span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/conv.py#L822"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.LazyConvTranspose1d.cls_to_become" title="Permalink to this definition">¶</a></dt>
 <dd><p>alias of <a class="reference internal" href="torch.nn.ConvTranspose1d.html#torch.nn.ConvTranspose1d" title="torch.nn.modules.conv.ConvTranspose1d"><code class="xref py py-class docutils literal notranslate"><span class="pre">ConvTranspose1d</span></code></a></p>
 </dd></dl>
 
diff --git a/main/generated/torch.nn.LazyConvTranspose2d.html b/main/generated/torch.nn.LazyConvTranspose2d.html
index fad5ffd6e33..b004998b3c7 100644
--- a/main/generated/torch.nn.LazyConvTranspose2d.html
+++ b/main/generated/torch.nn.LazyConvTranspose2d.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>LazyConvTranspose2d<a class="headerlink" href="#lazyconvtranspose2d" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.LazyConvTranspose2d">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">LazyConvTranspose2d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">out_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dilation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'zeros'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/conv.html#LazyConvTranspose2d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/conv.py#L1732"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.LazyConvTranspose2d" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">LazyConvTranspose2d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">out_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dilation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'zeros'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/conv.html#LazyConvTranspose2d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/conv.py#L1732"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.LazyConvTranspose2d" title="Permalink to this definition">¶</a></dt>
 <dd><p>A <a class="reference internal" href="torch.nn.ConvTranspose2d.html#torch.nn.ConvTranspose2d" title="torch.nn.ConvTranspose2d"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.nn.ConvTranspose2d</span></code></a> module with lazy initialization of the <code class="docutils literal notranslate"><span class="pre">in_channels</span></code> argument.</p>
 <p>The <code class="docutils literal notranslate"><span class="pre">in_channels</span></code> argument of the <a class="reference internal" href="torch.nn.ConvTranspose2d.html#torch.nn.ConvTranspose2d" title="torch.nn.ConvTranspose2d"><code class="xref py py-class docutils literal notranslate"><span class="pre">ConvTranspose2d</span></code></a> is inferred from
 the <code class="docutils literal notranslate"><span class="pre">input.size(1)</span></code>.
@@ -641,7 +641,7 @@ <h1>LazyConvTranspose2d<a class="headerlink" href="#lazyconvtranspose2d" title="
 </div>
 <dl class="py attribute">
 <dt class="sig sig-object py" id="torch.nn.LazyConvTranspose2d.cls_to_become">
-<span class="sig-name descname"><span class="pre">cls_to_become</span></span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/conv.py#L986"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.LazyConvTranspose2d.cls_to_become" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">cls_to_become</span></span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/conv.py#L986"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.LazyConvTranspose2d.cls_to_become" title="Permalink to this definition">¶</a></dt>
 <dd><p>alias of <a class="reference internal" href="torch.nn.ConvTranspose2d.html#torch.nn.ConvTranspose2d" title="torch.nn.modules.conv.ConvTranspose2d"><code class="xref py py-class docutils literal notranslate"><span class="pre">ConvTranspose2d</span></code></a></p>
 </dd></dl>
 
diff --git a/main/generated/torch.nn.LazyConvTranspose3d.html b/main/generated/torch.nn.LazyConvTranspose3d.html
index 9ba926c0fff..f6e00b89ad4 100644
--- a/main/generated/torch.nn.LazyConvTranspose3d.html
+++ b/main/generated/torch.nn.LazyConvTranspose3d.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>LazyConvTranspose3d<a class="headerlink" href="#lazyconvtranspose3d" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.LazyConvTranspose3d">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">LazyConvTranspose3d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">out_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dilation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'zeros'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/conv.html#LazyConvTranspose3d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/conv.py#L1801"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.LazyConvTranspose3d" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">LazyConvTranspose3d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">out_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dilation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'zeros'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/conv.html#LazyConvTranspose3d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/conv.py#L1801"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.LazyConvTranspose3d" title="Permalink to this definition">¶</a></dt>
 <dd><p>A <a class="reference internal" href="torch.nn.ConvTranspose3d.html#torch.nn.ConvTranspose3d" title="torch.nn.ConvTranspose3d"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.nn.ConvTranspose3d</span></code></a> module with lazy initialization of the <code class="docutils literal notranslate"><span class="pre">in_channels</span></code> argument.</p>
 <p>The <code class="docutils literal notranslate"><span class="pre">in_channels</span></code> argument of the <a class="reference internal" href="torch.nn.ConvTranspose3d.html#torch.nn.ConvTranspose3d" title="torch.nn.ConvTranspose3d"><code class="xref py py-class docutils literal notranslate"><span class="pre">ConvTranspose3d</span></code></a> is inferred from
 the <code class="docutils literal notranslate"><span class="pre">input.size(1)</span></code>.
@@ -641,7 +641,7 @@ <h1>LazyConvTranspose3d<a class="headerlink" href="#lazyconvtranspose3d" title="
 </div>
 <dl class="py attribute">
 <dt class="sig sig-object py" id="torch.nn.LazyConvTranspose3d.cls_to_become">
-<span class="sig-name descname"><span class="pre">cls_to_become</span></span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/conv.py#L1174"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.LazyConvTranspose3d.cls_to_become" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">cls_to_become</span></span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/conv.py#L1174"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.LazyConvTranspose3d.cls_to_become" title="Permalink to this definition">¶</a></dt>
 <dd><p>alias of <a class="reference internal" href="torch.nn.ConvTranspose3d.html#torch.nn.ConvTranspose3d" title="torch.nn.modules.conv.ConvTranspose3d"><code class="xref py py-class docutils literal notranslate"><span class="pre">ConvTranspose3d</span></code></a></p>
 </dd></dl>
 
diff --git a/main/generated/torch.nn.LazyInstanceNorm1d.html b/main/generated/torch.nn.LazyInstanceNorm1d.html
index e11924157ea..52614020def 100644
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+++ b/main/generated/torch.nn.LazyInstanceNorm1d.html
@@ -300,7 +300,7 @@
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     <div class="version">
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+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>LazyInstanceNorm1d<a class="headerlink" href="#lazyinstancenorm1d" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.LazyInstanceNorm1d">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">LazyInstanceNorm1d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-05</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">momentum</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">affine</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">track_running_stats</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/instancenorm.html#LazyInstanceNorm1d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/instancenorm.py#L204"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.LazyInstanceNorm1d" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">LazyInstanceNorm1d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-05</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">momentum</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">affine</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">track_running_stats</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/instancenorm.html#LazyInstanceNorm1d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/instancenorm.py#L204"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.LazyInstanceNorm1d" title="Permalink to this definition">¶</a></dt>
 <dd><p>A <a class="reference internal" href="torch.nn.InstanceNorm1d.html#torch.nn.InstanceNorm1d" title="torch.nn.InstanceNorm1d"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.nn.InstanceNorm1d</span></code></a> module with lazy initialization of the <code class="docutils literal notranslate"><span class="pre">num_features</span></code> argument.</p>
 <p>The <code class="docutils literal notranslate"><span class="pre">num_features</span></code> argument of the <a class="reference internal" href="torch.nn.InstanceNorm1d.html#torch.nn.InstanceNorm1d" title="torch.nn.InstanceNorm1d"><code class="xref py py-class docutils literal notranslate"><span class="pre">InstanceNorm1d</span></code></a> is inferred from the <code class="docutils literal notranslate"><span class="pre">input.size(1)</span></code>.
 The attributes that will be lazily initialized are <cite>weight</cite>, <cite>bias</cite>, <cite>running_mean</cite> and <cite>running_var</cite>.</p>
@@ -644,7 +644,7 @@ <h1>LazyInstanceNorm1d<a class="headerlink" href="#lazyinstancenorm1d" title="Pe
 </dl>
 <dl class="py attribute">
 <dt class="sig sig-object py" id="torch.nn.LazyInstanceNorm1d.cls_to_become">
-<span class="sig-name descname"><span class="pre">cls_to_become</span></span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/instancenorm.py#L127"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.LazyInstanceNorm1d.cls_to_become" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">cls_to_become</span></span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/instancenorm.py#L127"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.LazyInstanceNorm1d.cls_to_become" title="Permalink to this definition">¶</a></dt>
 <dd><p>alias of <a class="reference internal" href="torch.nn.InstanceNorm1d.html#torch.nn.InstanceNorm1d" title="torch.nn.modules.instancenorm.InstanceNorm1d"><code class="xref py py-class docutils literal notranslate"><span class="pre">InstanceNorm1d</span></code></a></p>
 </dd></dl>
 
diff --git a/main/generated/torch.nn.LazyInstanceNorm2d.html b/main/generated/torch.nn.LazyInstanceNorm2d.html
index e7dcf89adb6..51c4b6cf107 100644
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+++ b/main/generated/torch.nn.LazyInstanceNorm2d.html
@@ -300,7 +300,7 @@
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     <div class="version">
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+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>LazyInstanceNorm2d<a class="headerlink" href="#lazyinstancenorm2d" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.LazyInstanceNorm2d">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">LazyInstanceNorm2d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-05</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">momentum</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">affine</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">track_running_stats</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/instancenorm.html#LazyInstanceNorm2d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/instancenorm.py#L320"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.LazyInstanceNorm2d" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">LazyInstanceNorm2d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-05</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">momentum</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">affine</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">track_running_stats</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/instancenorm.html#LazyInstanceNorm2d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/instancenorm.py#L320"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.LazyInstanceNorm2d" title="Permalink to this definition">¶</a></dt>
 <dd><p>A <a class="reference internal" href="torch.nn.InstanceNorm2d.html#torch.nn.InstanceNorm2d" title="torch.nn.InstanceNorm2d"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.nn.InstanceNorm2d</span></code></a> module with lazy initialization of the <code class="docutils literal notranslate"><span class="pre">num_features</span></code> argument.</p>
 <p>The <code class="docutils literal notranslate"><span class="pre">num_features</span></code> argument of the <a class="reference internal" href="torch.nn.InstanceNorm2d.html#torch.nn.InstanceNorm2d" title="torch.nn.InstanceNorm2d"><code class="xref py py-class docutils literal notranslate"><span class="pre">InstanceNorm2d</span></code></a> is inferred from the <code class="docutils literal notranslate"><span class="pre">input.size(1)</span></code>.
 The attributes that will be lazily initialized are <cite>weight</cite>, <cite>bias</cite>,
@@ -645,7 +645,7 @@ <h1>LazyInstanceNorm2d<a class="headerlink" href="#lazyinstancenorm2d" title="Pe
 </dl>
 <dl class="py attribute">
 <dt class="sig sig-object py" id="torch.nn.LazyInstanceNorm2d.cls_to_become">
-<span class="sig-name descname"><span class="pre">cls_to_become</span></span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/instancenorm.py#L241"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.LazyInstanceNorm2d.cls_to_become" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">cls_to_become</span></span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/instancenorm.py#L241"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.LazyInstanceNorm2d.cls_to_become" title="Permalink to this definition">¶</a></dt>
 <dd><p>alias of <a class="reference internal" href="torch.nn.InstanceNorm2d.html#torch.nn.InstanceNorm2d" title="torch.nn.modules.instancenorm.InstanceNorm2d"><code class="xref py py-class docutils literal notranslate"><span class="pre">InstanceNorm2d</span></code></a></p>
 </dd></dl>
 
diff --git a/main/generated/torch.nn.LazyInstanceNorm3d.html b/main/generated/torch.nn.LazyInstanceNorm3d.html
index 9450f684e72..6c64f66e16e 100644
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+++ b/main/generated/torch.nn.LazyInstanceNorm3d.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
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     <div id="searchBox">
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@@ -612,7 +612,7 @@
 <h1>LazyInstanceNorm3d<a class="headerlink" href="#lazyinstancenorm3d" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.LazyInstanceNorm3d">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">LazyInstanceNorm3d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-05</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">momentum</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">affine</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">track_running_stats</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/instancenorm.html#LazyInstanceNorm3d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/instancenorm.py#L436"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.LazyInstanceNorm3d" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">LazyInstanceNorm3d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-05</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">momentum</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">affine</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">track_running_stats</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/instancenorm.html#LazyInstanceNorm3d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/instancenorm.py#L436"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.LazyInstanceNorm3d" title="Permalink to this definition">¶</a></dt>
 <dd><p>A <a class="reference internal" href="torch.nn.InstanceNorm3d.html#torch.nn.InstanceNorm3d" title="torch.nn.InstanceNorm3d"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.nn.InstanceNorm3d</span></code></a> module with lazy initialization of the <code class="docutils literal notranslate"><span class="pre">num_features</span></code> argument.</p>
 <p>The <code class="docutils literal notranslate"><span class="pre">num_features</span></code> argument of the <a class="reference internal" href="torch.nn.InstanceNorm3d.html#torch.nn.InstanceNorm3d" title="torch.nn.InstanceNorm3d"><code class="xref py py-class docutils literal notranslate"><span class="pre">InstanceNorm3d</span></code></a> is inferred from the <code class="docutils literal notranslate"><span class="pre">input.size(1)</span></code>.
 The attributes that will be lazily initialized are <cite>weight</cite>, <cite>bias</cite>,
@@ -645,7 +645,7 @@ <h1>LazyInstanceNorm3d<a class="headerlink" href="#lazyinstancenorm3d" title="Pe
 </dl>
 <dl class="py attribute">
 <dt class="sig sig-object py" id="torch.nn.LazyInstanceNorm3d.cls_to_become">
-<span class="sig-name descname"><span class="pre">cls_to_become</span></span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/instancenorm.py#L358"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.LazyInstanceNorm3d.cls_to_become" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">cls_to_become</span></span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/instancenorm.py#L358"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.LazyInstanceNorm3d.cls_to_become" title="Permalink to this definition">¶</a></dt>
 <dd><p>alias of <a class="reference internal" href="torch.nn.InstanceNorm3d.html#torch.nn.InstanceNorm3d" title="torch.nn.modules.instancenorm.InstanceNorm3d"><code class="xref py py-class docutils literal notranslate"><span class="pre">InstanceNorm3d</span></code></a></p>
 </dd></dl>
 
diff --git a/main/generated/torch.nn.LazyLinear.html b/main/generated/torch.nn.LazyLinear.html
index 0a4ad4ae4cb..d407588a296 100644
--- a/main/generated/torch.nn.LazyLinear.html
+++ b/main/generated/torch.nn.LazyLinear.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>LazyLinear<a class="headerlink" href="#lazylinear" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.LazyLinear">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">LazyLinear</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">out_features</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/linear.html#LazyLinear"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/linear.py#L234"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.LazyLinear" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">LazyLinear</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">out_features</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/linear.html#LazyLinear"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/linear.py#L234"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.LazyLinear" title="Permalink to this definition">¶</a></dt>
 <dd><p>A <a class="reference internal" href="torch.nn.Linear.html#torch.nn.Linear" title="torch.nn.Linear"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.nn.Linear</span></code></a> module where <cite>in_features</cite> is inferred.</p>
 <p>In this module, the <cite>weight</cite> and <cite>bias</cite> are of <code class="xref py py-class docutils literal notranslate"><span class="pre">torch.nn.UninitializedParameter</span></code>
 class. They will be initialized after the first call to <code class="docutils literal notranslate"><span class="pre">forward</span></code> is done and the
@@ -687,7 +687,7 @@ <h1>LazyLinear<a class="headerlink" href="#lazylinear" title="Permalink to this
 </dl>
 <dl class="py attribute">
 <dt class="sig sig-object py" id="torch.nn.LazyLinear.cls_to_become">
-<span class="sig-name descname"><span class="pre">cls_to_become</span></span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/linear.py#L50"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.LazyLinear.cls_to_become" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">cls_to_become</span></span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/linear.py#L50"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.LazyLinear.cls_to_become" title="Permalink to this definition">¶</a></dt>
 <dd><p>alias of <a class="reference internal" href="torch.nn.Linear.html#torch.nn.Linear" title="torch.nn.modules.linear.Linear"><code class="xref py py-class docutils literal notranslate"><span class="pre">Linear</span></code></a></p>
 </dd></dl>
 
diff --git a/main/generated/torch.nn.LeakyReLU.html b/main/generated/torch.nn.LeakyReLU.html
index 83b6fdcc398..156d2ead4d8 100644
--- a/main/generated/torch.nn.LeakyReLU.html
+++ b/main/generated/torch.nn.LeakyReLU.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
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@@ -612,7 +612,7 @@
 <h1>LeakyReLU<a class="headerlink" href="#leakyrelu" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.LeakyReLU">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">LeakyReLU</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">negative_slope</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.01</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/activation.html#LeakyReLU"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/activation.py#L783"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.LeakyReLU" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">LeakyReLU</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">negative_slope</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.01</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/activation.html#LeakyReLU"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/activation.py#L783"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.LeakyReLU" title="Permalink to this definition">¶</a></dt>
 <dd><p>Applies the LeakyReLU function element-wise.</p>
 <div class="math">
 <span class="katex-display"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML" display="block"><semantics><mrow><mtext>LeakyReLU</mtext><mo stretchy="false">(</mo><mi>x</mi><mo stretchy="false">)</mo><mo>=</mo><mi>max</mi><mo>⁡</mo><mo stretchy="false">(</mo><mn>0</mn><mo separator="true">,</mo><mi>x</mi><mo stretchy="false">)</mo><mo>+</mo><mtext>negative_slope</mtext><mo>∗</mo><mi>min</mi><mo>⁡</mo><mo stretchy="false">(</mo><mn>0</mn><mo separator="true">,</mo><mi>x</mi><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">\text{LeakyReLU}(x) = \max(0, x) + \text{negative\_slope} * \min(0, x)
diff --git a/main/generated/torch.nn.Linear.html b/main/generated/torch.nn.Linear.html
index f87c68ebb2f..cbc0cbebd03 100644
--- a/main/generated/torch.nn.Linear.html
+++ b/main/generated/torch.nn.Linear.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>Linear<a class="headerlink" href="#linear" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.Linear">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">Linear</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">in_features</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out_features</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/linear.html#Linear"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/linear.py#L50"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Linear" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">Linear</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">in_features</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out_features</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/linear.html#Linear"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/linear.py#L50"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Linear" title="Permalink to this definition">¶</a></dt>
 <dd><p>Applies an affine linear transformation to the incoming data: <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>y</mi><mo>=</mo><mi>x</mi><msup><mi>A</mi><mi>T</mi></msup><mo>+</mo><mi>b</mi></mrow><annotation encoding="application/x-tex">y = xA^T + b</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.625em;vertical-align:-0.1944em;"></span><span class="mord mathnormal" style="margin-right:0.03588em;">y</span><span class="mspace" style="margin-right:0.2778em;"></span><span class="mrel">=</span><span class="mspace" style="margin-right:0.2778em;"></span></span><span class="base"><span class="strut" style="height:0.9247em;vertical-align:-0.0833em;"></span><span class="mord mathnormal">x</span><span class="mord"><span class="mord mathnormal">A</span><span class="msupsub"><span class="vlist-t"><span class="vlist-r"><span class="vlist" style="height:0.8413em;"><span style="top:-3.063em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mathnormal mtight" style="margin-right:0.13889em;">T</span></span></span></span></span></span></span></span><span class="mspace" style="margin-right:0.2222em;"></span><span class="mbin">+</span><span class="mspace" style="margin-right:0.2222em;"></span></span><span class="base"><span class="strut" style="height:0.6944em;"></span><span class="mord mathnormal">b</span></span></span></span></span>.</p>
 <p>This module supports <a class="reference internal" href="../notes/cuda.html#tf32-on-ampere"><span class="std std-ref">TensorFloat32</span></a>.</p>
 <p>On certain ROCm devices, when using float16 inputs this module will use <a class="reference internal" href="../notes/numerical_accuracy.html#fp16-on-mi200"><span class="std std-ref">different precision</span></a> for backward.</p>
diff --git a/main/generated/torch.nn.LocalResponseNorm.html b/main/generated/torch.nn.LocalResponseNorm.html
index 638bce42371..dac3d69fb52 100644
--- a/main/generated/torch.nn.LocalResponseNorm.html
+++ b/main/generated/torch.nn.LocalResponseNorm.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
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     </div>
     <div id="searchBox">
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@@ -612,7 +612,7 @@
 <h1>LocalResponseNorm<a class="headerlink" href="#localresponsenorm" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.LocalResponseNorm">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">LocalResponseNorm</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">beta</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.75</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">k</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/normalization.html#LocalResponseNorm"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/normalization.py#L17"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.LocalResponseNorm" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">LocalResponseNorm</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">beta</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.75</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">k</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/normalization.html#LocalResponseNorm"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/normalization.py#L17"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.LocalResponseNorm" title="Permalink to this definition">¶</a></dt>
 <dd><p>Applies local response normalization over an input signal.</p>
 <p>The input signal is composed of several input planes, where channels occupy the second dimension.
 Applies normalization across channels.</p>
diff --git a/main/generated/torch.nn.LogSigmoid.html b/main/generated/torch.nn.LogSigmoid.html
index d4548a9d836..cf438bde920 100644
--- a/main/generated/torch.nn.LogSigmoid.html
+++ b/main/generated/torch.nn.LogSigmoid.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
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     </div>
     <div id="searchBox">
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@@ -612,7 +612,7 @@
 <h1>LogSigmoid<a class="headerlink" href="#logsigmoid" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.LogSigmoid">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">LogSigmoid</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/activation.html#LogSigmoid"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/activation.py#L835"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.LogSigmoid" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">LogSigmoid</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/activation.html#LogSigmoid"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/activation.py#L835"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.LogSigmoid" title="Permalink to this definition">¶</a></dt>
 <dd><p>Applies the Logsigmoid function element-wise.</p>
 <div class="math">
 <span class="katex-display"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML" display="block"><semantics><mrow><mtext>LogSigmoid</mtext><mo stretchy="false">(</mo><mi>x</mi><mo stretchy="false">)</mo><mo>=</mo><mi>log</mi><mo>⁡</mo><mrow><mo fence="true">(</mo><mfrac><mn>1</mn><mrow><mn>1</mn><mo>+</mo><mi>exp</mi><mo>⁡</mo><mo stretchy="false">(</mo><mo>−</mo><mi>x</mi><mo stretchy="false">)</mo></mrow></mfrac><mo fence="true">)</mo></mrow></mrow><annotation encoding="application/x-tex">\text{LogSigmoid}(x) = \log\left(\frac{ 1 }{ 1 + \exp(-x)}\right)
diff --git a/main/generated/torch.nn.LogSoftmax.html b/main/generated/torch.nn.LogSoftmax.html
index fb101fe030f..ef1848fed97 100644
--- a/main/generated/torch.nn.LogSoftmax.html
+++ b/main/generated/torch.nn.LogSoftmax.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
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     </div>
     <div id="searchBox">
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@@ -612,7 +612,7 @@
 <h1>LogSoftmax<a class="headerlink" href="#logsoftmax" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.LogSoftmax">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">LogSoftmax</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/activation.html#LogSoftmax"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/activation.py#L1708"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.LogSoftmax" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">LogSoftmax</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/activation.html#LogSoftmax"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/activation.py#L1708"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.LogSoftmax" title="Permalink to this definition">¶</a></dt>
 <dd><p>Applies the <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>log</mi><mo>⁡</mo><mo stretchy="false">(</mo><mtext>Softmax</mtext><mo stretchy="false">(</mo><mi>x</mi><mo stretchy="false">)</mo><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">\log(\text{Softmax}(x))</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mop">lo<span style="margin-right:0.01389em;">g</span></span><span class="mopen">(</span><span class="mord text"><span class="mord">Softmax</span></span><span class="mopen">(</span><span class="mord mathnormal">x</span><span class="mclose">))</span></span></span></span></span> function to an n-dimensional input Tensor.</p>
 <p>The LogSoftmax formulation can be simplified as:</p>
 <div class="math">
diff --git a/main/generated/torch.nn.MSELoss.html b/main/generated/torch.nn.MSELoss.html
index adbdd0ab811..8dd838dc97c 100644
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@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
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     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>MSELoss<a class="headerlink" href="#mseloss" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.MSELoss">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">MSELoss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">size_average</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduce</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mean'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/loss.html#MSELoss"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/loss.py#L548"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.MSELoss" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">MSELoss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">size_average</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduce</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mean'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/loss.html#MSELoss"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/loss.py#L548"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.MSELoss" title="Permalink to this definition">¶</a></dt>
 <dd><p>Creates a criterion that measures the mean squared error (squared L2 norm) between
 each element in the input <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>x</mi></mrow><annotation encoding="application/x-tex">x</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.4306em;"></span><span class="mord mathnormal">x</span></span></span></span></span> and target <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>y</mi></mrow><annotation encoding="application/x-tex">y</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.625em;vertical-align:-0.1944em;"></span><span class="mord mathnormal" style="margin-right:0.03588em;">y</span></span></span></span></span>.</p>
 <p>The unreduced (i.e. with <code class="xref py py-attr docutils literal notranslate"><span class="pre">reduction</span></code> set to <code class="docutils literal notranslate"><span class="pre">'none'</span></code>) loss can be described as:</p>
diff --git a/main/generated/torch.nn.MarginRankingLoss.html b/main/generated/torch.nn.MarginRankingLoss.html
index 0eb87475c75..f6d2bded2dc 100644
--- a/main/generated/torch.nn.MarginRankingLoss.html
+++ b/main/generated/torch.nn.MarginRankingLoss.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>MarginRankingLoss<a class="headerlink" href="#marginrankingloss" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.MarginRankingLoss">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">MarginRankingLoss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">margin</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">size_average</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduce</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mean'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/loss.html#MarginRankingLoss"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/loss.py#L1430"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.MarginRankingLoss" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">MarginRankingLoss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">margin</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">size_average</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduce</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mean'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/loss.html#MarginRankingLoss"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/loss.py#L1430"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.MarginRankingLoss" title="Permalink to this definition">¶</a></dt>
 <dd><p>Creates a criterion that measures the loss given
 inputs <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>x</mi><mn>1</mn></mrow><annotation encoding="application/x-tex">x1</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.6444em;"></span><span class="mord mathnormal">x</span><span class="mord">1</span></span></span></span></span>, <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>x</mi><mn>2</mn></mrow><annotation encoding="application/x-tex">x2</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.6444em;"></span><span class="mord mathnormal">x</span><span class="mord">2</span></span></span></span></span>, two 1D mini-batch or 0D <cite>Tensors</cite>,
 and a label 1D mini-batch or 0D <cite>Tensor</cite> <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>y</mi></mrow><annotation encoding="application/x-tex">y</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.625em;vertical-align:-0.1944em;"></span><span class="mord mathnormal" style="margin-right:0.03588em;">y</span></span></span></span></span> (containing 1 or -1).</p>
diff --git a/main/generated/torch.nn.MaxPool1d.html b/main/generated/torch.nn.MaxPool1d.html
index f2b4970fbe2..e62b3bb9735 100644
--- a/main/generated/torch.nn.MaxPool1d.html
+++ b/main/generated/torch.nn.MaxPool1d.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
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@@ -612,7 +612,7 @@
 <h1>MaxPool1d<a class="headerlink" href="#maxpool1d" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.MaxPool1d">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">MaxPool1d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dilation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_indices</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ceil_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/pooling.html#MaxPool1d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/pooling.py#L81"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.MaxPool1d" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">MaxPool1d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dilation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_indices</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ceil_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/pooling.html#MaxPool1d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/pooling.py#L81"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.MaxPool1d" title="Permalink to this definition">¶</a></dt>
 <dd><p>Applies a 1D max pooling over an input signal composed of several input planes.</p>
 <p>In the simplest case, the output value of the layer with input size <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mo stretchy="false">(</mo><mi>N</mi><mo separator="true">,</mo><mi>C</mi><mo separator="true">,</mo><mi>L</mi><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">(N, C, L)</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mopen">(</span><span class="mord mathnormal" style="margin-right:0.10903em;">N</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord mathnormal" style="margin-right:0.07153em;">C</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord mathnormal">L</span><span class="mclose">)</span></span></span></span></span>
 and output <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mo stretchy="false">(</mo><mi>N</mi><mo separator="true">,</mo><mi>C</mi><mo separator="true">,</mo><msub><mi>L</mi><mrow><mi>o</mi><mi>u</mi><mi>t</mi></mrow></msub><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">(N, C, L_{out})</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mopen">(</span><span class="mord mathnormal" style="margin-right:0.10903em;">N</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord mathnormal" style="margin-right:0.07153em;">C</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord"><span class="mord mathnormal">L</span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.2806em;"><span style="top:-2.55em;margin-left:0em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mord mathnormal mtight">o</span><span class="mord mathnormal mtight">u</span><span class="mord mathnormal mtight">t</span></span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.15em;"><span></span></span></span></span></span></span><span class="mclose">)</span></span></span></span></span> can be precisely described as:</p>
diff --git a/main/generated/torch.nn.MaxPool2d.html b/main/generated/torch.nn.MaxPool2d.html
index f7a5c1927a4..9410c170015 100644
--- a/main/generated/torch.nn.MaxPool2d.html
+++ b/main/generated/torch.nn.MaxPool2d.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
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@@ -612,7 +612,7 @@
 <h1>MaxPool2d<a class="headerlink" href="#maxpool2d" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.MaxPool2d">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">MaxPool2d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dilation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_indices</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ceil_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/pooling.html#MaxPool2d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/pooling.py#L145"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.MaxPool2d" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">MaxPool2d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dilation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_indices</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ceil_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/pooling.html#MaxPool2d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/pooling.py#L145"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.MaxPool2d" title="Permalink to this definition">¶</a></dt>
 <dd><p>Applies a 2D max pooling over an input signal composed of several input planes.</p>
 <p>In the simplest case, the output value of the layer with input size <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mo stretchy="false">(</mo><mi>N</mi><mo separator="true">,</mo><mi>C</mi><mo separator="true">,</mo><mi>H</mi><mo separator="true">,</mo><mi>W</mi><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">(N, C, H, W)</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mopen">(</span><span class="mord mathnormal" style="margin-right:0.10903em;">N</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord mathnormal" style="margin-right:0.07153em;">C</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord mathnormal" style="margin-right:0.08125em;">H</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord mathnormal" style="margin-right:0.13889em;">W</span><span class="mclose">)</span></span></span></span></span>,
 output <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mo stretchy="false">(</mo><mi>N</mi><mo separator="true">,</mo><mi>C</mi><mo separator="true">,</mo><msub><mi>H</mi><mrow><mi>o</mi><mi>u</mi><mi>t</mi></mrow></msub><mo separator="true">,</mo><msub><mi>W</mi><mrow><mi>o</mi><mi>u</mi><mi>t</mi></mrow></msub><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">(N, C, H_{out}, W_{out})</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mopen">(</span><span class="mord mathnormal" style="margin-right:0.10903em;">N</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord mathnormal" style="margin-right:0.07153em;">C</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord"><span class="mord mathnormal" style="margin-right:0.08125em;">H</span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.2806em;"><span style="top:-2.55em;margin-left:-0.0813em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mord mathnormal mtight">o</span><span class="mord mathnormal mtight">u</span><span class="mord mathnormal mtight">t</span></span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.15em;"><span></span></span></span></span></span></span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord"><span class="mord mathnormal" style="margin-right:0.13889em;">W</span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.2806em;"><span style="top:-2.55em;margin-left:-0.1389em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mord mathnormal mtight">o</span><span class="mord mathnormal mtight">u</span><span class="mord mathnormal mtight">t</span></span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.15em;"><span></span></span></span></span></span></span><span class="mclose">)</span></span></span></span></span> and <code class="xref py py-attr docutils literal notranslate"><span class="pre">kernel_size</span></code> <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mo stretchy="false">(</mo><mi>k</mi><mi>H</mi><mo separator="true">,</mo><mi>k</mi><mi>W</mi><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">(kH, kW)</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mopen">(</span><span class="mord mathnormal" style="margin-right:0.03148em;">k</span><span class="mord mathnormal" style="margin-right:0.08125em;">H</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord mathnormal" style="margin-right:0.13889em;">kW</span><span class="mclose">)</span></span></span></span></span>
diff --git a/main/generated/torch.nn.MaxPool3d.html b/main/generated/torch.nn.MaxPool3d.html
index ff627f4d6f4..6573a15ea28 100644
--- a/main/generated/torch.nn.MaxPool3d.html
+++ b/main/generated/torch.nn.MaxPool3d.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>MaxPool3d<a class="headerlink" href="#maxpool3d" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.MaxPool3d">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">MaxPool3d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dilation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_indices</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ceil_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/pooling.html#MaxPool3d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/pooling.py#L224"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.MaxPool3d" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">MaxPool3d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dilation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_indices</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ceil_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/pooling.html#MaxPool3d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/pooling.py#L224"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.MaxPool3d" title="Permalink to this definition">¶</a></dt>
 <dd><p>Applies a 3D max pooling over an input signal composed of several input planes.</p>
 <p>In the simplest case, the output value of the layer with input size <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mo stretchy="false">(</mo><mi>N</mi><mo separator="true">,</mo><mi>C</mi><mo separator="true">,</mo><mi>D</mi><mo separator="true">,</mo><mi>H</mi><mo separator="true">,</mo><mi>W</mi><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">(N, C, D, H, W)</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mopen">(</span><span class="mord mathnormal" style="margin-right:0.10903em;">N</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord mathnormal" style="margin-right:0.07153em;">C</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord mathnormal" style="margin-right:0.02778em;">D</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord mathnormal" style="margin-right:0.08125em;">H</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord mathnormal" style="margin-right:0.13889em;">W</span><span class="mclose">)</span></span></span></span></span>,
 output <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mo stretchy="false">(</mo><mi>N</mi><mo separator="true">,</mo><mi>C</mi><mo separator="true">,</mo><msub><mi>D</mi><mrow><mi>o</mi><mi>u</mi><mi>t</mi></mrow></msub><mo separator="true">,</mo><msub><mi>H</mi><mrow><mi>o</mi><mi>u</mi><mi>t</mi></mrow></msub><mo separator="true">,</mo><msub><mi>W</mi><mrow><mi>o</mi><mi>u</mi><mi>t</mi></mrow></msub><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">(N, C, D_{out}, H_{out}, W_{out})</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mopen">(</span><span class="mord mathnormal" style="margin-right:0.10903em;">N</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord mathnormal" style="margin-right:0.07153em;">C</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord"><span class="mord mathnormal" style="margin-right:0.02778em;">D</span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.2806em;"><span style="top:-2.55em;margin-left:-0.0278em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mord mathnormal mtight">o</span><span class="mord mathnormal mtight">u</span><span class="mord mathnormal mtight">t</span></span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.15em;"><span></span></span></span></span></span></span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord"><span class="mord mathnormal" style="margin-right:0.08125em;">H</span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.2806em;"><span style="top:-2.55em;margin-left:-0.0813em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mord mathnormal mtight">o</span><span class="mord mathnormal mtight">u</span><span class="mord mathnormal mtight">t</span></span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.15em;"><span></span></span></span></span></span></span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord"><span class="mord mathnormal" style="margin-right:0.13889em;">W</span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.2806em;"><span style="top:-2.55em;margin-left:-0.1389em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mord mathnormal mtight">o</span><span class="mord mathnormal mtight">u</span><span class="mord mathnormal mtight">t</span></span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.15em;"><span></span></span></span></span></span></span><span class="mclose">)</span></span></span></span></span> and <code class="xref py py-attr docutils literal notranslate"><span class="pre">kernel_size</span></code> <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mo stretchy="false">(</mo><mi>k</mi><mi>D</mi><mo separator="true">,</mo><mi>k</mi><mi>H</mi><mo separator="true">,</mo><mi>k</mi><mi>W</mi><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">(kD, kH, kW)</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mopen">(</span><span class="mord mathnormal" style="margin-right:0.03148em;">k</span><span class="mord mathnormal" style="margin-right:0.02778em;">D</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord mathnormal" style="margin-right:0.03148em;">k</span><span class="mord mathnormal" style="margin-right:0.08125em;">H</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord mathnormal" style="margin-right:0.13889em;">kW</span><span class="mclose">)</span></span></span></span></span>
diff --git a/main/generated/torch.nn.MaxUnpool1d.html b/main/generated/torch.nn.MaxUnpool1d.html
index 311163754b3..7a02449f414 100644
--- a/main/generated/torch.nn.MaxUnpool1d.html
+++ b/main/generated/torch.nn.MaxUnpool1d.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>MaxUnpool1d<a class="headerlink" href="#maxunpool1d" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.MaxUnpool1d">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">MaxUnpool1d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/pooling.html#MaxUnpool1d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/pooling.py#L312"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.MaxUnpool1d" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">MaxUnpool1d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/pooling.html#MaxUnpool1d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/pooling.py#L312"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.MaxUnpool1d" title="Permalink to this definition">¶</a></dt>
 <dd><p>Computes a partial inverse of <a class="reference internal" href="torch.nn.MaxPool1d.html#torch.nn.MaxPool1d" title="torch.nn.MaxPool1d"><code class="xref py py-class docutils literal notranslate"><span class="pre">MaxPool1d</span></code></a>.</p>
 <p><a class="reference internal" href="torch.nn.MaxPool1d.html#torch.nn.MaxPool1d" title="torch.nn.MaxPool1d"><code class="xref py py-class docutils literal notranslate"><span class="pre">MaxPool1d</span></code></a> is not fully invertible, since the non-maximal values are lost.</p>
 <p><a class="reference internal" href="#torch.nn.MaxUnpool1d" title="torch.nn.MaxUnpool1d"><code class="xref py py-class docutils literal notranslate"><span class="pre">MaxUnpool1d</span></code></a> takes in as input the output of <a class="reference internal" href="torch.nn.MaxPool1d.html#torch.nn.MaxPool1d" title="torch.nn.MaxPool1d"><code class="xref py py-class docutils literal notranslate"><span class="pre">MaxPool1d</span></code></a>
diff --git a/main/generated/torch.nn.MaxUnpool2d.html b/main/generated/torch.nn.MaxUnpool2d.html
index ecfd934ed40..1834b594e99 100644
--- a/main/generated/torch.nn.MaxUnpool2d.html
+++ b/main/generated/torch.nn.MaxUnpool2d.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>MaxUnpool2d<a class="headerlink" href="#maxunpool2d" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.MaxUnpool2d">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">MaxUnpool2d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/pooling.html#MaxUnpool2d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/pooling.py#L394"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.MaxUnpool2d" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">MaxUnpool2d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/pooling.html#MaxUnpool2d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/pooling.py#L394"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.MaxUnpool2d" title="Permalink to this definition">¶</a></dt>
 <dd><p>Computes a partial inverse of <a class="reference internal" href="torch.nn.MaxPool2d.html#torch.nn.MaxPool2d" title="torch.nn.MaxPool2d"><code class="xref py py-class docutils literal notranslate"><span class="pre">MaxPool2d</span></code></a>.</p>
 <p><a class="reference internal" href="torch.nn.MaxPool2d.html#torch.nn.MaxPool2d" title="torch.nn.MaxPool2d"><code class="xref py py-class docutils literal notranslate"><span class="pre">MaxPool2d</span></code></a> is not fully invertible, since the non-maximal values are lost.</p>
 <p><a class="reference internal" href="#torch.nn.MaxUnpool2d" title="torch.nn.MaxUnpool2d"><code class="xref py py-class docutils literal notranslate"><span class="pre">MaxUnpool2d</span></code></a> takes in as input the output of <a class="reference internal" href="torch.nn.MaxPool2d.html#torch.nn.MaxPool2d" title="torch.nn.MaxPool2d"><code class="xref py py-class docutils literal notranslate"><span class="pre">MaxPool2d</span></code></a>
diff --git a/main/generated/torch.nn.MaxUnpool3d.html b/main/generated/torch.nn.MaxUnpool3d.html
index d791ec8a038..804c308f65d 100644
--- a/main/generated/torch.nn.MaxUnpool3d.html
+++ b/main/generated/torch.nn.MaxUnpool3d.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>MaxUnpool3d<a class="headerlink" href="#maxunpool3d" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.MaxUnpool3d">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">MaxUnpool3d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/pooling.html#MaxUnpool3d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/pooling.py#L489"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.MaxUnpool3d" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">MaxUnpool3d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/pooling.html#MaxUnpool3d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/pooling.py#L489"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.MaxUnpool3d" title="Permalink to this definition">¶</a></dt>
 <dd><p>Computes a partial inverse of <a class="reference internal" href="torch.nn.MaxPool3d.html#torch.nn.MaxPool3d" title="torch.nn.MaxPool3d"><code class="xref py py-class docutils literal notranslate"><span class="pre">MaxPool3d</span></code></a>.</p>
 <p><a class="reference internal" href="torch.nn.MaxPool3d.html#torch.nn.MaxPool3d" title="torch.nn.MaxPool3d"><code class="xref py py-class docutils literal notranslate"><span class="pre">MaxPool3d</span></code></a> is not fully invertible, since the non-maximal values are lost.
 <a class="reference internal" href="#torch.nn.MaxUnpool3d" title="torch.nn.MaxUnpool3d"><code class="xref py py-class docutils literal notranslate"><span class="pre">MaxUnpool3d</span></code></a> takes in as input the output of <a class="reference internal" href="torch.nn.MaxPool3d.html#torch.nn.MaxPool3d" title="torch.nn.MaxPool3d"><code class="xref py py-class docutils literal notranslate"><span class="pre">MaxPool3d</span></code></a>
diff --git a/main/generated/torch.nn.Mish.html b/main/generated/torch.nn.Mish.html
index 12cd5d8757b..4a7ac14a690 100644
--- a/main/generated/torch.nn.Mish.html
+++ b/main/generated/torch.nn.Mish.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>Mish<a class="headerlink" href="#mish" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.Mish">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">Mish</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/activation.html#Mish"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/activation.py#L439"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Mish" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">Mish</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/activation.html#Mish"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/activation.py#L439"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Mish" title="Permalink to this definition">¶</a></dt>
 <dd><p>Applies the Mish function, element-wise.</p>
 <p>Mish: A Self Regularized Non-Monotonic Neural Activation Function.</p>
 <div class="math">
diff --git a/main/generated/torch.nn.Module.html b/main/generated/torch.nn.Module.html
index c352739cebc..06c82314fba 100644
--- a/main/generated/torch.nn.Module.html
+++ b/main/generated/torch.nn.Module.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>Module<a class="headerlink" href="#module" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.Module">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">Module</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#Module"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L390"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Module" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">Module</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#Module"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/module.py#L390"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Module" title="Permalink to this definition">¶</a></dt>
 <dd><p>Base class for all neural network modules.</p>
 <p>Your models should also subclass this class.</p>
 <p>Modules can also contain other Modules, allowing them to be nested in
@@ -646,7 +646,7 @@ <h1>Module<a class="headerlink" href="#module" title="Permalink to this heading"
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.Module.add_module">
-<span class="sig-name descname"><span class="pre">add_module</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">module</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#Module.add_module"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L622"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Module.add_module" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">add_module</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">module</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#Module.add_module"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/module.py#L622"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Module.add_module" title="Permalink to this definition">¶</a></dt>
 <dd><p>Add a child module to the current module.</p>
 <p>The module can be accessed as an attribute using the given name.</p>
 <dl class="field-list simple">
@@ -662,7 +662,7 @@ <h1>Module<a class="headerlink" href="#module" title="Permalink to this heading"
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.Module.apply">
-<span class="sig-name descname"><span class="pre">apply</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">fn</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#Module.apply"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L1005"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Module.apply" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">apply</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">fn</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#Module.apply"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/module.py#L1005"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Module.apply" title="Permalink to this definition">¶</a></dt>
 <dd><p>Apply <code class="docutils literal notranslate"><span class="pre">fn</span></code> recursively to every submodule (as returned by <code class="docutils literal notranslate"><span class="pre">.children()</span></code>) as well as self.</p>
 <p>Typical use includes initializing the parameters of a model
 (see also <a class="reference internal" href="../nn.init.html#nn-init-doc"><span class="std std-ref">torch.nn.init</span></a>).</p>
@@ -704,7 +704,7 @@ <h1>Module<a class="headerlink" href="#module" title="Permalink to this heading"
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.Module.bfloat16">
-<span class="sig-name descname"><span class="pre">bfloat16</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#Module.bfloat16"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L1180"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Module.bfloat16" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">bfloat16</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#Module.bfloat16"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/module.py#L1180"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Module.bfloat16" title="Permalink to this definition">¶</a></dt>
 <dd><p>Casts all floating point parameters and buffers to <code class="docutils literal notranslate"><span class="pre">bfloat16</span></code> datatype.</p>
 <div class="admonition note">
 <p class="admonition-title">Note</p>
@@ -722,7 +722,7 @@ <h1>Module<a class="headerlink" href="#module" title="Permalink to this heading"
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.Module.buffers">
-<span class="sig-name descname"><span class="pre">buffers</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">recurse</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#Module.buffers"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L2675"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Module.buffers" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">buffers</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">recurse</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#Module.buffers"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/module.py#L2675"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Module.buffers" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return an iterator over module buffers.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -748,7 +748,7 @@ <h1>Module<a class="headerlink" href="#module" title="Permalink to this heading"
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.Module.children">
-<span class="sig-name descname"><span class="pre">children</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#Module.children"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L2729"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Module.children" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">children</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#Module.children"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/module.py#L2729"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Module.children" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return an iterator over immediate children modules.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Yields</dt>
@@ -762,7 +762,7 @@ <h1>Module<a class="headerlink" href="#module" title="Permalink to this heading"
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.Module.compile">
-<span class="sig-name descname"><span class="pre">compile</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#Module.compile"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L2992"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Module.compile" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">compile</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#Module.compile"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/module.py#L2992"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Module.compile" title="Permalink to this definition">¶</a></dt>
 <dd><p>Compile this Module’s forward using <a class="reference internal" href="torch.compile.html#torch.compile" title="torch.compile"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.compile()</span></code></a>.</p>
 <p>This Module’s <cite>__call__</cite> method is compiled and all arguments are passed as-is
 to <a class="reference internal" href="torch.compile.html#torch.compile" title="torch.compile"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.compile()</span></code></a>.</p>
@@ -771,7 +771,7 @@ <h1>Module<a class="headerlink" href="#module" title="Permalink to this heading"
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.Module.cpu">
-<span class="sig-name descname"><span class="pre">cpu</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#Module.cpu"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L1122"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Module.cpu" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">cpu</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#Module.cpu"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/module.py#L1122"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Module.cpu" title="Permalink to this definition">¶</a></dt>
 <dd><p>Move all model parameters and buffers to the CPU.</p>
 <div class="admonition note">
 <p class="admonition-title">Note</p>
@@ -789,7 +789,7 @@ <h1>Module<a class="headerlink" href="#module" title="Permalink to this heading"
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.Module.cuda">
-<span class="sig-name descname"><span class="pre">cuda</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#Module.cuda"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L1046"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Module.cuda" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">cuda</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#Module.cuda"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/module.py#L1046"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Module.cuda" title="Permalink to this definition">¶</a></dt>
 <dd><p>Move all model parameters and buffers to the GPU.</p>
 <p>This also makes associated parameters and buffers different objects. So
 it should be called before constructing the optimizer if the module will
@@ -814,7 +814,7 @@ <h1>Module<a class="headerlink" href="#module" title="Permalink to this heading"
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.Module.double">
-<span class="sig-name descname"><span class="pre">double</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#Module.double"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L1158"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Module.double" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">double</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#Module.double"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/module.py#L1158"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Module.double" title="Permalink to this definition">¶</a></dt>
 <dd><p>Casts all floating point parameters and buffers to <code class="docutils literal notranslate"><span class="pre">double</span></code> datatype.</p>
 <div class="admonition note">
 <p class="admonition-title">Note</p>
@@ -832,7 +832,7 @@ <h1>Module<a class="headerlink" href="#module" title="Permalink to this heading"
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.Module.eval">
-<span class="sig-name descname"><span class="pre">eval</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#Module.eval"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L2856"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Module.eval" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">eval</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#Module.eval"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/module.py#L2856"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Module.eval" title="Permalink to this definition">¶</a></dt>
 <dd><p>Set the module in evaluation mode.</p>
 <p>This has an effect only on certain modules. See the documentation of
 particular modules for details of their behaviors in training/evaluation
@@ -853,7 +853,7 @@ <h1>Module<a class="headerlink" href="#module" title="Permalink to this heading"
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.Module.extra_repr">
-<span class="sig-name descname"><span class="pre">extra_repr</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#Module.extra_repr"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L2932"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Module.extra_repr" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">extra_repr</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#Module.extra_repr"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/module.py#L2932"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Module.extra_repr" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the extra representation of the module.</p>
 <p>To print customized extra information, you should re-implement
 this method in your own modules. Both single-line and multi-line
@@ -867,7 +867,7 @@ <h1>Module<a class="headerlink" href="#module" title="Permalink to this heading"
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.Module.float">
-<span class="sig-name descname"><span class="pre">float</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#Module.float"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L1147"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Module.float" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">float</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#Module.float"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/module.py#L1147"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Module.float" title="Permalink to this definition">¶</a></dt>
 <dd><p>Casts all floating point parameters and buffers to <code class="docutils literal notranslate"><span class="pre">float</span></code> datatype.</p>
 <div class="admonition note">
 <p class="admonition-title">Note</p>
@@ -885,7 +885,7 @@ <h1>Module<a class="headerlink" href="#module" title="Permalink to this heading"
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.Module.forward">
-<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">input</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L374"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Module.forward" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">input</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/module.py#L374"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Module.forward" title="Permalink to this definition">¶</a></dt>
 <dd><p>Define the computation performed at every call.</p>
 <p>Should be overridden by all subclasses.</p>
 <div class="admonition note">
@@ -901,7 +901,7 @@ <h1>Module<a class="headerlink" href="#module" title="Permalink to this heading"
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.Module.get_buffer">
-<span class="sig-name descname"><span class="pre">get_buffer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">target</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#Module.get_buffer"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L836"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Module.get_buffer" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">get_buffer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">target</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#Module.get_buffer"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/module.py#L836"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Module.get_buffer" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the buffer given by <code class="docutils literal notranslate"><span class="pre">target</span></code> if it exists, otherwise throw an error.</p>
 <p>See the docstring for <code class="docutils literal notranslate"><span class="pre">get_submodule</span></code> for a more detailed
 explanation of this method’s functionality as well as how to
@@ -928,7 +928,7 @@ <h1>Module<a class="headerlink" href="#module" title="Permalink to this heading"
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.Module.get_extra_state">
-<span class="sig-name descname"><span class="pre">get_extra_state</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#Module.get_extra_state"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L872"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Module.get_extra_state" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">get_extra_state</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#Module.get_extra_state"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/module.py#L872"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Module.get_extra_state" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return any extra state to include in the module’s state_dict.</p>
 <p>Implement this and a corresponding <a class="reference internal" href="#torch.nn.Module.set_extra_state" title="torch.nn.Module.set_extra_state"><code class="xref py py-func docutils literal notranslate"><span class="pre">set_extra_state()</span></code></a> for your module
 if you need to store extra state. This function is called when building the
@@ -949,7 +949,7 @@ <h1>Module<a class="headerlink" href="#module" title="Permalink to this heading"
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.Module.get_parameter">
-<span class="sig-name descname"><span class="pre">get_parameter</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">target</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#Module.get_parameter"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L800"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Module.get_parameter" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">get_parameter</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">target</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#Module.get_parameter"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/module.py#L800"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Module.get_parameter" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the parameter given by <code class="docutils literal notranslate"><span class="pre">target</span></code> if it exists, otherwise throw an error.</p>
 <p>See the docstring for <code class="docutils literal notranslate"><span class="pre">get_submodule</span></code> for a more detailed
 explanation of this method’s functionality as well as how to
@@ -976,7 +976,7 @@ <h1>Module<a class="headerlink" href="#module" title="Permalink to this heading"
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.Module.get_submodule">
-<span class="sig-name descname"><span class="pre">get_submodule</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">target</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#Module.get_submodule"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L654"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Module.get_submodule" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">get_submodule</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">target</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#Module.get_submodule"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/module.py#L654"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Module.get_submodule" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the submodule given by <code class="docutils literal notranslate"><span class="pre">target</span></code> if it exists, otherwise throw an error.</p>
 <p>For example, let’s say you have an <code class="docutils literal notranslate"><span class="pre">nn.Module</span></code> <code class="docutils literal notranslate"><span class="pre">A</span></code> that
 looks like this:</p>
@@ -1025,7 +1025,7 @@ <h1>Module<a class="headerlink" href="#module" title="Permalink to this heading"
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.Module.half">
-<span class="sig-name descname"><span class="pre">half</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#Module.half"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L1169"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Module.half" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">half</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#Module.half"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/module.py#L1169"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Module.half" title="Permalink to this definition">¶</a></dt>
 <dd><p>Casts all floating point parameters and buffers to <code class="docutils literal notranslate"><span class="pre">half</span></code> datatype.</p>
 <div class="admonition note">
 <p class="admonition-title">Note</p>
@@ -1043,7 +1043,7 @@ <h1>Module<a class="headerlink" href="#module" title="Permalink to this heading"
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.Module.ipu">
-<span class="sig-name descname"><span class="pre">ipu</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#Module.ipu"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L1065"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Module.ipu" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">ipu</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#Module.ipu"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/module.py#L1065"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Module.ipu" title="Permalink to this definition">¶</a></dt>
 <dd><p>Move all model parameters and buffers to the IPU.</p>
 <p>This also makes associated parameters and buffers different objects. So
 it should be called before constructing the optimizer if the module will
@@ -1068,7 +1068,7 @@ <h1>Module<a class="headerlink" href="#module" title="Permalink to this heading"
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.Module.load_state_dict">
-<span class="sig-name descname"><span class="pre">load_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state_dict</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">strict</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">assign</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#Module.load_state_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L2483"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Module.load_state_dict" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">load_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state_dict</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">strict</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">assign</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#Module.load_state_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/module.py#L2483"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Module.load_state_dict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Copy parameters and buffers from <a class="reference internal" href="#torch.nn.Module.state_dict" title="torch.nn.Module.state_dict"><code class="xref py py-attr docutils literal notranslate"><span class="pre">state_dict</span></code></a> into this module and its descendants.</p>
 <p>If <code class="xref py py-attr docutils literal notranslate"><span class="pre">strict</span></code> is <code class="docutils literal notranslate"><span class="pre">True</span></code>, then
 the keys of <a class="reference internal" href="#torch.nn.Module.state_dict" title="torch.nn.Module.state_dict"><code class="xref py py-attr docutils literal notranslate"><span class="pre">state_dict</span></code></a> must exactly match the keys returned
@@ -1123,7 +1123,7 @@ <h1>Module<a class="headerlink" href="#module" title="Permalink to this heading"
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.Module.modules">
-<span class="sig-name descname"><span class="pre">modules</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#Module.modules"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L2758"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Module.modules" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">modules</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#Module.modules"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/module.py#L2758"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Module.modules" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return an iterator over all modules in the network.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Yields</dt>
@@ -1155,7 +1155,7 @@ <h1>Module<a class="headerlink" href="#module" title="Permalink to this heading"
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.Module.mtia">
-<span class="sig-name descname"><span class="pre">mtia</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#Module.mtia"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L1103"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Module.mtia" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">mtia</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#Module.mtia"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/module.py#L1103"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Module.mtia" title="Permalink to this definition">¶</a></dt>
 <dd><p>Move all model parameters and buffers to the MTIA.</p>
 <p>This also makes associated parameters and buffers different objects. So
 it should be called before constructing the optimizer if the module will
@@ -1180,7 +1180,7 @@ <h1>Module<a class="headerlink" href="#module" title="Permalink to this heading"
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.Module.named_buffers">
-<span class="sig-name descname"><span class="pre">named_buffers</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">prefix</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">recurse</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">remove_duplicate</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#Module.named_buffers"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L2698"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Module.named_buffers" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">named_buffers</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">prefix</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">recurse</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">remove_duplicate</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#Module.named_buffers"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/module.py#L2698"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Module.named_buffers" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return an iterator over module buffers, yielding both the name of the buffer as well as the buffer itself.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -1209,7 +1209,7 @@ <h1>Module<a class="headerlink" href="#module" title="Permalink to this heading"
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.Module.named_children">
-<span class="sig-name descname"><span class="pre">named_children</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#Module.named_children"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L2738"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Module.named_children" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">named_children</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#Module.named_children"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/module.py#L2738"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Module.named_children" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return an iterator over immediate children modules, yielding both the name of the module as well as the module itself.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Yields</dt>
@@ -1229,7 +1229,7 @@ <h1>Module<a class="headerlink" href="#module" title="Permalink to this heading"
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.Module.named_modules">
-<span class="sig-name descname"><span class="pre">named_modules</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">memo</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prefix</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">remove_duplicate</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#Module.named_modules"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L2785"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Module.named_modules" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">named_modules</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">memo</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prefix</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">remove_duplicate</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#Module.named_modules"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/module.py#L2785"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Module.named_modules" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return an iterator over all modules in the network, yielding both the name of the module as well as the module itself.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -1266,7 +1266,7 @@ <h1>Module<a class="headerlink" href="#module" title="Permalink to this heading"
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.Module.named_parameters">
-<span class="sig-name descname"><span class="pre">named_parameters</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">prefix</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">recurse</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">remove_duplicate</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#Module.named_parameters"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L2643"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Module.named_parameters" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">named_parameters</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">prefix</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">recurse</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">remove_duplicate</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#Module.named_parameters"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/module.py#L2643"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Module.named_parameters" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return an iterator over module parameters, yielding both the name of the parameter as well as the parameter itself.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -1296,7 +1296,7 @@ <h1>Module<a class="headerlink" href="#module" title="Permalink to this heading"
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.Module.parameters">
-<span class="sig-name descname"><span class="pre">parameters</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">recurse</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#Module.parameters"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L2618"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Module.parameters" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">parameters</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">recurse</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#Module.parameters"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/module.py#L2618"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Module.parameters" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return an iterator over module parameters.</p>
 <p>This is typically passed to an optimizer.</p>
 <dl class="field-list simple">
@@ -1323,7 +1323,7 @@ <h1>Module<a class="headerlink" href="#module" title="Permalink to this heading"
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.Module.register_backward_hook">
-<span class="sig-name descname"><span class="pre">register_backward_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#Module.register_backward_hook"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L1404"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Module.register_backward_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_backward_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#Module.register_backward_hook"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/module.py#L1404"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Module.register_backward_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register a backward hook on the module.</p>
 <p>This function is deprecated in favor of <a class="reference internal" href="#torch.nn.Module.register_full_backward_hook" title="torch.nn.Module.register_full_backward_hook"><code class="xref py py-meth docutils literal notranslate"><span class="pre">register_full_backward_hook()</span></code></a> and
 the behavior of this function will change in future versions.</p>
@@ -1340,7 +1340,7 @@ <h1>Module<a class="headerlink" href="#module" title="Permalink to this heading"
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.Module.register_buffer">
-<span class="sig-name descname"><span class="pre">register_buffer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">persistent</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#Module.register_buffer"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L510"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Module.register_buffer" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_buffer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">persistent</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#Module.register_buffer"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/module.py#L510"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Module.register_buffer" title="Permalink to this definition">¶</a></dt>
 <dd><p>Add a buffer to the module.</p>
 <p>This is typically used to register a buffer that should not to be
 considered a model parameter. For example, BatchNorm’s <code class="docutils literal notranslate"><span class="pre">running_mean</span></code>
@@ -1372,7 +1372,7 @@ <h1>Module<a class="headerlink" href="#module" title="Permalink to this heading"
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.Module.register_forward_hook">
-<span class="sig-name descname"><span class="pre">register_forward_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">with_kwargs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">always_call</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#Module.register_forward_hook"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L1656"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Module.register_forward_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_forward_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">with_kwargs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">always_call</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#Module.register_forward_hook"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/module.py#L1656"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Module.register_forward_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register a forward hook on the module.</p>
 <p>The hook will be called every time after <a class="reference internal" href="#torch.nn.Module.forward" title="torch.nn.Module.forward"><code class="xref py py-func docutils literal notranslate"><span class="pre">forward()</span></code></a> has computed an output.</p>
 <p>If <code class="docutils literal notranslate"><span class="pre">with_kwargs</span></code> is <code class="docutils literal notranslate"><span class="pre">False</span></code> or not specified, the input contains only
@@ -1423,7 +1423,7 @@ <h1>Module<a class="headerlink" href="#module" title="Permalink to this heading"
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.Module.register_forward_pre_hook">
-<span class="sig-name descname"><span class="pre">register_forward_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">with_kwargs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#Module.register_forward_pre_hook"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L1590"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Module.register_forward_pre_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_forward_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">with_kwargs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#Module.register_forward_pre_hook"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/module.py#L1590"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Module.register_forward_pre_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register a forward pre-hook on the module.</p>
 <p>The hook will be called every time before <a class="reference internal" href="#torch.nn.Module.forward" title="torch.nn.Module.forward"><code class="xref py py-func docutils literal notranslate"><span class="pre">forward()</span></code></a> is invoked.</p>
 <p>If <code class="docutils literal notranslate"><span class="pre">with_kwargs</span></code> is false or not specified, the input contains only
@@ -1473,7 +1473,7 @@ <h1>Module<a class="headerlink" href="#module" title="Permalink to this heading"
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.Module.register_full_backward_hook">
-<span class="sig-name descname"><span class="pre">register_full_backward_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#Module.register_full_backward_hook"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L1430"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Module.register_full_backward_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_full_backward_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#Module.register_full_backward_hook"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/module.py#L1430"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Module.register_full_backward_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register a backward hook on the module.</p>
 <p>The hook will be called every time the gradients with respect to a module
 are computed, i.e. the hook will execute if and only if the gradients with
@@ -1524,7 +1524,7 @@ <h1>Module<a class="headerlink" href="#module" title="Permalink to this heading"
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.Module.register_full_backward_pre_hook">
-<span class="sig-name descname"><span class="pre">register_full_backward_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#Module.register_full_backward_pre_hook"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L1355"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Module.register_full_backward_pre_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_full_backward_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#Module.register_full_backward_pre_hook"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/module.py#L1355"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Module.register_full_backward_pre_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register a backward pre-hook on the module.</p>
 <p>The hook will be called every time the gradients for the module are computed.
 The hook should have the following signature:</p>
@@ -1570,7 +1570,7 @@ <h1>Module<a class="headerlink" href="#module" title="Permalink to this heading"
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.Module.register_load_state_dict_post_hook">
-<span class="sig-name descname"><span class="pre">register_load_state_dict_post_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#Module.register_load_state_dict_post_hook"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L2273"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Module.register_load_state_dict_post_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_load_state_dict_post_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#Module.register_load_state_dict_post_hook"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/module.py#L2273"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Module.register_load_state_dict_post_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register a post-hook to be run after module’s <code class="xref py py-meth docutils literal notranslate"><span class="pre">load_state_dict()</span></code> is called.</p>
 <dl class="simple">
 <dt>It should have the following signature::</dt><dd><p>hook(module, incompatible_keys) -&gt; None</p>
@@ -1600,7 +1600,7 @@ <h1>Module<a class="headerlink" href="#module" title="Permalink to this heading"
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.Module.register_load_state_dict_pre_hook">
-<span class="sig-name descname"><span class="pre">register_load_state_dict_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#Module.register_load_state_dict_pre_hook"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L2261"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Module.register_load_state_dict_pre_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_load_state_dict_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#Module.register_load_state_dict_pre_hook"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/module.py#L2261"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Module.register_load_state_dict_pre_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register a pre-hook to be run before module’s <code class="xref py py-meth docutils literal notranslate"><span class="pre">load_state_dict()</span></code> is called.</p>
 <dl class="simple">
 <dt>It should have the following signature::</dt><dd><p>hook(module, state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs) -&gt; None  # noqa: B950</p>
@@ -1616,7 +1616,7 @@ <h1>Module<a class="headerlink" href="#module" title="Permalink to this heading"
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.Module.register_module">
-<span class="sig-name descname"><span class="pre">register_module</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">module</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#Module.register_module"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L650"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Module.register_module" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_module</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">module</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#Module.register_module"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/module.py#L650"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Module.register_module" title="Permalink to this definition">¶</a></dt>
 <dd><p>Alias for <a class="reference internal" href="#torch.nn.Module.add_module" title="torch.nn.Module.add_module"><code class="xref py py-func docutils literal notranslate"><span class="pre">add_module()</span></code></a>.</p>
 <dl class="field-list simple">
 </dl>
@@ -1624,7 +1624,7 @@ <h1>Module<a class="headerlink" href="#module" title="Permalink to this heading"
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.Module.register_parameter">
-<span class="sig-name descname"><span class="pre">register_parameter</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">param</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#Module.register_parameter"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L572"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Module.register_parameter" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_parameter</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">param</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#Module.register_parameter"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/module.py#L572"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Module.register_parameter" title="Permalink to this definition">¶</a></dt>
 <dd><p>Add a parameter to the module.</p>
 <p>The parameter can be accessed as an attribute using given name.</p>
 <dl class="field-list simple">
@@ -1643,7 +1643,7 @@ <h1>Module<a class="headerlink" href="#module" title="Permalink to this heading"
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.Module.register_state_dict_post_hook">
-<span class="sig-name descname"><span class="pre">register_state_dict_post_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#Module.register_state_dict_post_hook"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L2071"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Module.register_state_dict_post_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_state_dict_post_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#Module.register_state_dict_post_hook"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/module.py#L2071"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Module.register_state_dict_post_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register a post-hook for the <a class="reference internal" href="#torch.nn.Module.state_dict" title="torch.nn.Module.state_dict"><code class="xref py py-meth docutils literal notranslate"><span class="pre">state_dict()</span></code></a> method.</p>
 <dl class="simple">
 <dt>It should have the following signature::</dt><dd><p>hook(module, state_dict, prefix, local_metadata) -&gt; None</p>
@@ -1654,7 +1654,7 @@ <h1>Module<a class="headerlink" href="#module" title="Permalink to this heading"
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.Module.register_state_dict_pre_hook">
-<span class="sig-name descname"><span class="pre">register_state_dict_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#Module.register_state_dict_pre_hook"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L2095"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Module.register_state_dict_pre_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_state_dict_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#Module.register_state_dict_pre_hook"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/module.py#L2095"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Module.register_state_dict_pre_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register a pre-hook for the <a class="reference internal" href="#torch.nn.Module.state_dict" title="torch.nn.Module.state_dict"><code class="xref py py-meth docutils literal notranslate"><span class="pre">state_dict()</span></code></a> method.</p>
 <dl class="simple">
 <dt>It should have the following signature::</dt><dd><p>hook(module, prefix, keep_vars) -&gt; None</p>
@@ -1666,7 +1666,7 @@ <h1>Module<a class="headerlink" href="#module" title="Permalink to this heading"
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.Module.requires_grad_">
-<span class="sig-name descname"><span class="pre">requires_grad_</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">requires_grad</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#Module.requires_grad_"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L2874"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Module.requires_grad_" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">requires_grad_</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">requires_grad</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#Module.requires_grad_"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/module.py#L2874"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Module.requires_grad_" title="Permalink to this definition">¶</a></dt>
 <dd><p>Change if autograd should record operations on parameters in this module.</p>
 <p>This method sets the parameters’ <code class="xref py py-attr docutils literal notranslate"><span class="pre">requires_grad</span></code> attributes
 in-place.</p>
@@ -1690,7 +1690,7 @@ <h1>Module<a class="headerlink" href="#module" title="Permalink to this heading"
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.Module.set_extra_state">
-<span class="sig-name descname"><span class="pre">set_extra_state</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#Module.set_extra_state"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L893"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Module.set_extra_state" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">set_extra_state</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#Module.set_extra_state"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/module.py#L893"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Module.set_extra_state" title="Permalink to this definition">¶</a></dt>
 <dd><p>Set extra state contained in the loaded <cite>state_dict</cite>.</p>
 <p>This function is called from <a class="reference internal" href="#torch.nn.Module.load_state_dict" title="torch.nn.Module.load_state_dict"><code class="xref py py-func docutils literal notranslate"><span class="pre">load_state_dict()</span></code></a> to handle any extra state
 found within the <cite>state_dict</cite>. Implement this function and a corresponding
@@ -1705,7 +1705,7 @@ <h1>Module<a class="headerlink" href="#module" title="Permalink to this heading"
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.Module.set_submodule">
-<span class="sig-name descname"><span class="pre">set_submodule</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">target</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">strict</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#Module.set_submodule"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L719"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Module.set_submodule" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">set_submodule</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">target</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">strict</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#Module.set_submodule"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/module.py#L719"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Module.set_submodule" title="Permalink to this definition">¶</a></dt>
 <dd><p>Set the submodule given by <code class="docutils literal notranslate"><span class="pre">target</span></code> if it exists, otherwise throw an error.</p>
 <div class="admonition note">
 <p class="admonition-title">Note</p>
@@ -1763,7 +1763,7 @@ <h1>Module<a class="headerlink" href="#module" title="Permalink to this heading"
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.Module.share_memory">
-<span class="sig-name descname"><span class="pre">share_memory</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#Module.share_memory"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L2925"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Module.share_memory" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">share_memory</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#Module.share_memory"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/module.py#L2925"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Module.share_memory" title="Permalink to this definition">¶</a></dt>
 <dd><p>See <a class="reference internal" href="torch.Tensor.share_memory_.html#torch.Tensor.share_memory_" title="torch.Tensor.share_memory_"><code class="xref py py-meth docutils literal notranslate"><span class="pre">torch.Tensor.share_memory_()</span></code></a>.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -1774,7 +1774,7 @@ <h1>Module<a class="headerlink" href="#module" title="Permalink to this heading"
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.Module.state_dict">
-<span class="sig-name descname"><span class="pre">state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">destination</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">T_destination</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prefix</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">''</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">keep_vars</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">T_destination</span></span></span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#Module.state_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L2152"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Module.state_dict" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">destination</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">T_destination</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prefix</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">''</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">keep_vars</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">T_destination</span></span></span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#Module.state_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/module.py#L2152"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Module.state_dict" title="Permalink to this definition">¶</a></dt>
 <dt class="sig sig-object py">
 <span class="sig-name descname"><span class="pre">state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prefix</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">''</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">keep_vars</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.13)"><span class="pre">dict</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.13)"><span class="pre">Any</span></a><span class="p"><span class="pre">]</span></span></span></span></dt>
 <dd><p>Return a dictionary containing references to the whole state of the module.</p>
@@ -1829,7 +1829,7 @@ <h1>Module<a class="headerlink" href="#module" title="Permalink to this heading"
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.Module.to">
-<span class="sig-name descname"><span class="pre">to</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Optional" title="(in Python v3.13)"><span class="pre">Optional</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Union" title="(in Python v3.13)"><span class="pre">Union</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="../tensor_attributes.html#torch.device" title="torch.device"><span class="pre">device</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><span class="pre">int</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">...</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Optional" title="(in Python v3.13)"><span class="pre">Optional</span></a><span class="p"><span class="pre">[</span></span><a class="reference internal" href="../tensor_attributes.html#torch.dtype" title="torch.dtype"><span class="pre">dtype</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">...</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">non_blocking</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">...</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">Self</span></span></span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#Module.to"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L1226"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Module.to" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">to</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Optional" title="(in Python v3.13)"><span class="pre">Optional</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Union" title="(in Python v3.13)"><span class="pre">Union</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="../tensor_attributes.html#torch.device" title="torch.device"><span class="pre">device</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><span class="pre">int</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">...</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Optional" title="(in Python v3.13)"><span class="pre">Optional</span></a><span class="p"><span class="pre">[</span></span><a class="reference internal" href="../tensor_attributes.html#torch.dtype" title="torch.dtype"><span class="pre">dtype</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">...</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">non_blocking</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">...</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">Self</span></span></span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#Module.to"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/module.py#L1226"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Module.to" title="Permalink to this definition">¶</a></dt>
 <dt class="sig sig-object py">
 <span class="sig-name descname"><span class="pre">to</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="../tensor_attributes.html#torch.dtype" title="torch.dtype"><span class="pre">dtype</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">non_blocking</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">...</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">Self</span></span></span></dt>
 <dt class="sig sig-object py">
@@ -1838,22 +1838,22 @@ <h1>Module<a class="headerlink" href="#module" title="Permalink to this heading"
 <p>This can be called as</p>
 <dl class="py function">
 <dt class="sig sig-object py">
-<span class="sig-name descname"><span class="pre">to</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">non_blocking</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#Module.to"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L1226"><span class="viewcode-link"><span class="pre">[source]</span></span></a></dt>
+<span class="sig-name descname"><span class="pre">to</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">non_blocking</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#Module.to"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/module.py#L1226"><span class="viewcode-link"><span class="pre">[source]</span></span></a></dt>
 <dd></dd></dl>
 
 <dl class="py function">
 <dt class="sig sig-object py">
-<span class="sig-name descname"><span class="pre">to</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dtype</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">non_blocking</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#Module.to"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L1226"><span class="viewcode-link"><span class="pre">[source]</span></span></a></dt>
+<span class="sig-name descname"><span class="pre">to</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dtype</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">non_blocking</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#Module.to"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/module.py#L1226"><span class="viewcode-link"><span class="pre">[source]</span></span></a></dt>
 <dd></dd></dl>
 
 <dl class="py function">
 <dt class="sig sig-object py">
-<span class="sig-name descname"><span class="pre">to</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">non_blocking</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#Module.to"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L1226"><span class="viewcode-link"><span class="pre">[source]</span></span></a></dt>
+<span class="sig-name descname"><span class="pre">to</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">non_blocking</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#Module.to"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/module.py#L1226"><span class="viewcode-link"><span class="pre">[source]</span></span></a></dt>
 <dd></dd></dl>
 
 <dl class="py function">
 <dt class="sig sig-object py">
-<span class="sig-name descname"><span class="pre">to</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">memory_format</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.channels_last</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#Module.to"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L1226"><span class="viewcode-link"><span class="pre">[source]</span></span></a></dt>
+<span class="sig-name descname"><span class="pre">to</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">memory_format</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.channels_last</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#Module.to"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/module.py#L1226"><span class="viewcode-link"><span class="pre">[source]</span></span></a></dt>
 <dd></dd></dl>
 
 <p>Its signature is similar to <a class="reference internal" href="torch.Tensor.to.html#torch.Tensor.to" title="torch.Tensor.to"><code class="xref py py-meth docutils literal notranslate"><span class="pre">torch.Tensor.to()</span></code></a>, but only accepts
@@ -1932,7 +1932,7 @@ <h1>Module<a class="headerlink" href="#module" title="Permalink to this heading"
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.Module.to_empty">
-<span class="sig-name descname"><span class="pre">to_empty</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">recurse</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#Module.to_empty"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L1191"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Module.to_empty" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">to_empty</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">recurse</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#Module.to_empty"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/module.py#L1191"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Module.to_empty" title="Permalink to this definition">¶</a></dt>
 <dd><p>Move the parameters and buffers to the specified device without copying storage.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -1954,7 +1954,7 @@ <h1>Module<a class="headerlink" href="#module" title="Permalink to this heading"
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.Module.train">
-<span class="sig-name descname"><span class="pre">train</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#Module.train"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L2834"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Module.train" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">train</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#Module.train"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/module.py#L2834"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Module.train" title="Permalink to this definition">¶</a></dt>
 <dd><p>Set the module in training mode.</p>
 <p>This has an effect only on certain modules. See the documentation of
 particular modules for details of their behaviors in training/evaluation
@@ -1976,7 +1976,7 @@ <h1>Module<a class="headerlink" href="#module" title="Permalink to this heading"
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.Module.type">
-<span class="sig-name descname"><span class="pre">type</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dst_type</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#Module.type"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L1133"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Module.type" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">type</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dst_type</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#Module.type"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/module.py#L1133"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Module.type" title="Permalink to this definition">¶</a></dt>
 <dd><p>Casts all parameters and buffers to <code class="xref py py-attr docutils literal notranslate"><span class="pre">dst_type</span></code>.</p>
 <div class="admonition note">
 <p class="admonition-title">Note</p>
@@ -1997,7 +1997,7 @@ <h1>Module<a class="headerlink" href="#module" title="Permalink to this heading"
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.Module.xpu">
-<span class="sig-name descname"><span class="pre">xpu</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#Module.xpu"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L1084"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Module.xpu" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">xpu</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#Module.xpu"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/module.py#L1084"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Module.xpu" title="Permalink to this definition">¶</a></dt>
 <dd><p>Move all model parameters and buffers to the XPU.</p>
 <p>This also makes associated parameters and buffers different objects. So
 it should be called before constructing optimizer if the module will
@@ -2022,7 +2022,7 @@ <h1>Module<a class="headerlink" href="#module" title="Permalink to this heading"
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.Module.zero_grad">
-<span class="sig-name descname"><span class="pre">zero_grad</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">set_to_none</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#Module.zero_grad"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L2897"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Module.zero_grad" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">zero_grad</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">set_to_none</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#Module.zero_grad"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/module.py#L2897"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Module.zero_grad" title="Permalink to this definition">¶</a></dt>
 <dd><p>Reset gradients of all model parameters.</p>
 <p>See similar function under <a class="reference internal" href="../optim.html#torch.optim.Optimizer" title="torch.optim.Optimizer"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.optim.Optimizer</span></code></a> for more context.</p>
 <dl class="field-list simple">
diff --git a/main/generated/torch.nn.ModuleDict.html b/main/generated/torch.nn.ModuleDict.html
index 6aa59521300..f59b1fecdb9 100644
--- a/main/generated/torch.nn.ModuleDict.html
+++ b/main/generated/torch.nn.ModuleDict.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>ModuleDict<a class="headerlink" href="#moduledict" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.ModuleDict">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">ModuleDict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">modules</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/container.html#ModuleDict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/container.py#L439"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.ModuleDict" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">ModuleDict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">modules</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/container.html#ModuleDict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/container.py#L439"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.ModuleDict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Holds submodules in a dictionary.</p>
 <p><a class="reference internal" href="#torch.nn.ModuleDict" title="torch.nn.ModuleDict"><code class="xref py py-class docutils literal notranslate"><span class="pre">ModuleDict</span></code></a> can be indexed like a regular Python dictionary,
 but modules it contains are properly registered, and will be visible by all
@@ -655,7 +655,7 @@ <h1>ModuleDict<a class="headerlink" href="#moduledict" title="Permalink to this
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.ModuleDict.clear">
-<span class="sig-name descname"><span class="pre">clear</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/container.html#ModuleDict.clear"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/container.py#L512"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.ModuleDict.clear" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">clear</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/container.html#ModuleDict.clear"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/container.py#L512"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.ModuleDict.clear" title="Permalink to this definition">¶</a></dt>
 <dd><p>Remove all items from the ModuleDict.</p>
 <dl class="field-list simple">
 </dl>
@@ -663,7 +663,7 @@ <h1>ModuleDict<a class="headerlink" href="#moduledict" title="Permalink to this
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.ModuleDict.items">
-<span class="sig-name descname"><span class="pre">items</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/container.html#ModuleDict.items"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/container.py#L531"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.ModuleDict.items" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">items</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/container.html#ModuleDict.items"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/container.py#L531"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.ModuleDict.items" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return an iterable of the ModuleDict key/value pairs.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -674,7 +674,7 @@ <h1>ModuleDict<a class="headerlink" href="#moduledict" title="Permalink to this
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.ModuleDict.keys">
-<span class="sig-name descname"><span class="pre">keys</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/container.html#ModuleDict.keys"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/container.py#L526"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.ModuleDict.keys" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">keys</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/container.html#ModuleDict.keys"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/container.py#L526"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.ModuleDict.keys" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return an iterable of the ModuleDict keys.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -685,7 +685,7 @@ <h1>ModuleDict<a class="headerlink" href="#moduledict" title="Permalink to this
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.ModuleDict.pop">
-<span class="sig-name descname"><span class="pre">pop</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">key</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/container.html#ModuleDict.pop"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/container.py#L516"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.ModuleDict.pop" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">pop</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">key</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/container.html#ModuleDict.pop"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/container.py#L516"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.ModuleDict.pop" title="Permalink to this definition">¶</a></dt>
 <dd><p>Remove key from the ModuleDict and return its module.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -699,7 +699,7 @@ <h1>ModuleDict<a class="headerlink" href="#moduledict" title="Permalink to this
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.ModuleDict.update">
-<span class="sig-name descname"><span class="pre">update</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">modules</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/container.html#ModuleDict.update"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/container.py#L541"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.ModuleDict.update" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">update</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">modules</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/container.html#ModuleDict.update"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/container.py#L541"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.ModuleDict.update" title="Permalink to this definition">¶</a></dt>
 <dd><p>Update the <a class="reference internal" href="#torch.nn.ModuleDict" title="torch.nn.ModuleDict"><code class="xref py py-class docutils literal notranslate"><span class="pre">ModuleDict</span></code></a> with key-value pairs from a mapping, overwriting existing keys.</p>
 <div class="admonition note">
 <p class="admonition-title">Note</p>
@@ -716,7 +716,7 @@ <h1>ModuleDict<a class="headerlink" href="#moduledict" title="Permalink to this
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.ModuleDict.values">
-<span class="sig-name descname"><span class="pre">values</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/container.html#ModuleDict.values"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/container.py#L536"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.ModuleDict.values" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">values</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/container.html#ModuleDict.values"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/container.py#L536"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.ModuleDict.values" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return an iterable of the ModuleDict values.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
diff --git a/main/generated/torch.nn.ModuleList.html b/main/generated/torch.nn.ModuleList.html
index 808b67d715e..625fce7d93e 100644
--- a/main/generated/torch.nn.ModuleList.html
+++ b/main/generated/torch.nn.ModuleList.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>ModuleList<a class="headerlink" href="#modulelist" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.ModuleList">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">ModuleList</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">modules</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/container.html#ModuleList"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/container.py#L271"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.ModuleList" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">ModuleList</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">modules</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/container.html#ModuleList"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/container.py#L271"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.ModuleList" title="Permalink to this definition">¶</a></dt>
 <dd><p>Holds submodules in a list.</p>
 <p><a class="reference internal" href="#torch.nn.ModuleList" title="torch.nn.ModuleList"><code class="xref py py-class docutils literal notranslate"><span class="pre">ModuleList</span></code></a> can be indexed like a regular Python list, but
 modules it contains are properly registered, and will be visible by all
@@ -637,7 +637,7 @@ <h1>ModuleList<a class="headerlink" href="#modulelist" title="Permalink to this
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.ModuleList.append">
-<span class="sig-name descname"><span class="pre">append</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/container.html#ModuleList.append"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/container.py#L406"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.ModuleList.append" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">append</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/container.html#ModuleList.append"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/container.py#L406"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.ModuleList.append" title="Permalink to this definition">¶</a></dt>
 <dd><p>Append a given module to the end of the list.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -651,7 +651,7 @@ <h1>ModuleList<a class="headerlink" href="#modulelist" title="Permalink to this
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.ModuleList.extend">
-<span class="sig-name descname"><span class="pre">extend</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">modules</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/container.html#ModuleList.extend"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/container.py#L420"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.ModuleList.extend" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">extend</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">modules</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/container.html#ModuleList.extend"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/container.py#L420"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.ModuleList.extend" title="Permalink to this definition">¶</a></dt>
 <dd><p>Append modules from a Python iterable to the end of the list.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -665,7 +665,7 @@ <h1>ModuleList<a class="headerlink" href="#modulelist" title="Permalink to this
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.ModuleList.insert">
-<span class="sig-name descname"><span class="pre">insert</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">index</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">module</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/container.html#ModuleList.insert"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/container.py#L395"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.ModuleList.insert" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">insert</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">index</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">module</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/container.html#ModuleList.insert"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/container.py#L395"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.ModuleList.insert" title="Permalink to this definition">¶</a></dt>
 <dd><p>Insert a given module before a given index in the list.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.nn.MultiLabelMarginLoss.html b/main/generated/torch.nn.MultiLabelMarginLoss.html
index b6e9f3bffb6..1f70a8b65d3 100644
--- a/main/generated/torch.nn.MultiLabelMarginLoss.html
+++ b/main/generated/torch.nn.MultiLabelMarginLoss.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>MultiLabelMarginLoss<a class="headerlink" href="#multilabelmarginloss" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.MultiLabelMarginLoss">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">MultiLabelMarginLoss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">size_average</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduce</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mean'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/loss.html#MultiLabelMarginLoss"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/loss.py#L898"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.MultiLabelMarginLoss" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">MultiLabelMarginLoss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">size_average</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduce</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mean'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/loss.html#MultiLabelMarginLoss"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/loss.py#L898"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.MultiLabelMarginLoss" title="Permalink to this definition">¶</a></dt>
 <dd><p>Creates a criterion that optimizes a multi-class multi-classification
 hinge loss (margin-based loss) between input <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>x</mi></mrow><annotation encoding="application/x-tex">x</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.4306em;"></span><span class="mord mathnormal">x</span></span></span></span></span> (a 2D mini-batch <cite>Tensor</cite>)
 and output <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>y</mi></mrow><annotation encoding="application/x-tex">y</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.625em;vertical-align:-0.1944em;"></span><span class="mord mathnormal" style="margin-right:0.03588em;">y</span></span></span></span></span> (which is a 2D <cite>Tensor</cite> of target class indices).
diff --git a/main/generated/torch.nn.MultiLabelSoftMarginLoss.html b/main/generated/torch.nn.MultiLabelSoftMarginLoss.html
index 6b2dfcc6e85..ab0ea1e963f 100644
--- a/main/generated/torch.nn.MultiLabelSoftMarginLoss.html
+++ b/main/generated/torch.nn.MultiLabelSoftMarginLoss.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>MultiLabelSoftMarginLoss<a class="headerlink" href="#multilabelsoftmarginloss" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.MultiLabelSoftMarginLoss">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">MultiLabelSoftMarginLoss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">size_average</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduce</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mean'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/loss.html#MultiLabelSoftMarginLoss"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/loss.py#L1305"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.MultiLabelSoftMarginLoss" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">MultiLabelSoftMarginLoss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">size_average</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduce</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mean'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/loss.html#MultiLabelSoftMarginLoss"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/loss.py#L1305"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.MultiLabelSoftMarginLoss" title="Permalink to this definition">¶</a></dt>
 <dd><p>Creates a criterion that optimizes a multi-label one-versus-all
 loss based on max-entropy, between input <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>x</mi></mrow><annotation encoding="application/x-tex">x</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.4306em;"></span><span class="mord mathnormal">x</span></span></span></span></span> and target <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>y</mi></mrow><annotation encoding="application/x-tex">y</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.625em;vertical-align:-0.1944em;"></span><span class="mord mathnormal" style="margin-right:0.03588em;">y</span></span></span></span></span> of size
 <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mo stretchy="false">(</mo><mi>N</mi><mo separator="true">,</mo><mi>C</mi><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">(N, C)</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mopen">(</span><span class="mord mathnormal" style="margin-right:0.10903em;">N</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord mathnormal" style="margin-right:0.07153em;">C</span><span class="mclose">)</span></span></span></span></span>.
diff --git a/main/generated/torch.nn.MultiMarginLoss.html b/main/generated/torch.nn.MultiMarginLoss.html
index ad446098a54..38e5429942b 100644
--- a/main/generated/torch.nn.MultiMarginLoss.html
+++ b/main/generated/torch.nn.MultiMarginLoss.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>MultiMarginLoss<a class="headerlink" href="#multimarginloss" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.MultiMarginLoss">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">MultiMarginLoss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">p</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">margin</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">size_average</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduce</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mean'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/loss.html#MultiMarginLoss"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/loss.py#L1495"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.MultiMarginLoss" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">MultiMarginLoss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">p</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">margin</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">size_average</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduce</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mean'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/loss.html#MultiMarginLoss"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/loss.py#L1495"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.MultiMarginLoss" title="Permalink to this definition">¶</a></dt>
 <dd><p>Creates a criterion that optimizes a multi-class classification hinge
 loss (margin-based loss) between input <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>x</mi></mrow><annotation encoding="application/x-tex">x</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.4306em;"></span><span class="mord mathnormal">x</span></span></span></span></span> (a 2D mini-batch <cite>Tensor</cite>) and
 output <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>y</mi></mrow><annotation encoding="application/x-tex">y</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.625em;vertical-align:-0.1944em;"></span><span class="mord mathnormal" style="margin-right:0.03588em;">y</span></span></span></span></span> (which is a 1D tensor of target class indices,
diff --git a/main/generated/torch.nn.MultiheadAttention.html b/main/generated/torch.nn.MultiheadAttention.html
index f4ebe5b3492..de3f80a8133 100644
--- a/main/generated/torch.nn.MultiheadAttention.html
+++ b/main/generated/torch.nn.MultiheadAttention.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>MultiheadAttention<a class="headerlink" href="#multiheadattention" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.MultiheadAttention">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">MultiheadAttention</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">embed_dim</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_heads</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dropout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">add_bias_kv</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">add_zero_attn</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kdim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">vdim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">batch_first</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/activation.html#MultiheadAttention"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/activation.py#L973"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.MultiheadAttention" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">MultiheadAttention</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">embed_dim</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_heads</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dropout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">add_bias_kv</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">add_zero_attn</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kdim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">vdim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">batch_first</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/activation.html#MultiheadAttention"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/activation.py#L973"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.MultiheadAttention" title="Permalink to this definition">¶</a></dt>
 <dd><p>Allows the model to jointly attend to information from different representation subspaces.</p>
 <div class="admonition note">
 <p class="admonition-title">Note</p>
@@ -675,7 +675,7 @@ <h1>MultiheadAttention<a class="headerlink" href="#multiheadattention" title="Pe
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.MultiheadAttention.forward">
-<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">query</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">key</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">value</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">key_padding_mask</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">need_weights</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">attn_mask</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">average_attn_weights</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">is_causal</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/activation.html#MultiheadAttention.forward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/activation.py#L1139"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.MultiheadAttention.forward" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">query</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">key</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">value</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">key_padding_mask</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">need_weights</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">attn_mask</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">average_attn_weights</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">is_causal</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/activation.html#MultiheadAttention.forward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/activation.py#L1139"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.MultiheadAttention.forward" title="Permalink to this definition">¶</a></dt>
 <dd><p>Compute attention outputs using query, key, and value embeddings.</p>
 <blockquote>
 <div><p>Supports optional parameters for padding, masks and attention weights.</p>
@@ -751,7 +751,7 @@ <h1>MultiheadAttention<a class="headerlink" href="#multiheadattention" title="Pe
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.MultiheadAttention.merge_masks">
-<span class="sig-name descname"><span class="pre">merge_masks</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">attn_mask</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">key_padding_mask</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">query</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/activation.html#MultiheadAttention.merge_masks"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/activation.py#L1399"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.MultiheadAttention.merge_masks" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">merge_masks</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">attn_mask</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">key_padding_mask</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">query</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/activation.html#MultiheadAttention.merge_masks"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/activation.py#L1399"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.MultiheadAttention.merge_masks" title="Permalink to this definition">¶</a></dt>
 <dd><p>Determine mask type and combine masks if necessary.</p>
 <p>If only one mask is provided, that mask
 and the corresponding mask type will be returned. If both masks are provided, they will be both
diff --git a/main/generated/torch.nn.NLLLoss.html b/main/generated/torch.nn.NLLLoss.html
index 7b2ece2b936..d7e7ddcffbf 100644
--- a/main/generated/torch.nn.NLLLoss.html
+++ b/main/generated/torch.nn.NLLLoss.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>NLLLoss<a class="headerlink" href="#nllloss" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.NLLLoss">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">NLLLoss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">size_average</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ignore_index</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-100</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduce</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mean'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/loss.html#NLLLoss"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/loss.py#L131"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.NLLLoss" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">NLLLoss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">size_average</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ignore_index</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-100</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduce</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mean'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/loss.html#NLLLoss"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/loss.py#L131"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.NLLLoss" title="Permalink to this definition">¶</a></dt>
 <dd><p>The negative log likelihood loss. It is useful to train a classification
 problem with <cite>C</cite> classes.</p>
 <p>If provided, the optional argument <code class="xref py py-attr docutils literal notranslate"><span class="pre">weight</span></code> should be a 1D Tensor assigning
diff --git a/main/generated/torch.nn.PReLU.html b/main/generated/torch.nn.PReLU.html
index 62f53036d20..d06c8eb75c1 100644
--- a/main/generated/torch.nn.PReLU.html
+++ b/main/generated/torch.nn.PReLU.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>PReLU<a class="headerlink" href="#prelu" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.PReLU">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">PReLU</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">num_parameters</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">init</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.25</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/activation.html#PReLU"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/activation.py#L1450"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.PReLU" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">PReLU</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">num_parameters</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">init</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.25</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/activation.html#PReLU"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/activation.py#L1450"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.PReLU" title="Permalink to this definition">¶</a></dt>
 <dd><p>Applies the element-wise PReLU function.</p>
 <div class="math">
 <span class="katex-display"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML" display="block"><semantics><mrow><mtext>PReLU</mtext><mo stretchy="false">(</mo><mi>x</mi><mo stretchy="false">)</mo><mo>=</mo><mi>max</mi><mo>⁡</mo><mo stretchy="false">(</mo><mn>0</mn><mo separator="true">,</mo><mi>x</mi><mo stretchy="false">)</mo><mo>+</mo><mi>a</mi><mo>∗</mo><mi>min</mi><mo>⁡</mo><mo stretchy="false">(</mo><mn>0</mn><mo separator="true">,</mo><mi>x</mi><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">\text{PReLU}(x) = \max(0,x) + a * \min(0,x)
diff --git a/main/generated/torch.nn.PairwiseDistance.html b/main/generated/torch.nn.PairwiseDistance.html
index 689f6a05e54..52f96032955 100644
--- a/main/generated/torch.nn.PairwiseDistance.html
+++ b/main/generated/torch.nn.PairwiseDistance.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>PairwiseDistance<a class="headerlink" href="#pairwisedistance" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.PairwiseDistance">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">PairwiseDistance</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">p</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-06</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">keepdim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/distance.html#PairwiseDistance"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/distance.py#L10"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.PairwiseDistance" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">PairwiseDistance</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">p</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-06</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">keepdim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/distance.html#PairwiseDistance"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/distance.py#L10"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.PairwiseDistance" title="Permalink to this definition">¶</a></dt>
 <dd><p>Computes the pairwise distance between input vectors, or between columns of input matrices.</p>
 <p>Distances are computed using <code class="docutils literal notranslate"><span class="pre">p</span></code>-norm, with constant <code class="docutils literal notranslate"><span class="pre">eps</span></code> added to avoid division by zero
 if <code class="docutils literal notranslate"><span class="pre">p</span></code> is negative, i.e.:</p>
diff --git a/main/generated/torch.nn.ParameterDict.html b/main/generated/torch.nn.ParameterDict.html
index aa72563cbf2..62ce5e8e696 100644
--- a/main/generated/torch.nn.ParameterDict.html
+++ b/main/generated/torch.nn.ParameterDict.html
@@ -300,7 +300,7 @@
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     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>ParameterDict<a class="headerlink" href="#parameterdict" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.ParameterDict">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">ParameterDict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">parameters</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/container.html#ParameterDict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/container.py#L725"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.ParameterDict" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">ParameterDict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">parameters</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/container.html#ParameterDict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/container.py#L725"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.ParameterDict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Holds parameters in a dictionary.</p>
 <p>ParameterDict can be indexed like a regular Python dictionary, but Parameters it
 contains are properly registered, and will be visible by all Module methods.
@@ -648,7 +648,7 @@ <h1>ParameterDict<a class="headerlink" href="#parameterdict" title="Permalink to
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.ParameterDict.clear">
-<span class="sig-name descname"><span class="pre">clear</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/container.html#ParameterDict.clear"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/container.py#L833"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.ParameterDict.clear" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">clear</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/container.html#ParameterDict.clear"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/container.py#L833"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.ParameterDict.clear" title="Permalink to this definition">¶</a></dt>
 <dd><p>Remove all items from the ParameterDict.</p>
 <dl class="field-list simple">
 </dl>
@@ -656,7 +656,7 @@ <h1>ParameterDict<a class="headerlink" href="#parameterdict" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.ParameterDict.copy">
-<span class="sig-name descname"><span class="pre">copy</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/container.html#ParameterDict.copy"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/container.py#L809"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.ParameterDict.copy" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">copy</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/container.html#ParameterDict.copy"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/container.py#L809"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.ParameterDict.copy" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return a copy of this <a class="reference internal" href="#torch.nn.ParameterDict" title="torch.nn.ParameterDict"><code class="xref py py-class docutils literal notranslate"><span class="pre">ParameterDict</span></code></a> instance.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -667,7 +667,7 @@ <h1>ParameterDict<a class="headerlink" href="#parameterdict" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.ParameterDict.fromkeys">
-<span class="sig-name descname"><span class="pre">fromkeys</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">keys</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">default</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/container.html#ParameterDict.fromkeys"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/container.py#L866"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.ParameterDict.fromkeys" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">fromkeys</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">keys</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">default</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/container.html#ParameterDict.fromkeys"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/container.py#L866"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.ParameterDict.fromkeys" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return a new ParameterDict with the keys provided.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -684,7 +684,7 @@ <h1>ParameterDict<a class="headerlink" href="#parameterdict" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.ParameterDict.get">
-<span class="sig-name descname"><span class="pre">get</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">key</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">default</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/container.html#ParameterDict.get"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/container.py#L857"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.ParameterDict.get" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">get</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">key</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">default</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/container.html#ParameterDict.get"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/container.py#L857"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.ParameterDict.get" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the parameter associated with key if present. Otherwise return default if provided, None if not.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -701,7 +701,7 @@ <h1>ParameterDict<a class="headerlink" href="#parameterdict" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.ParameterDict.items">
-<span class="sig-name descname"><span class="pre">items</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/container.html#ParameterDict.items"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/container.py#L881"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.ParameterDict.items" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">items</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/container.html#ParameterDict.items"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/container.py#L881"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.ParameterDict.items" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return an iterable of the ParameterDict key/value pairs.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -712,7 +712,7 @@ <h1>ParameterDict<a class="headerlink" href="#parameterdict" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.ParameterDict.keys">
-<span class="sig-name descname"><span class="pre">keys</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/container.html#ParameterDict.keys"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/container.py#L877"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.ParameterDict.keys" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">keys</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/container.html#ParameterDict.keys"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/container.py#L877"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.ParameterDict.keys" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return an iterable of the ParameterDict keys.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -723,7 +723,7 @@ <h1>ParameterDict<a class="headerlink" href="#parameterdict" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.ParameterDict.pop">
-<span class="sig-name descname"><span class="pre">pop</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">key</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/container.html#ParameterDict.pop"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/container.py#L838"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.ParameterDict.pop" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">pop</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">key</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/container.html#ParameterDict.pop"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/container.py#L838"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.ParameterDict.pop" title="Permalink to this definition">¶</a></dt>
 <dd><p>Remove key from the ParameterDict and return its parameter.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -737,7 +737,7 @@ <h1>ParameterDict<a class="headerlink" href="#parameterdict" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.ParameterDict.popitem">
-<span class="sig-name descname"><span class="pre">popitem</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/container.html#ParameterDict.popitem"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/container.py#L848"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.ParameterDict.popitem" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">popitem</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/container.html#ParameterDict.popitem"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/container.py#L848"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.ParameterDict.popitem" title="Permalink to this definition">¶</a></dt>
 <dd><p>Remove and return the last inserted <cite>(key, parameter)</cite> pair from the ParameterDict.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -748,7 +748,7 @@ <h1>ParameterDict<a class="headerlink" href="#parameterdict" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.ParameterDict.setdefault">
-<span class="sig-name descname"><span class="pre">setdefault</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">key</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">default</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/container.html#ParameterDict.setdefault"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/container.py#L818"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.ParameterDict.setdefault" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">setdefault</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">key</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">default</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/container.html#ParameterDict.setdefault"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/container.py#L818"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.ParameterDict.setdefault" title="Permalink to this definition">¶</a></dt>
 <dd><p>Set the default for a key in the Parameterdict.</p>
 <p>If key is in the ParameterDict, return its value.
 If not, insert <cite>key</cite> with a parameter <cite>default</cite> and return <cite>default</cite>.
@@ -768,7 +768,7 @@ <h1>ParameterDict<a class="headerlink" href="#parameterdict" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.ParameterDict.update">
-<span class="sig-name descname"><span class="pre">update</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">parameters</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/container.html#ParameterDict.update"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/container.py#L889"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.ParameterDict.update" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">update</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">parameters</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/container.html#ParameterDict.update"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/container.py#L889"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.ParameterDict.update" title="Permalink to this definition">¶</a></dt>
 <dd><p>Update the <a class="reference internal" href="#torch.nn.ParameterDict" title="torch.nn.ParameterDict"><code class="xref py py-class docutils literal notranslate"><span class="pre">ParameterDict</span></code></a> with key-value pairs from <code class="docutils literal notranslate"><span class="pre">parameters</span></code>, overwriting existing keys.</p>
 <div class="admonition note">
 <p class="admonition-title">Note</p>
@@ -786,7 +786,7 @@ <h1>ParameterDict<a class="headerlink" href="#parameterdict" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.ParameterDict.values">
-<span class="sig-name descname"><span class="pre">values</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/container.html#ParameterDict.values"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/container.py#L885"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.ParameterDict.values" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">values</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/container.html#ParameterDict.values"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/container.py#L885"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.ParameterDict.values" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return an iterable of the ParameterDict values.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
diff --git a/main/generated/torch.nn.ParameterList.html b/main/generated/torch.nn.ParameterList.html
index 9ebbb873aca..93526ddf06b 100644
--- a/main/generated/torch.nn.ParameterList.html
+++ b/main/generated/torch.nn.ParameterList.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
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-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
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     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>ParameterList<a class="headerlink" href="#parameterlist" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.ParameterList">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">ParameterList</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">values</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/container.html#ParameterList"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/container.py#L581"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.ParameterList" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">ParameterList</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">values</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/container.html#ParameterList"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/container.py#L581"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.ParameterList" title="Permalink to this definition">¶</a></dt>
 <dd><p>Holds parameters in a list.</p>
 <p><a class="reference internal" href="#torch.nn.ParameterList" title="torch.nn.ParameterList"><code class="xref py py-class docutils literal notranslate"><span class="pre">ParameterList</span></code></a> can be used like a regular Python
 list, but Tensors that are <code class="xref py py-class docutils literal notranslate"><span class="pre">Parameter</span></code> are properly registered,
@@ -640,7 +640,7 @@ <h1>ParameterList<a class="headerlink" href="#parameterlist" title="Permalink to
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.ParameterList.append">
-<span class="sig-name descname"><span class="pre">append</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/container.html#ParameterList.append"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/container.py#L668"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.ParameterList.append" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">append</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/container.html#ParameterList.append"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/container.py#L668"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.ParameterList.append" title="Permalink to this definition">¶</a></dt>
 <dd><p>Append a given value at the end of the list.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -654,7 +654,7 @@ <h1>ParameterList<a class="headerlink" href="#parameterlist" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.ParameterList.extend">
-<span class="sig-name descname"><span class="pre">extend</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">values</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/container.html#ParameterList.extend"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/container.py#L679"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.ParameterList.extend" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">extend</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">values</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/container.html#ParameterList.extend"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/container.py#L679"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.ParameterList.extend" title="Permalink to this definition">¶</a></dt>
 <dd><p>Append values from a Python iterable to the end of the list.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.nn.PixelShuffle.html b/main/generated/torch.nn.PixelShuffle.html
index 073241623a2..be10bb93ed6 100644
--- a/main/generated/torch.nn.PixelShuffle.html
+++ b/main/generated/torch.nn.PixelShuffle.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>PixelShuffle<a class="headerlink" href="#pixelshuffle" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.PixelShuffle">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">PixelShuffle</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">upscale_factor</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/pixelshuffle.html#PixelShuffle"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/pixelshuffle.py#L10"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.PixelShuffle" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">PixelShuffle</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">upscale_factor</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/pixelshuffle.html#PixelShuffle"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/pixelshuffle.py#L10"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.PixelShuffle" title="Permalink to this definition">¶</a></dt>
 <dd><p>Rearrange elements in a tensor according to an upscaling factor.</p>
 <p>Rearranges elements in a tensor of shape <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mo stretchy="false">(</mo><mo>∗</mo><mo separator="true">,</mo><mi>C</mi><mo>×</mo><msup><mi>r</mi><mn>2</mn></msup><mo separator="true">,</mo><mi>H</mi><mo separator="true">,</mo><mi>W</mi><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">(*, C \times r^2, H, W)</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mopen">(</span><span class="mord">∗</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord mathnormal" style="margin-right:0.07153em;">C</span><span class="mspace" style="margin-right:0.2222em;"></span><span class="mbin">×</span><span class="mspace" style="margin-right:0.2222em;"></span></span><span class="base"><span class="strut" style="height:1.0641em;vertical-align:-0.25em;"></span><span class="mord"><span class="mord mathnormal" style="margin-right:0.02778em;">r</span><span class="msupsub"><span class="vlist-t"><span class="vlist-r"><span class="vlist" style="height:0.8141em;"><span style="top:-3.063em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight">2</span></span></span></span></span></span></span></span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord mathnormal" style="margin-right:0.08125em;">H</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord mathnormal" style="margin-right:0.13889em;">W</span><span class="mclose">)</span></span></span></span></span>
 to a tensor of shape <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mo stretchy="false">(</mo><mo>∗</mo><mo separator="true">,</mo><mi>C</mi><mo separator="true">,</mo><mi>H</mi><mo>×</mo><mi>r</mi><mo separator="true">,</mo><mi>W</mi><mo>×</mo><mi>r</mi><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">(*, C, H \times r, W \times r)</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mopen">(</span><span class="mord">∗</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord mathnormal" style="margin-right:0.07153em;">C</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord mathnormal" style="margin-right:0.08125em;">H</span><span class="mspace" style="margin-right:0.2222em;"></span><span class="mbin">×</span><span class="mspace" style="margin-right:0.2222em;"></span></span><span class="base"><span class="strut" style="height:0.8778em;vertical-align:-0.1944em;"></span><span class="mord mathnormal" style="margin-right:0.02778em;">r</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord mathnormal" style="margin-right:0.13889em;">W</span><span class="mspace" style="margin-right:0.2222em;"></span><span class="mbin">×</span><span class="mspace" style="margin-right:0.2222em;"></span></span><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mord mathnormal" style="margin-right:0.02778em;">r</span><span class="mclose">)</span></span></span></span></span>, where r is an upscale factor.</p>
diff --git a/main/generated/torch.nn.PixelUnshuffle.html b/main/generated/torch.nn.PixelUnshuffle.html
index 3ee6033ec2f..200cf1eaeeb 100644
--- a/main/generated/torch.nn.PixelUnshuffle.html
+++ b/main/generated/torch.nn.PixelUnshuffle.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>PixelUnshuffle<a class="headerlink" href="#pixelunshuffle" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.PixelUnshuffle">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">PixelUnshuffle</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">downscale_factor</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/pixelshuffle.html#PixelUnshuffle"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/pixelshuffle.py#L65"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.PixelUnshuffle" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">PixelUnshuffle</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">downscale_factor</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/pixelshuffle.html#PixelUnshuffle"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/pixelshuffle.py#L65"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.PixelUnshuffle" title="Permalink to this definition">¶</a></dt>
 <dd><p>Reverse the PixelShuffle operation.</p>
 <p>Reverses the <a class="reference internal" href="torch.nn.PixelShuffle.html#torch.nn.PixelShuffle" title="torch.nn.PixelShuffle"><code class="xref py py-class docutils literal notranslate"><span class="pre">PixelShuffle</span></code></a> operation by rearranging elements
 in a tensor of shape <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mo stretchy="false">(</mo><mo>∗</mo><mo separator="true">,</mo><mi>C</mi><mo separator="true">,</mo><mi>H</mi><mo>×</mo><mi>r</mi><mo separator="true">,</mo><mi>W</mi><mo>×</mo><mi>r</mi><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">(*, C, H \times r, W \times r)</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mopen">(</span><span class="mord">∗</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord mathnormal" style="margin-right:0.07153em;">C</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord mathnormal" style="margin-right:0.08125em;">H</span><span class="mspace" style="margin-right:0.2222em;"></span><span class="mbin">×</span><span class="mspace" style="margin-right:0.2222em;"></span></span><span class="base"><span class="strut" style="height:0.8778em;vertical-align:-0.1944em;"></span><span class="mord mathnormal" style="margin-right:0.02778em;">r</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord mathnormal" style="margin-right:0.13889em;">W</span><span class="mspace" style="margin-right:0.2222em;"></span><span class="mbin">×</span><span class="mspace" style="margin-right:0.2222em;"></span></span><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mord mathnormal" style="margin-right:0.02778em;">r</span><span class="mclose">)</span></span></span></span></span> to a tensor of shape
diff --git a/main/generated/torch.nn.PoissonNLLLoss.html b/main/generated/torch.nn.PoissonNLLLoss.html
index 427104ac3cd..b04cb984480 100644
--- a/main/generated/torch.nn.PoissonNLLLoss.html
+++ b/main/generated/torch.nn.PoissonNLLLoss.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>PoissonNLLLoss<a class="headerlink" href="#poissonnllloss" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.PoissonNLLLoss">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">PoissonNLLLoss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">log_input</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">full</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">size_average</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-08</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduce</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mean'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/loss.html#PoissonNLLLoss"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/loss.py#L278"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.PoissonNLLLoss" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">PoissonNLLLoss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">log_input</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">full</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">size_average</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-08</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduce</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mean'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/loss.html#PoissonNLLLoss"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/loss.py#L278"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.PoissonNLLLoss" title="Permalink to this definition">¶</a></dt>
 <dd><p>Negative log likelihood loss with Poisson distribution of target.</p>
 <p>The loss can be described as:</p>
 <div class="math">
diff --git a/main/generated/torch.nn.RMSNorm.html b/main/generated/torch.nn.RMSNorm.html
index 8c0ef55958a..7e6cf3e6a6d 100644
--- a/main/generated/torch.nn.RMSNorm.html
+++ b/main/generated/torch.nn.RMSNorm.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>RMSNorm<a class="headerlink" href="#rmsnorm" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.RMSNorm">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">RMSNorm</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">normalized_shape</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">elementwise_affine</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/normalization.html#RMSNorm"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/normalization.py#L321"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.RMSNorm" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">RMSNorm</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">normalized_shape</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">elementwise_affine</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/normalization.html#RMSNorm"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/normalization.py#L321"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.RMSNorm" title="Permalink to this definition">¶</a></dt>
 <dd><p>Applies Root Mean Square Layer Normalization over a mini-batch of inputs.</p>
 <p>This layer implements the operation as described in
 the paper <a class="reference external" href="https://arxiv.org/pdf/1910.07467.pdf">Root Mean Square Layer Normalization</a></p>
@@ -661,7 +661,7 @@ <h1>RMSNorm<a class="headerlink" href="#rmsnorm" title="Permalink to this headin
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.RMSNorm.extra_repr">
-<span class="sig-name descname"><span class="pre">extra_repr</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/normalization.html#RMSNorm.extra_repr"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/normalization.py#L403"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.RMSNorm.extra_repr" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">extra_repr</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/normalization.html#RMSNorm.extra_repr"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/normalization.py#L403"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.RMSNorm.extra_repr" title="Permalink to this definition">¶</a></dt>
 <dd><p>Extra information about the module.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -672,7 +672,7 @@ <h1>RMSNorm<a class="headerlink" href="#rmsnorm" title="Permalink to this headin
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.RMSNorm.forward">
-<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/normalization.html#RMSNorm.forward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/normalization.py#L397"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.RMSNorm.forward" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/normalization.html#RMSNorm.forward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/normalization.py#L397"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.RMSNorm.forward" title="Permalink to this definition">¶</a></dt>
 <dd><p>Runs forward pass.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -683,7 +683,7 @@ <h1>RMSNorm<a class="headerlink" href="#rmsnorm" title="Permalink to this headin
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.RMSNorm.reset_parameters">
-<span class="sig-name descname"><span class="pre">reset_parameters</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/normalization.html#RMSNorm.reset_parameters"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/normalization.py#L390"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.RMSNorm.reset_parameters" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">reset_parameters</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/normalization.html#RMSNorm.reset_parameters"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/normalization.py#L390"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.RMSNorm.reset_parameters" title="Permalink to this definition">¶</a></dt>
 <dd><p>Resets parameters based on their initialization used in __init__.</p>
 <dl class="field-list simple">
 </dl>
diff --git a/main/generated/torch.nn.RNN.html b/main/generated/torch.nn.RNN.html
index 8caf3c6bd7d..04d5ecaa6b9 100644
--- a/main/generated/torch.nn.RNN.html
+++ b/main/generated/torch.nn.RNN.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>RNN<a class="headerlink" href="#rnn" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.RNN">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">RNN</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">hidden_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_layers</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">nonlinearity</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'tanh'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">batch_first</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dropout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bidirectional</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/rnn.html#RNN"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/rnn.py#L469"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.RNN" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">RNN</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">hidden_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_layers</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">nonlinearity</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'tanh'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">batch_first</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dropout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bidirectional</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/rnn.html#RNN"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/rnn.py#L469"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.RNN" title="Permalink to this definition">¶</a></dt>
 <dd><p>Apply a multi-layer Elman RNN with <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>tanh</mi><mo>⁡</mo></mrow><annotation encoding="application/x-tex">\tanh</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.6944em;"></span><span class="mop">tanh</span></span></span></span></span> or <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mtext>ReLU</mtext></mrow><annotation encoding="application/x-tex">\text{ReLU}</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.6833em;"></span><span class="mord text"><span class="mord">ReLU</span></span></span></span></span></span>
 non-linearity to an input sequence. For each element in the input sequence,
 each layer computes the following function:</p>
diff --git a/main/generated/torch.nn.RNNBase.html b/main/generated/torch.nn.RNNBase.html
index 4f0f230bf16..c4de902fb4d 100644
--- a/main/generated/torch.nn.RNNBase.html
+++ b/main/generated/torch.nn.RNNBase.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>RNNBase<a class="headerlink" href="#rnnbase" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.RNNBase">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">RNNBase</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mode</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">hidden_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_layers</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">batch_first</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dropout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bidirectional</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">proj_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/rnn.html#RNNBase"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/rnn.py#L48"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.RNNBase" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">RNNBase</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mode</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">hidden_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_layers</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">batch_first</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dropout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bidirectional</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">proj_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/rnn.html#RNNBase"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/rnn.py#L48"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.RNNBase" title="Permalink to this definition">¶</a></dt>
 <dd><p>Base class for RNN modules (RNN, LSTM, GRU).</p>
 <p>Implements aspects of RNNs shared by the RNN, LSTM, and GRU classes, such as module initialization
 and utility methods for parameter storage management.</p>
@@ -628,7 +628,7 @@ <h1>RNNBase<a class="headerlink" href="#rnnbase" title="Permalink to this headin
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.RNNBase.flatten_parameters">
-<span class="sig-name descname"><span class="pre">flatten_parameters</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/rnn.html#RNNBase.flatten_parameters"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/rnn.py#L224"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.RNNBase.flatten_parameters" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">flatten_parameters</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/rnn.html#RNNBase.flatten_parameters"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/rnn.py#L224"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.RNNBase.flatten_parameters" title="Permalink to this definition">¶</a></dt>
 <dd><p>Reset parameter data pointer so that they can use faster code paths.</p>
 <p>Right now, this works only if the module is on the GPU and cuDNN is enabled.
 Otherwise, it’s a no-op.</p>
diff --git a/main/generated/torch.nn.RNNCell.html b/main/generated/torch.nn.RNNCell.html
index 941d76fc544..12430e8261a 100644
--- a/main/generated/torch.nn.RNNCell.html
+++ b/main/generated/torch.nn.RNNCell.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>RNNCell<a class="headerlink" href="#rnncell" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.RNNCell">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">RNNCell</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">hidden_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">nonlinearity</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'tanh'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/rnn.html#RNNCell"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/rnn.py#L1491"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.RNNCell" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">RNNCell</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">hidden_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">nonlinearity</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'tanh'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/rnn.html#RNNCell"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/rnn.py#L1491"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.RNNCell" title="Permalink to this definition">¶</a></dt>
 <dd><p>An Elman RNN cell with tanh or ReLU non-linearity.</p>
 <div class="math">
 <span class="katex-display"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML" display="block"><semantics><mrow><msup><mi>h</mi><mo mathvariant="normal" lspace="0em" rspace="0em">′</mo></msup><mo>=</mo><mi>tanh</mi><mo>⁡</mo><mo stretchy="false">(</mo><msub><mi>W</mi><mrow><mi>i</mi><mi>h</mi></mrow></msub><mi>x</mi><mo>+</mo><msub><mi>b</mi><mrow><mi>i</mi><mi>h</mi></mrow></msub><mo>+</mo><msub><mi>W</mi><mrow><mi>h</mi><mi>h</mi></mrow></msub><mi>h</mi><mo>+</mo><msub><mi>b</mi><mrow><mi>h</mi><mi>h</mi></mrow></msub><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">h&#x27; = \tanh(W_{ih} x + b_{ih}  +  W_{hh} h + b_{hh})</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.8019em;"></span><span class="mord"><span class="mord mathnormal">h</span><span class="msupsub"><span class="vlist-t"><span class="vlist-r"><span class="vlist" style="height:0.8019em;"><span style="top:-3.113em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mord mtight">′</span></span></span></span></span></span></span></span></span><span class="mspace" style="margin-right:0.2778em;"></span><span class="mrel">=</span><span class="mspace" style="margin-right:0.2778em;"></span></span><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mop">tanh</span><span class="mopen">(</span><span class="mord"><span class="mord mathnormal" style="margin-right:0.13889em;">W</span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.3361em;"><span style="top:-2.55em;margin-left:-0.1389em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mord mathnormal mtight">ih</span></span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.15em;"><span></span></span></span></span></span></span><span class="mord mathnormal">x</span><span class="mspace" style="margin-right:0.2222em;"></span><span class="mbin">+</span><span class="mspace" style="margin-right:0.2222em;"></span></span><span class="base"><span class="strut" style="height:0.8444em;vertical-align:-0.15em;"></span><span class="mord"><span class="mord mathnormal">b</span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.3361em;"><span style="top:-2.55em;margin-left:0em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mord mathnormal mtight">ih</span></span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.15em;"><span></span></span></span></span></span></span><span class="mspace" style="margin-right:0.2222em;"></span><span class="mbin">+</span><span class="mspace" style="margin-right:0.2222em;"></span></span><span class="base"><span class="strut" style="height:0.8444em;vertical-align:-0.15em;"></span><span class="mord"><span class="mord mathnormal" style="margin-right:0.13889em;">W</span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.3361em;"><span style="top:-2.55em;margin-left:-0.1389em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mord mathnormal mtight">hh</span></span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.15em;"><span></span></span></span></span></span></span><span class="mord mathnormal">h</span><span class="mspace" style="margin-right:0.2222em;"></span><span class="mbin">+</span><span class="mspace" style="margin-right:0.2222em;"></span></span><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mord"><span class="mord mathnormal">b</span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.3361em;"><span style="top:-2.55em;margin-left:0em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mord mathnormal mtight">hh</span></span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.15em;"><span></span></span></span></span></span></span><span class="mclose">)</span></span></span></span></span></div><p>If <code class="xref py py-attr docutils literal notranslate"><span class="pre">nonlinearity</span></code> is <cite>‘relu’</cite>, then ReLU is used in place of tanh.</p>
diff --git a/main/generated/torch.nn.RReLU.html b/main/generated/torch.nn.RReLU.html
index 7708816a39a..4ad6acc8522 100644
--- a/main/generated/torch.nn.RReLU.html
+++ b/main/generated/torch.nn.RReLU.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>RReLU<a class="headerlink" href="#rrelu" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.RReLU">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">RReLU</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">lower</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.125</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">upper</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.3333333333333333</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/activation.html#RReLU"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/activation.py#L140"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.RReLU" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">RReLU</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">lower</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.125</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">upper</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.3333333333333333</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/activation.html#RReLU"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/activation.py#L140"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.RReLU" title="Permalink to this definition">¶</a></dt>
 <dd><p>Applies the randomized leaky rectified linear unit function, element-wise.</p>
 <p>Method described in the paper:
 <a class="reference external" href="https://arxiv.org/abs/1505.00853">Empirical Evaluation of Rectified Activations in Convolutional Network</a>.</p>
diff --git a/main/generated/torch.nn.ReLU.html b/main/generated/torch.nn.ReLU.html
index ac92da75688..24d7ff4ba7f 100644
--- a/main/generated/torch.nn.ReLU.html
+++ b/main/generated/torch.nn.ReLU.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>ReLU<a class="headerlink" href="#relu" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.ReLU">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">ReLU</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/activation.html#ReLU"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/activation.py#L97"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.ReLU" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">ReLU</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/activation.html#ReLU"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/activation.py#L97"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.ReLU" title="Permalink to this definition">¶</a></dt>
 <dd><p>Applies the rectified linear unit function element-wise.</p>
 <p><span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mtext>ReLU</mtext><mo stretchy="false">(</mo><mi>x</mi><mo stretchy="false">)</mo><mo>=</mo><mo stretchy="false">(</mo><mi>x</mi><msup><mo stretchy="false">)</mo><mo>+</mo></msup><mo>=</mo><mi>max</mi><mo>⁡</mo><mo stretchy="false">(</mo><mn>0</mn><mo separator="true">,</mo><mi>x</mi><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">\text{ReLU}(x) = (x)^+ = \max(0, x)</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mord text"><span class="mord">ReLU</span></span><span class="mopen">(</span><span class="mord mathnormal">x</span><span class="mclose">)</span><span class="mspace" style="margin-right:0.2778em;"></span><span class="mrel">=</span><span class="mspace" style="margin-right:0.2778em;"></span></span><span class="base"><span class="strut" style="height:1.0213em;vertical-align:-0.25em;"></span><span class="mopen">(</span><span class="mord mathnormal">x</span><span class="mclose"><span class="mclose">)</span><span class="msupsub"><span class="vlist-t"><span class="vlist-r"><span class="vlist" style="height:0.7713em;"><span style="top:-3.063em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mbin mtight">+</span></span></span></span></span></span></span></span><span class="mspace" style="margin-right:0.2778em;"></span><span class="mrel">=</span><span class="mspace" style="margin-right:0.2778em;"></span></span><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mop">max</span><span class="mopen">(</span><span class="mord">0</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord mathnormal">x</span><span class="mclose">)</span></span></span></span></span></p>
 <dl class="field-list simple">
diff --git a/main/generated/torch.nn.ReLU6.html b/main/generated/torch.nn.ReLU6.html
index 089ceb9f1e5..0fec1e22154 100644
--- a/main/generated/torch.nn.ReLU6.html
+++ b/main/generated/torch.nn.ReLU6.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>ReLU6<a class="headerlink" href="#relu6" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.ReLU6">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">ReLU6</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/activation.html#ReLU6"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/activation.py#L276"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.ReLU6" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">ReLU6</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/activation.html#ReLU6"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/activation.py#L276"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.ReLU6" title="Permalink to this definition">¶</a></dt>
 <dd><p>Applies the ReLU6 function element-wise.</p>
 <div class="math">
 <span class="katex-display"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML" display="block"><semantics><mrow><mtext>ReLU6</mtext><mo stretchy="false">(</mo><mi>x</mi><mo stretchy="false">)</mo><mo>=</mo><mi>min</mi><mo>⁡</mo><mo stretchy="false">(</mo><mi>max</mi><mo>⁡</mo><mo stretchy="false">(</mo><mn>0</mn><mo separator="true">,</mo><mi>x</mi><mo stretchy="false">)</mo><mo separator="true">,</mo><mn>6</mn><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">\text{ReLU6}(x) = \min(\max(0,x), 6)
diff --git a/main/generated/torch.nn.ReflectionPad1d.html b/main/generated/torch.nn.ReflectionPad1d.html
index 94ff268625d..c366e8ed60f 100644
--- a/main/generated/torch.nn.ReflectionPad1d.html
+++ b/main/generated/torch.nn.ReflectionPad1d.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>ReflectionPad1d<a class="headerlink" href="#reflectionpad1d" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.ReflectionPad1d">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">ReflectionPad1d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">padding</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/padding.html#ReflectionPad1d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/padding.py#L374"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.ReflectionPad1d" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">ReflectionPad1d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">padding</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/padding.html#ReflectionPad1d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/padding.py#L374"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.ReflectionPad1d" title="Permalink to this definition">¶</a></dt>
 <dd><p>Pads the input tensor using the reflection of the input boundary.</p>
 <p>For <cite>N</cite>-dimensional padding, use <a class="reference internal" href="torch.nn.functional.pad.html#torch.nn.functional.pad" title="torch.nn.functional.pad"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.nn.functional.pad()</span></code></a>.</p>
 <dl class="field-list simple">
diff --git a/main/generated/torch.nn.ReflectionPad2d.html b/main/generated/torch.nn.ReflectionPad2d.html
index ae728e50166..a949770f46c 100644
--- a/main/generated/torch.nn.ReflectionPad2d.html
+++ b/main/generated/torch.nn.ReflectionPad2d.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>ReflectionPad2d<a class="headerlink" href="#reflectionpad2d" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.ReflectionPad2d">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">ReflectionPad2d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">padding</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/padding.html#ReflectionPad2d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/padding.py#L415"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.ReflectionPad2d" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">ReflectionPad2d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">padding</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/padding.html#ReflectionPad2d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/padding.py#L415"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.ReflectionPad2d" title="Permalink to this definition">¶</a></dt>
 <dd><p>Pads the input tensor using the reflection of the input boundary.</p>
 <p>For <cite>N</cite>-dimensional padding, use <a class="reference internal" href="torch.nn.functional.pad.html#torch.nn.functional.pad" title="torch.nn.functional.pad"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.nn.functional.pad()</span></code></a>.</p>
 <dl class="field-list simple">
diff --git a/main/generated/torch.nn.ReflectionPad3d.html b/main/generated/torch.nn.ReflectionPad3d.html
index 2770742ca21..d1d34bd16de 100644
--- a/main/generated/torch.nn.ReflectionPad3d.html
+++ b/main/generated/torch.nn.ReflectionPad3d.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>ReflectionPad3d<a class="headerlink" href="#reflectionpad3d" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.ReflectionPad3d">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">ReflectionPad3d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">padding</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/padding.html#ReflectionPad3d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/padding.py#L468"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.ReflectionPad3d" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">ReflectionPad3d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">padding</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/padding.html#ReflectionPad3d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/padding.py#L468"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.ReflectionPad3d" title="Permalink to this definition">¶</a></dt>
 <dd><p>Pads the input tensor using the reflection of the input boundary.</p>
 <p>For <cite>N</cite>-dimensional padding, use <a class="reference internal" href="torch.nn.functional.pad.html#torch.nn.functional.pad" title="torch.nn.functional.pad"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.nn.functional.pad()</span></code></a>.</p>
 <dl class="field-list simple">
diff --git a/main/generated/torch.nn.ReplicationPad1d.html b/main/generated/torch.nn.ReplicationPad1d.html
index 9eea53fceae..13dcdc20419 100644
--- a/main/generated/torch.nn.ReplicationPad1d.html
+++ b/main/generated/torch.nn.ReplicationPad1d.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>ReplicationPad1d<a class="headerlink" href="#replicationpad1d" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.ReplicationPad1d">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">ReplicationPad1d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">padding</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/padding.html#ReplicationPad1d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/padding.py#L533"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.ReplicationPad1d" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">ReplicationPad1d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">padding</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/padding.html#ReplicationPad1d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/padding.py#L533"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.ReplicationPad1d" title="Permalink to this definition">¶</a></dt>
 <dd><p>Pads the input tensor using replication of the input boundary.</p>
 <p>For <cite>N</cite>-dimensional padding, use <a class="reference internal" href="torch.nn.functional.pad.html#torch.nn.functional.pad" title="torch.nn.functional.pad"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.nn.functional.pad()</span></code></a>.</p>
 <dl class="field-list simple">
diff --git a/main/generated/torch.nn.ReplicationPad2d.html b/main/generated/torch.nn.ReplicationPad2d.html
index cadaf736e54..b5954eab267 100644
--- a/main/generated/torch.nn.ReplicationPad2d.html
+++ b/main/generated/torch.nn.ReplicationPad2d.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>ReplicationPad2d<a class="headerlink" href="#replicationpad2d" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.ReplicationPad2d">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">ReplicationPad2d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">padding</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/padding.html#ReplicationPad2d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/padding.py#L574"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.ReplicationPad2d" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">ReplicationPad2d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">padding</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/padding.html#ReplicationPad2d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/padding.py#L574"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.ReplicationPad2d" title="Permalink to this definition">¶</a></dt>
 <dd><p>Pads the input tensor using replication of the input boundary.</p>
 <p>For <cite>N</cite>-dimensional padding, use <a class="reference internal" href="torch.nn.functional.pad.html#torch.nn.functional.pad" title="torch.nn.functional.pad"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.nn.functional.pad()</span></code></a>.</p>
 <dl class="field-list simple">
diff --git a/main/generated/torch.nn.ReplicationPad3d.html b/main/generated/torch.nn.ReplicationPad3d.html
index f8c4f464ca2..e932c5b186f 100644
--- a/main/generated/torch.nn.ReplicationPad3d.html
+++ b/main/generated/torch.nn.ReplicationPad3d.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>ReplicationPad3d<a class="headerlink" href="#replicationpad3d" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.ReplicationPad3d">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">ReplicationPad3d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">padding</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/padding.html#ReplicationPad3d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/padding.py#L626"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.ReplicationPad3d" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">ReplicationPad3d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">padding</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/padding.html#ReplicationPad3d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/padding.py#L626"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.ReplicationPad3d" title="Permalink to this definition">¶</a></dt>
 <dd><p>Pads the input tensor using replication of the input boundary.</p>
 <p>For <cite>N</cite>-dimensional padding, use <a class="reference internal" href="torch.nn.functional.pad.html#torch.nn.functional.pad" title="torch.nn.functional.pad"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.nn.functional.pad()</span></code></a>.</p>
 <dl class="field-list simple">
diff --git a/main/generated/torch.nn.SELU.html b/main/generated/torch.nn.SELU.html
index 2ed86706381..c4ea6d7c538 100644
--- a/main/generated/torch.nn.SELU.html
+++ b/main/generated/torch.nn.SELU.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>SELU<a class="headerlink" href="#selu" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.SELU">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">SELU</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/activation.html#SELU"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/activation.py#L613"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.SELU" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">SELU</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/activation.html#SELU"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/activation.py#L613"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.SELU" title="Permalink to this definition">¶</a></dt>
 <dd><p>Applies the SELU function element-wise.</p>
 <div class="math">
 <span class="katex-display"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML" display="block"><semantics><mrow><mtext>SELU</mtext><mo stretchy="false">(</mo><mi>x</mi><mo stretchy="false">)</mo><mo>=</mo><mtext>scale</mtext><mo>∗</mo><mo stretchy="false">(</mo><mi>max</mi><mo>⁡</mo><mo stretchy="false">(</mo><mn>0</mn><mo separator="true">,</mo><mi>x</mi><mo stretchy="false">)</mo><mo>+</mo><mi>min</mi><mo>⁡</mo><mo stretchy="false">(</mo><mn>0</mn><mo separator="true">,</mo><mi>α</mi><mo>∗</mo><mo stretchy="false">(</mo><mi>exp</mi><mo>⁡</mo><mo stretchy="false">(</mo><mi>x</mi><mo stretchy="false">)</mo><mo>−</mo><mn>1</mn><mo stretchy="false">)</mo><mo stretchy="false">)</mo><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">\text{SELU}(x) = \text{scale} * (\max(0,x) + \min(0, \alpha * (\exp(x) - 1)))
diff --git a/main/generated/torch.nn.Sequential.html b/main/generated/torch.nn.Sequential.html
index f2e903086a4..a1db669a030 100644
--- a/main/generated/torch.nn.Sequential.html
+++ b/main/generated/torch.nn.Sequential.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>Sequential<a class="headerlink" href="#sequential" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.Sequential">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">Sequential</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="torch.nn.Module.html#torch.nn.Module" title="torch.nn.modules.module.Module"><span class="pre">Module</span></a></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/container.html#Sequential"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/container.py#L54"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Sequential" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">Sequential</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="torch.nn.Module.html#torch.nn.Module" title="torch.nn.modules.module.Module"><span class="pre">Module</span></a></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/container.html#Sequential"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/container.py#L54"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Sequential" title="Permalink to this definition">¶</a></dt>
 <dt class="sig sig-object py">
 <em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">Sequential</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">arg</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/collections.html#collections.OrderedDict" title="(in Python v3.13)"><span class="pre">OrderedDict</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="torch.nn.Module.html#torch.nn.Module" title="torch.nn.modules.module.Module"><span class="pre">Module</span></a><span class="p"><span class="pre">]</span></span></span></em><span class="sig-paren">)</span></dt>
 <dd><p>A sequential container.</p>
@@ -657,7 +657,7 @@ <h1>Sequential<a class="headerlink" href="#sequential" title="Permalink to this
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.Sequential.append">
-<span class="sig-name descname"><span class="pre">append</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/container.html#Sequential.append"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/container.py#L243"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Sequential.append" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">append</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/container.html#Sequential.append"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/container.py#L243"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Sequential.append" title="Permalink to this definition">¶</a></dt>
 <dd><p>Append a given module to the end.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.nn.SiLU.html b/main/generated/torch.nn.SiLU.html
index 6d94765266e..c2088bdf05d 100644
--- a/main/generated/torch.nn.SiLU.html
+++ b/main/generated/torch.nn.SiLU.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>SiLU<a class="headerlink" href="#silu" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.SiLU">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">SiLU</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/activation.html#SiLU"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/activation.py#L395"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.SiLU" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">SiLU</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/activation.html#SiLU"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/activation.py#L395"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.SiLU" title="Permalink to this definition">¶</a></dt>
 <dd><p>Applies the Sigmoid Linear Unit (SiLU) function, element-wise.</p>
 <p>The SiLU function is also known as the swish function.</p>
 <div class="math">
diff --git a/main/generated/torch.nn.Sigmoid.html b/main/generated/torch.nn.Sigmoid.html
index eadcd814f16..a92d2fb42b7 100644
--- a/main/generated/torch.nn.Sigmoid.html
+++ b/main/generated/torch.nn.Sigmoid.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>Sigmoid<a class="headerlink" href="#sigmoid" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.Sigmoid">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">Sigmoid</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/activation.html#Sigmoid"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/activation.py#L306"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Sigmoid" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">Sigmoid</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/activation.html#Sigmoid"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/activation.py#L306"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Sigmoid" title="Permalink to this definition">¶</a></dt>
 <dd><p>Applies the Sigmoid function element-wise.</p>
 <div class="math">
 <span class="katex-display"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML" display="block"><semantics><mrow><mtext>Sigmoid</mtext><mo stretchy="false">(</mo><mi>x</mi><mo stretchy="false">)</mo><mo>=</mo><mi>σ</mi><mo stretchy="false">(</mo><mi>x</mi><mo stretchy="false">)</mo><mo>=</mo><mfrac><mn>1</mn><mrow><mn>1</mn><mo>+</mo><mi>exp</mi><mo>⁡</mo><mo stretchy="false">(</mo><mo>−</mo><mi>x</mi><mo stretchy="false">)</mo></mrow></mfrac></mrow><annotation encoding="application/x-tex">\text{Sigmoid}(x) = \sigma(x) = \frac{1}{1 + \exp(-x)}
diff --git a/main/generated/torch.nn.SmoothL1Loss.html b/main/generated/torch.nn.SmoothL1Loss.html
index 1892f375bc5..31f923b41c5 100644
--- a/main/generated/torch.nn.SmoothL1Loss.html
+++ b/main/generated/torch.nn.SmoothL1Loss.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>SmoothL1Loss<a class="headerlink" href="#smoothl1loss" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.SmoothL1Loss">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">SmoothL1Loss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">size_average</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduce</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mean'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">beta</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/loss.html#SmoothL1Loss"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/loss.py#L962"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.SmoothL1Loss" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">SmoothL1Loss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">size_average</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduce</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mean'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">beta</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/loss.html#SmoothL1Loss"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/loss.py#L962"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.SmoothL1Loss" title="Permalink to this definition">¶</a></dt>
 <dd><p>Creates a criterion that uses a squared term if the absolute
 element-wise error falls below beta and an L1 term otherwise.
 It is less sensitive to outliers than <a class="reference internal" href="torch.nn.MSELoss.html#torch.nn.MSELoss" title="torch.nn.MSELoss"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.nn.MSELoss</span></code></a> and in some cases
diff --git a/main/generated/torch.nn.SoftMarginLoss.html b/main/generated/torch.nn.SoftMarginLoss.html
index ab999414940..5188a097a71 100644
--- a/main/generated/torch.nn.SoftMarginLoss.html
+++ b/main/generated/torch.nn.SoftMarginLoss.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
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@@ -612,7 +612,7 @@
 <h1>SoftMarginLoss<a class="headerlink" href="#softmarginloss" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.SoftMarginLoss">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">SoftMarginLoss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">size_average</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduce</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mean'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/loss.html#SoftMarginLoss"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/loss.py#L1105"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.SoftMarginLoss" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">SoftMarginLoss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">size_average</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduce</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mean'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/loss.html#SoftMarginLoss"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/loss.py#L1105"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.SoftMarginLoss" title="Permalink to this definition">¶</a></dt>
 <dd><p>Creates a criterion that optimizes a two-class classification
 logistic loss between input tensor <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>x</mi></mrow><annotation encoding="application/x-tex">x</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.4306em;"></span><span class="mord mathnormal">x</span></span></span></span></span> and target tensor <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>y</mi></mrow><annotation encoding="application/x-tex">y</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.625em;vertical-align:-0.1944em;"></span><span class="mord mathnormal" style="margin-right:0.03588em;">y</span></span></span></span></span>
 (containing 1 or -1).</p>
diff --git a/main/generated/torch.nn.Softmax.html b/main/generated/torch.nn.Softmax.html
index f69a4f73f27..86bad122489 100644
--- a/main/generated/torch.nn.Softmax.html
+++ b/main/generated/torch.nn.Softmax.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>Softmax<a class="headerlink" href="#softmax" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.Softmax">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">Softmax</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/activation.html#Softmax"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/activation.py#L1619"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Softmax" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">Softmax</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/activation.html#Softmax"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/activation.py#L1619"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Softmax" title="Permalink to this definition">¶</a></dt>
 <dd><p>Applies the Softmax function to an n-dimensional input Tensor.</p>
 <p>Rescales them so that the elements of the n-dimensional output Tensor
 lie in the range [0,1] and sum to 1.</p>
diff --git a/main/generated/torch.nn.Softmax2d.html b/main/generated/torch.nn.Softmax2d.html
index 70f4cbfb856..94b2304fd4a 100644
--- a/main/generated/torch.nn.Softmax2d.html
+++ b/main/generated/torch.nn.Softmax2d.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>Softmax2d<a class="headerlink" href="#softmax2d" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.Softmax2d">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">Softmax2d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/activation.html#Softmax2d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/activation.py#L1678"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Softmax2d" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">Softmax2d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/activation.html#Softmax2d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/activation.py#L1678"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Softmax2d" title="Permalink to this definition">¶</a></dt>
 <dd><p>Applies SoftMax over features to each spatial location.</p>
 <p>When given an image of <code class="docutils literal notranslate"><span class="pre">Channels</span> <span class="pre">x</span> <span class="pre">Height</span> <span class="pre">x</span> <span class="pre">Width</span></code>, it will
 apply <cite>Softmax</cite> to each location <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mo stretchy="false">(</mo><mi>C</mi><mi>h</mi><mi>a</mi><mi>n</mi><mi>n</mi><mi>e</mi><mi>l</mi><mi>s</mi><mo separator="true">,</mo><msub><mi>h</mi><mi>i</mi></msub><mo separator="true">,</mo><msub><mi>w</mi><mi>j</mi></msub><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">(Channels, h_i, w_j)</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1.0361em;vertical-align:-0.2861em;"></span><span class="mopen">(</span><span class="mord mathnormal" style="margin-right:0.07153em;">C</span><span class="mord mathnormal">hann</span><span class="mord mathnormal">e</span><span class="mord mathnormal" style="margin-right:0.01968em;">l</span><span class="mord mathnormal">s</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord"><span class="mord mathnormal">h</span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.3117em;"><span style="top:-2.55em;margin-left:0em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mathnormal mtight">i</span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.15em;"><span></span></span></span></span></span></span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord"><span class="mord mathnormal" style="margin-right:0.02691em;">w</span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.3117em;"><span style="top:-2.55em;margin-left:-0.0269em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mathnormal mtight" style="margin-right:0.05724em;">j</span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.2861em;"><span></span></span></span></span></span></span><span class="mclose">)</span></span></span></span></span></p>
diff --git a/main/generated/torch.nn.Softmin.html b/main/generated/torch.nn.Softmin.html
index d73f4e20482..1c81ccc9a20 100644
--- a/main/generated/torch.nn.Softmin.html
+++ b/main/generated/torch.nn.Softmin.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>Softmin<a class="headerlink" href="#softmin" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.Softmin">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">Softmin</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/activation.html#Softmin"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/activation.py#L1569"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Softmin" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">Softmin</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/activation.html#Softmin"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/activation.py#L1569"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Softmin" title="Permalink to this definition">¶</a></dt>
 <dd><p>Applies the Softmin function to an n-dimensional input Tensor.</p>
 <p>Rescales them so that the elements of the n-dimensional output Tensor
 lie in the range <cite>[0, 1]</cite> and sum to 1.</p>
diff --git a/main/generated/torch.nn.Softplus.html b/main/generated/torch.nn.Softplus.html
index bbaff469f86..45baecab358 100644
--- a/main/generated/torch.nn.Softplus.html
+++ b/main/generated/torch.nn.Softplus.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>Softplus<a class="headerlink" href="#softplus" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.Softplus">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">Softplus</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">beta</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">threshold</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">20.0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/activation.html#Softplus"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/activation.py#L858"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Softplus" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">Softplus</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">beta</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">threshold</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">20.0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/activation.html#Softplus"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/activation.py#L858"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Softplus" title="Permalink to this definition">¶</a></dt>
 <dd><p>Applies the Softplus function element-wise.</p>
 <div class="math">
 <span class="katex-display"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML" display="block"><semantics><mrow><mtext>Softplus</mtext><mo stretchy="false">(</mo><mi>x</mi><mo stretchy="false">)</mo><mo>=</mo><mfrac><mn>1</mn><mi>β</mi></mfrac><mo>∗</mo><mi>log</mi><mo>⁡</mo><mo stretchy="false">(</mo><mn>1</mn><mo>+</mo><mi>exp</mi><mo>⁡</mo><mo stretchy="false">(</mo><mi>β</mi><mo>∗</mo><mi>x</mi><mo stretchy="false">)</mo><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">\text{Softplus}(x) = \frac{1}{\beta} * \log(1 + \exp(\beta * x))
diff --git a/main/generated/torch.nn.Softshrink.html b/main/generated/torch.nn.Softshrink.html
index d7f27bc6a40..fe33f6b6016 100644
--- a/main/generated/torch.nn.Softshrink.html
+++ b/main/generated/torch.nn.Softshrink.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>Softshrink<a class="headerlink" href="#softshrink" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.Softshrink">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">Softshrink</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">lambd</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.5</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/activation.html#Softshrink"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/activation.py#L903"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Softshrink" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">Softshrink</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">lambd</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.5</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/activation.html#Softshrink"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/activation.py#L903"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Softshrink" title="Permalink to this definition">¶</a></dt>
 <dd><p>Applies the soft shrinkage function element-wise.</p>
 <div class="math">
 <span class="katex-display"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML" display="block"><semantics><mrow><mtext>SoftShrinkage</mtext><mo stretchy="false">(</mo><mi>x</mi><mo stretchy="false">)</mo><mo>=</mo><mrow><mo fence="true">{</mo><mtable rowspacing="0.36em" columnalign="left left" columnspacing="1em"><mtr><mtd><mstyle scriptlevel="0" displaystyle="false"><mrow><mi>x</mi><mo>−</mo><mi>λ</mi><mo separator="true">,</mo></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="false"><mrow><mtext> if </mtext><mi>x</mi><mo>&gt;</mo><mi>λ</mi></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="false"><mrow><mi>x</mi><mo>+</mo><mi>λ</mi><mo separator="true">,</mo></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="false"><mrow><mtext> if </mtext><mi>x</mi><mo>&lt;</mo><mo>−</mo><mi>λ</mi></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="false"><mrow><mn>0</mn><mo separator="true">,</mo></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="false"><mtext> otherwise </mtext></mstyle></mtd></mtr></mtable></mrow></mrow><annotation encoding="application/x-tex">\text{SoftShrinkage}(x) =
diff --git a/main/generated/torch.nn.Softsign.html b/main/generated/torch.nn.Softsign.html
index cf1bed769fd..bf3ff6cee2d 100644
--- a/main/generated/torch.nn.Softsign.html
+++ b/main/generated/torch.nn.Softsign.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>Softsign<a class="headerlink" href="#softsign" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.Softsign">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">Softsign</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/activation.html#Softsign"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/activation.py#L1523"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Softsign" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">Softsign</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/activation.html#Softsign"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/activation.py#L1523"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Softsign" title="Permalink to this definition">¶</a></dt>
 <dd><p>Applies the element-wise Softsign function.</p>
 <div class="math">
 <span class="katex-display"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML" display="block"><semantics><mrow><mtext>SoftSign</mtext><mo stretchy="false">(</mo><mi>x</mi><mo stretchy="false">)</mo><mo>=</mo><mfrac><mi>x</mi><mrow><mn>1</mn><mo>+</mo><mi mathvariant="normal">∣</mi><mi>x</mi><mi mathvariant="normal">∣</mi></mrow></mfrac></mrow><annotation encoding="application/x-tex">\text{SoftSign}(x) = \frac{x}{ 1 + |x|}
diff --git a/main/generated/torch.nn.SyncBatchNorm.html b/main/generated/torch.nn.SyncBatchNorm.html
index 0e6ff6e49e2..6c862ffd7a4 100644
--- a/main/generated/torch.nn.SyncBatchNorm.html
+++ b/main/generated/torch.nn.SyncBatchNorm.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>SyncBatchNorm<a class="headerlink" href="#syncbatchnorm" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.SyncBatchNorm">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">SyncBatchNorm</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">num_features</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-05</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">momentum</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">affine</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">track_running_stats</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">process_group</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/batchnorm.html#SyncBatchNorm"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/batchnorm.py#L600"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.SyncBatchNorm" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">SyncBatchNorm</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">num_features</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-05</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">momentum</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">affine</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">track_running_stats</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">process_group</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/batchnorm.html#SyncBatchNorm"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/batchnorm.py#L600"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.SyncBatchNorm" title="Permalink to this definition">¶</a></dt>
 <dd><p>Applies Batch Normalization over a N-Dimensional input.</p>
 <p>The N-D input is a mini-batch of [N-2]D inputs with additional channel dimension) as described in the paper
 <a class="reference external" href="https://arxiv.org/abs/1502.03167">Batch Normalization: Accelerating Deep Network Training by Reducing
@@ -724,7 +724,7 @@ <h1>SyncBatchNorm<a class="headerlink" href="#syncbatchnorm" title="Permalink to
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.SyncBatchNorm.convert_sync_batchnorm">
-<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">convert_sync_batchnorm</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">process_group</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/batchnorm.html#SyncBatchNorm.convert_sync_batchnorm"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/batchnorm.py#L822"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.SyncBatchNorm.convert_sync_batchnorm" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">convert_sync_batchnorm</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">process_group</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/batchnorm.html#SyncBatchNorm.convert_sync_batchnorm"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/batchnorm.py#L822"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.SyncBatchNorm.convert_sync_batchnorm" title="Permalink to this definition">¶</a></dt>
 <dd><p>Converts all <code class="xref py py-attr docutils literal notranslate"><span class="pre">BatchNorm*D</span></code> layers in the model to <a class="reference internal" href="#torch.nn.SyncBatchNorm" title="torch.nn.SyncBatchNorm"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.nn.SyncBatchNorm</span></code></a> layers.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.nn.Tanh.html b/main/generated/torch.nn.Tanh.html
index 18cdc80c5c6..b10c77b061f 100644
--- a/main/generated/torch.nn.Tanh.html
+++ b/main/generated/torch.nn.Tanh.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>Tanh<a class="headerlink" href="#tanh" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.Tanh">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">Tanh</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/activation.html#Tanh"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/activation.py#L370"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Tanh" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">Tanh</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/activation.html#Tanh"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/activation.py#L370"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Tanh" title="Permalink to this definition">¶</a></dt>
 <dd><p>Applies the Hyperbolic Tangent (Tanh) function element-wise.</p>
 <p>Tanh is defined as:</p>
 <div class="math">
diff --git a/main/generated/torch.nn.Tanhshrink.html b/main/generated/torch.nn.Tanhshrink.html
index c5b683441c4..f11718fa870 100644
--- a/main/generated/torch.nn.Tanhshrink.html
+++ b/main/generated/torch.nn.Tanhshrink.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>Tanhshrink<a class="headerlink" href="#tanhshrink" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.Tanhshrink">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">Tanhshrink</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/activation.html#Tanhshrink"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/activation.py#L1546"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Tanhshrink" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">Tanhshrink</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/activation.html#Tanhshrink"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/activation.py#L1546"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Tanhshrink" title="Permalink to this definition">¶</a></dt>
 <dd><p>Applies the element-wise Tanhshrink function.</p>
 <div class="math">
 <span class="katex-display"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML" display="block"><semantics><mrow><mtext>Tanhshrink</mtext><mo stretchy="false">(</mo><mi>x</mi><mo stretchy="false">)</mo><mo>=</mo><mi>x</mi><mo>−</mo><mi>tanh</mi><mo>⁡</mo><mo stretchy="false">(</mo><mi>x</mi><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">\text{Tanhshrink}(x) = x - \tanh(x)
diff --git a/main/generated/torch.nn.Threshold.html b/main/generated/torch.nn.Threshold.html
index ed4da41c359..c74c90c50d3 100644
--- a/main/generated/torch.nn.Threshold.html
+++ b/main/generated/torch.nn.Threshold.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>Threshold<a class="headerlink" href="#threshold" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.Threshold">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">Threshold</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">threshold</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">value</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/activation.html#Threshold"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/activation.py#L48"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Threshold" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">Threshold</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">threshold</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">value</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/activation.html#Threshold"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/activation.py#L48"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Threshold" title="Permalink to this definition">¶</a></dt>
 <dd><p>Thresholds each element of the input Tensor.</p>
 <p>Threshold is defined as:</p>
 <div class="math">
diff --git a/main/generated/torch.nn.Transformer.html b/main/generated/torch.nn.Transformer.html
index 0d6d01aca13..28830f77612 100644
--- a/main/generated/torch.nn.Transformer.html
+++ b/main/generated/torch.nn.Transformer.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>Transformer<a class="headerlink" href="#transformer" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.Transformer">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">Transformer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">d_model=512</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">nhead=8</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_encoder_layers=6</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_decoder_layers=6</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dim_feedforward=2048</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dropout=0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">activation=&lt;function</span> <span class="pre">relu&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">custom_encoder=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">custom_decoder=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">layer_norm_eps=1e-05</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">batch_first=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">norm_first=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias=True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype=None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/transformer.html#Transformer"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/transformer.py#L57"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Transformer" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">Transformer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">d_model=512</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">nhead=8</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_encoder_layers=6</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_decoder_layers=6</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dim_feedforward=2048</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dropout=0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">activation=&lt;function</span> <span class="pre">relu&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">custom_encoder=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">custom_decoder=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">layer_norm_eps=1e-05</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">batch_first=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">norm_first=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias=True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype=None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/transformer.html#Transformer"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/transformer.py#L57"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Transformer" title="Permalink to this definition">¶</a></dt>
 <dd><p>A transformer model.</p>
 <div class="admonition note">
 <p class="admonition-title">Note</p>
@@ -658,7 +658,7 @@ <h1>Transformer<a class="headerlink" href="#transformer" title="Permalink to thi
 <a class="reference external" href="https://github.com/pytorch/examples/tree/master/word_language_model">https://github.com/pytorch/examples/tree/master/word_language_model</a></p>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.Transformer.forward">
-<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">src</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tgt</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">src_mask</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tgt_mask</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">memory_mask</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">src_key_padding_mask</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tgt_key_padding_mask</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">memory_key_padding_mask</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">src_is_causal</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tgt_is_causal</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">memory_is_causal</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/transformer.html#Transformer.forward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/transformer.py#L170"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Transformer.forward" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">src</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tgt</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">src_mask</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tgt_mask</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">memory_mask</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">src_key_padding_mask</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tgt_key_padding_mask</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">memory_key_padding_mask</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">src_is_causal</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tgt_is_causal</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">memory_is_causal</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/transformer.html#Transformer.forward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/transformer.py#L170"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Transformer.forward" title="Permalink to this definition">¶</a></dt>
 <dd><p>Take in and process masked source/target sequences.</p>
 <div class="admonition note">
 <p class="admonition-title">Note</p>
@@ -745,7 +745,7 @@ <h1>Transformer<a class="headerlink" href="#transformer" title="Permalink to thi
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.Transformer.generate_square_subsequent_mask">
-<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">generate_square_subsequent_mask</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sz</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/transformer.html#Transformer.generate_square_subsequent_mask"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/transformer.py#L288"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Transformer.generate_square_subsequent_mask" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">generate_square_subsequent_mask</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sz</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/transformer.html#Transformer.generate_square_subsequent_mask"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/transformer.py#L288"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Transformer.generate_square_subsequent_mask" title="Permalink to this definition">¶</a></dt>
 <dd><p>Generate a square causal mask for the sequence.</p>
 <p>The masked positions are filled with float(‘-inf’). Unmasked positions are filled with float(0.0).</p>
 <dl class="field-list simple">
diff --git a/main/generated/torch.nn.TransformerDecoder.html b/main/generated/torch.nn.TransformerDecoder.html
index 82050c03889..90274fa44b4 100644
--- a/main/generated/torch.nn.TransformerDecoder.html
+++ b/main/generated/torch.nn.TransformerDecoder.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>TransformerDecoder<a class="headerlink" href="#transformerdecoder" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.TransformerDecoder">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">TransformerDecoder</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">decoder_layer</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_layers</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">norm</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/transformer.html#TransformerDecoder"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/transformer.py#L530"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.TransformerDecoder" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">TransformerDecoder</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">decoder_layer</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_layers</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">norm</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/transformer.html#TransformerDecoder"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/transformer.py#L530"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.TransformerDecoder" title="Permalink to this definition">¶</a></dt>
 <dd><p>TransformerDecoder is a stack of N decoder layers.</p>
 <div class="admonition note">
 <p class="admonition-title">Note</p>
@@ -641,7 +641,7 @@ <h1>TransformerDecoder<a class="headerlink" href="#transformerdecoder" title="Pe
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.TransformerDecoder.forward">
-<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tgt</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">memory</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tgt_mask</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">memory_mask</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tgt_key_padding_mask</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">memory_key_padding_mask</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tgt_is_causal</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">memory_is_causal</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/transformer.html#TransformerDecoder.forward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/transformer.py#L565"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.TransformerDecoder.forward" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tgt</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">memory</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tgt_mask</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">memory_mask</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tgt_key_padding_mask</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">memory_key_padding_mask</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tgt_is_causal</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">memory_is_causal</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/transformer.html#TransformerDecoder.forward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/transformer.py#L565"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.TransformerDecoder.forward" title="Permalink to this definition">¶</a></dt>
 <dd><p>Pass the inputs (and mask) through the decoder layer in turn.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.nn.TransformerDecoderLayer.html b/main/generated/torch.nn.TransformerDecoderLayer.html
index 43167c75da6..26100f56622 100644
--- a/main/generated/torch.nn.TransformerDecoderLayer.html
+++ b/main/generated/torch.nn.TransformerDecoderLayer.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>TransformerDecoderLayer<a class="headerlink" href="#transformerdecoderlayer" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.TransformerDecoderLayer">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">TransformerDecoderLayer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">d_model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">nhead</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dim_feedforward=2048</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dropout=0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">activation=&lt;function</span> <span class="pre">relu&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">layer_norm_eps=1e-05</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">batch_first=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">norm_first=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias=True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype=None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/transformer.html#TransformerDecoderLayer"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/transformer.py#L945"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.TransformerDecoderLayer" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">TransformerDecoderLayer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">d_model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">nhead</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dim_feedforward=2048</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dropout=0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">activation=&lt;function</span> <span class="pre">relu&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">layer_norm_eps=1e-05</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">batch_first=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">norm_first=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias=True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype=None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/transformer.html#TransformerDecoderLayer"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/transformer.py#L945"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.TransformerDecoderLayer" title="Permalink to this definition">¶</a></dt>
 <dd><p>TransformerDecoderLayer is made up of self-attn, multi-head-attn and feedforward network.</p>
 <div class="admonition note">
 <p class="admonition-title">Note</p>
@@ -660,7 +660,7 @@ <h1>TransformerDecoderLayer<a class="headerlink" href="#transformerdecoderlayer"
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.TransformerDecoderLayer.forward">
-<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tgt</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">memory</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tgt_mask</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">memory_mask</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tgt_key_padding_mask</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">memory_key_padding_mask</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tgt_is_causal</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">memory_is_causal</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/transformer.html#TransformerDecoderLayer.forward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/transformer.py#L1043"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.TransformerDecoderLayer.forward" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tgt</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">memory</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tgt_mask</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">memory_mask</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tgt_key_padding_mask</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">memory_key_padding_mask</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tgt_is_causal</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">memory_is_causal</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/transformer.html#TransformerDecoderLayer.forward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/transformer.py#L1043"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.TransformerDecoderLayer.forward" title="Permalink to this definition">¶</a></dt>
 <dd><p>Pass the inputs (and mask) through the decoder layer.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.nn.TransformerEncoder.html b/main/generated/torch.nn.TransformerEncoder.html
index 9563d8fd87e..9f491f6f13d 100644
--- a/main/generated/torch.nn.TransformerEncoder.html
+++ b/main/generated/torch.nn.TransformerEncoder.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>TransformerEncoder<a class="headerlink" href="#transformerencoder" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.TransformerEncoder">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">TransformerEncoder</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">encoder_layer</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_layers</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">norm</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">enable_nested_tensor</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mask_check</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/transformer.html#TransformerEncoder"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/transformer.py#L307"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.TransformerEncoder" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">TransformerEncoder</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">encoder_layer</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_layers</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">norm</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">enable_nested_tensor</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mask_check</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/transformer.html#TransformerEncoder"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/transformer.py#L307"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.TransformerEncoder" title="Permalink to this definition">¶</a></dt>
 <dd><p>TransformerEncoder is a stack of N encoder layers.</p>
 <div class="admonition note">
 <p class="admonition-title">Note</p>
@@ -644,7 +644,7 @@ <h1>TransformerEncoder<a class="headerlink" href="#transformerencoder" title="Pe
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.TransformerEncoder.forward">
-<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">src</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mask</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">src_key_padding_mask</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">is_causal</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/transformer.html#TransformerEncoder.forward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/transformer.py#L387"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.TransformerEncoder.forward" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">src</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mask</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">src_key_padding_mask</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">is_causal</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/transformer.html#TransformerEncoder.forward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/transformer.py#L387"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.TransformerEncoder.forward" title="Permalink to this definition">¶</a></dt>
 <dd><p>Pass the input through the encoder layers in turn.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.nn.TransformerEncoderLayer.html b/main/generated/torch.nn.TransformerEncoderLayer.html
index 0d8315023cd..2bf5bc1f2ff 100644
--- a/main/generated/torch.nn.TransformerEncoderLayer.html
+++ b/main/generated/torch.nn.TransformerEncoderLayer.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>TransformerEncoderLayer<a class="headerlink" href="#transformerencoderlayer" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.TransformerEncoderLayer">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">TransformerEncoderLayer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">d_model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">nhead</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dim_feedforward=2048</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dropout=0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">activation=&lt;function</span> <span class="pre">relu&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">layer_norm_eps=1e-05</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">batch_first=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">norm_first=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias=True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype=None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/transformer.html#TransformerEncoderLayer"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/transformer.py#L627"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.TransformerEncoderLayer" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">TransformerEncoderLayer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">d_model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">nhead</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dim_feedforward=2048</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dropout=0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">activation=&lt;function</span> <span class="pre">relu&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">layer_norm_eps=1e-05</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">batch_first=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">norm_first=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias=True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype=None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/transformer.html#TransformerEncoderLayer"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/transformer.py#L627"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.TransformerEncoderLayer" title="Permalink to this definition">¶</a></dt>
 <dd><p>TransformerEncoderLayer is made up of self-attn and feedforward network.</p>
 <div class="admonition note">
 <p class="admonition-title">Note</p>
@@ -690,7 +690,7 @@ <h1>TransformerEncoderLayer<a class="headerlink" href="#transformerencoderlayer"
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.TransformerEncoderLayer.forward">
-<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">src</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">src_mask</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">src_key_padding_mask</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">is_causal</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/transformer.html#TransformerEncoderLayer.forward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/transformer.py#L760"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.TransformerEncoderLayer.forward" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">src</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">src_mask</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">src_key_padding_mask</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">is_causal</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/transformer.html#TransformerEncoderLayer.forward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/transformer.py#L760"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.TransformerEncoderLayer.forward" title="Permalink to this definition">¶</a></dt>
 <dd><p>Pass the input through the encoder layer.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.nn.TripletMarginLoss.html b/main/generated/torch.nn.TripletMarginLoss.html
index 0a0fe113045..0db29d2658c 100644
--- a/main/generated/torch.nn.TripletMarginLoss.html
+++ b/main/generated/torch.nn.TripletMarginLoss.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>TripletMarginLoss<a class="headerlink" href="#tripletmarginloss" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.TripletMarginLoss">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">TripletMarginLoss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">margin</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">p</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-06</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">swap</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">size_average</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduce</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mean'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/loss.html#TripletMarginLoss"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/loss.py#L1589"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.TripletMarginLoss" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">TripletMarginLoss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">margin</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">p</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-06</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">swap</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">size_average</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduce</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mean'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/loss.html#TripletMarginLoss"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/loss.py#L1589"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.TripletMarginLoss" title="Permalink to this definition">¶</a></dt>
 <dd><p>Creates a criterion that measures the triplet loss given an input
 tensors <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>x</mi><mn>1</mn></mrow><annotation encoding="application/x-tex">x1</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.6444em;"></span><span class="mord mathnormal">x</span><span class="mord">1</span></span></span></span></span>, <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>x</mi><mn>2</mn></mrow><annotation encoding="application/x-tex">x2</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.6444em;"></span><span class="mord mathnormal">x</span><span class="mord">2</span></span></span></span></span>, <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>x</mi><mn>3</mn></mrow><annotation encoding="application/x-tex">x3</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.6444em;"></span><span class="mord mathnormal">x</span><span class="mord">3</span></span></span></span></span> and a margin with a value greater than <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mn>0</mn></mrow><annotation encoding="application/x-tex">0</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.6444em;"></span><span class="mord">0</span></span></span></span></span>.
 This is used for measuring a relative similarity between samples. A triplet
diff --git a/main/generated/torch.nn.TripletMarginWithDistanceLoss.html b/main/generated/torch.nn.TripletMarginWithDistanceLoss.html
index d111400610f..dd0f1bb96c4 100644
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@@ -612,7 +612,7 @@
 <h1>TripletMarginWithDistanceLoss<a class="headerlink" href="#tripletmarginwithdistanceloss" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.TripletMarginWithDistanceLoss">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">TripletMarginWithDistanceLoss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">distance_function</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">margin</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">swap</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mean'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/loss.html#TripletMarginWithDistanceLoss"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/loss.py#L1697"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.TripletMarginWithDistanceLoss" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">TripletMarginWithDistanceLoss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">distance_function</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">margin</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">swap</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mean'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/loss.html#TripletMarginWithDistanceLoss"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/loss.py#L1697"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.TripletMarginWithDistanceLoss" title="Permalink to this definition">¶</a></dt>
 <dd><p>Creates a criterion that measures the triplet loss given input
 tensors <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>a</mi></mrow><annotation encoding="application/x-tex">a</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.4306em;"></span><span class="mord mathnormal">a</span></span></span></span></span>, <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>p</mi></mrow><annotation encoding="application/x-tex">p</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.625em;vertical-align:-0.1944em;"></span><span class="mord mathnormal">p</span></span></span></span></span>, and <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>n</mi></mrow><annotation encoding="application/x-tex">n</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.4306em;"></span><span class="mord mathnormal">n</span></span></span></span></span> (representing anchor,
 positive, and negative examples, respectively), and a nonnegative,
diff --git a/main/generated/torch.nn.Unflatten.html b/main/generated/torch.nn.Unflatten.html
index c07c43fdb44..247a13a3e1d 100644
--- a/main/generated/torch.nn.Unflatten.html
+++ b/main/generated/torch.nn.Unflatten.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
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     </div>
     <div id="searchBox">
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@@ -612,7 +612,7 @@
 <h1>Unflatten<a class="headerlink" href="#unflatten" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.Unflatten">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">Unflatten</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dim</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">unflattened_size</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/flatten.html#Unflatten"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/flatten.py#L59"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Unflatten" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">Unflatten</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dim</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">unflattened_size</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/flatten.html#Unflatten"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/flatten.py#L59"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Unflatten" title="Permalink to this definition">¶</a></dt>
 <dd><p>Unflattens a tensor dim expanding it to a desired shape. For use with <code class="xref py py-class docutils literal notranslate"><span class="pre">Sequential</span></code>.</p>
 <ul class="simple">
 <li><p><code class="xref py py-attr docutils literal notranslate"><span class="pre">dim</span></code> specifies the dimension of the input tensor to be unflattened, and it can
diff --git a/main/generated/torch.nn.Unfold.html b/main/generated/torch.nn.Unfold.html
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--- a/main/generated/torch.nn.Unfold.html
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           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>Unfold<a class="headerlink" href="#unfold" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.Unfold">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">Unfold</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dilation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/fold.html#Unfold"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/fold.py#L164"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Unfold" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">Unfold</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dilation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/fold.html#Unfold"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/fold.py#L164"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Unfold" title="Permalink to this definition">¶</a></dt>
 <dd><p>Extracts sliding local blocks from a batched input tensor.</p>
 <p>Consider a batched <code class="xref py py-attr docutils literal notranslate"><span class="pre">input</span></code> tensor of shape <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mo stretchy="false">(</mo><mi>N</mi><mo separator="true">,</mo><mi>C</mi><mo separator="true">,</mo><mo>∗</mo><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">(N, C, *)</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mopen">(</span><span class="mord mathnormal" style="margin-right:0.10903em;">N</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord mathnormal" style="margin-right:0.07153em;">C</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord">∗</span><span class="mclose">)</span></span></span></span></span>,
 where <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>N</mi></mrow><annotation encoding="application/x-tex">N</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.6833em;"></span><span class="mord mathnormal" style="margin-right:0.10903em;">N</span></span></span></span></span> is the batch dimension, <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>C</mi></mrow><annotation encoding="application/x-tex">C</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.6833em;"></span><span class="mord mathnormal" style="margin-right:0.07153em;">C</span></span></span></span></span> is the channel dimension,
diff --git a/main/generated/torch.nn.Upsample.html b/main/generated/torch.nn.Upsample.html
index b65f4c48d55..605f51c4eaf 100644
--- a/main/generated/torch.nn.Upsample.html
+++ b/main/generated/torch.nn.Upsample.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
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@@ -612,7 +612,7 @@
 <h1>Upsample<a class="headerlink" href="#upsample" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.Upsample">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">Upsample</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale_factor</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'nearest'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">align_corners</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">recompute_scale_factor</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/upsampling.html#Upsample"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/upsampling.py#L14"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Upsample" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">Upsample</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale_factor</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'nearest'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">align_corners</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">recompute_scale_factor</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/upsampling.html#Upsample"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/upsampling.py#L14"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.Upsample" title="Permalink to this definition">¶</a></dt>
 <dd><p>Upsamples a given multi-channel 1D (temporal), 2D (spatial) or 3D (volumetric) data.</p>
 <p>The input data is assumed to be of the form
 <cite>minibatch x channels x [optional depth] x [optional height] x width</cite>.
diff --git a/main/generated/torch.nn.UpsamplingBilinear2d.html b/main/generated/torch.nn.UpsamplingBilinear2d.html
index 91a001f5fc9..8877aa338b7 100644
--- a/main/generated/torch.nn.UpsamplingBilinear2d.html
+++ b/main/generated/torch.nn.UpsamplingBilinear2d.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>UpsamplingBilinear2d<a class="headerlink" href="#upsamplingbilinear2d" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.UpsamplingBilinear2d">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">UpsamplingBilinear2d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale_factor</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/upsampling.html#UpsamplingBilinear2d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/upsampling.py#L245"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.UpsamplingBilinear2d" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">UpsamplingBilinear2d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale_factor</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/upsampling.html#UpsamplingBilinear2d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/upsampling.py#L245"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.UpsamplingBilinear2d" title="Permalink to this definition">¶</a></dt>
 <dd><p>Applies a 2D bilinear upsampling to an input signal composed of several input channels.</p>
 <p>To specify the scale, it takes either the <code class="xref py py-attr docutils literal notranslate"><span class="pre">size</span></code> or the <code class="xref py py-attr docutils literal notranslate"><span class="pre">scale_factor</span></code>
 as it’s constructor argument.</p>
diff --git a/main/generated/torch.nn.UpsamplingNearest2d.html b/main/generated/torch.nn.UpsamplingNearest2d.html
index 3242464f88a..c5896aad42b 100644
--- a/main/generated/torch.nn.UpsamplingNearest2d.html
+++ b/main/generated/torch.nn.UpsamplingNearest2d.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>UpsamplingNearest2d<a class="headerlink" href="#upsamplingnearest2d" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.UpsamplingNearest2d">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">UpsamplingNearest2d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale_factor</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/upsampling.html#UpsamplingNearest2d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/upsampling.py#L196"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.UpsamplingNearest2d" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">UpsamplingNearest2d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale_factor</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/upsampling.html#UpsamplingNearest2d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/upsampling.py#L196"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.UpsamplingNearest2d" title="Permalink to this definition">¶</a></dt>
 <dd><p>Applies a 2D nearest neighbor upsampling to an input signal composed of several input channels.</p>
 <p>To specify the scale, it takes either the <code class="xref py py-attr docutils literal notranslate"><span class="pre">size</span></code> or the <code class="xref py py-attr docutils literal notranslate"><span class="pre">scale_factor</span></code>
 as it’s constructor argument.</p>
diff --git a/main/generated/torch.nn.ZeroPad1d.html b/main/generated/torch.nn.ZeroPad1d.html
index 46fcd4aa8cf..94d1463c321 100644
--- a/main/generated/torch.nn.ZeroPad1d.html
+++ b/main/generated/torch.nn.ZeroPad1d.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>ZeroPad1d<a class="headerlink" href="#zeropad1d" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.ZeroPad1d">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">ZeroPad1d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">padding</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/padding.html#ZeroPad1d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/padding.py#L667"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.ZeroPad1d" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">ZeroPad1d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">padding</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/padding.html#ZeroPad1d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/padding.py#L667"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.ZeroPad1d" title="Permalink to this definition">¶</a></dt>
 <dd><p>Pads the input tensor boundaries with zero.</p>
 <p>For <cite>N</cite>-dimensional padding, use <a class="reference internal" href="torch.nn.functional.pad.html#torch.nn.functional.pad" title="torch.nn.functional.pad"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.nn.functional.pad()</span></code></a>.</p>
 <dl class="field-list simple">
diff --git a/main/generated/torch.nn.ZeroPad2d.html b/main/generated/torch.nn.ZeroPad2d.html
index 794714e03d6..96ec81b7bc5 100644
--- a/main/generated/torch.nn.ZeroPad2d.html
+++ b/main/generated/torch.nn.ZeroPad2d.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>ZeroPad2d<a class="headerlink" href="#zeropad2d" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.ZeroPad2d">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">ZeroPad2d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">padding</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/padding.html#ZeroPad2d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/padding.py#L720"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.ZeroPad2d" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">ZeroPad2d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">padding</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/padding.html#ZeroPad2d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/padding.py#L720"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.ZeroPad2d" title="Permalink to this definition">¶</a></dt>
 <dd><p>Pads the input tensor boundaries with zero.</p>
 <p>For <cite>N</cite>-dimensional padding, use <a class="reference internal" href="torch.nn.functional.pad.html#torch.nn.functional.pad" title="torch.nn.functional.pad"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.nn.functional.pad()</span></code></a>.</p>
 <dl class="field-list simple">
diff --git a/main/generated/torch.nn.ZeroPad3d.html b/main/generated/torch.nn.ZeroPad3d.html
index c207568139f..b8dae0e9052 100644
--- a/main/generated/torch.nn.ZeroPad3d.html
+++ b/main/generated/torch.nn.ZeroPad3d.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>ZeroPad3d<a class="headerlink" href="#zeropad3d" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.ZeroPad3d">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">ZeroPad3d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">padding</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/padding.html#ZeroPad3d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/padding.py#L774"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.ZeroPad3d" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.</span></span><span class="sig-name descname"><span class="pre">ZeroPad3d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">padding</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/padding.html#ZeroPad3d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/padding.py#L774"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.ZeroPad3d" title="Permalink to this definition">¶</a></dt>
 <dd><p>Pads the input tensor boundaries with zero.</p>
 <p>For <cite>N</cite>-dimensional padding, use <a class="reference internal" href="torch.nn.functional.pad.html#torch.nn.functional.pad" title="torch.nn.functional.pad"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.nn.functional.pad()</span></code></a>.</p>
 <dl class="field-list simple">
diff --git a/main/generated/torch.nn.attention.SDPBackend.html b/main/generated/torch.nn.attention.SDPBackend.html
index 05c78e1612f..74a53a1139a 100644
--- a/main/generated/torch.nn.attention.SDPBackend.html
+++ b/main/generated/torch.nn.attention.SDPBackend.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.nn.attention.bias.CausalBias.html b/main/generated/torch.nn.attention.bias.CausalBias.html
index 5a2e9765302..04e0ca4070d 100644
--- a/main/generated/torch.nn.attention.bias.CausalBias.html
+++ b/main/generated/torch.nn.attention.bias.CausalBias.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>torch.nn.attention.bias.CausalBias<a class="headerlink" href="#torch-nn-attention-bias-causalbias" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.attention.bias.CausalBias">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.attention.bias.</span></span><span class="sig-name descname"><span class="pre">CausalBias</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">variant</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">seq_len_q</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">seq_len_kv</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/attention/bias.html#CausalBias"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/attention/bias.py#L86"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.attention.bias.CausalBias" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.attention.bias.</span></span><span class="sig-name descname"><span class="pre">CausalBias</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">variant</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">seq_len_q</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">seq_len_kv</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/attention/bias.html#CausalBias"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/attention/bias.py#L86"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.attention.bias.CausalBias" title="Permalink to this definition">¶</a></dt>
 <dd><p>A bias representing causal attention patterns. For an overview of the bias structure, see the <a class="reference internal" href="torch.nn.attention.bias.CausalVariant.html#torch.nn.attention.bias.CausalVariant" title="torch.nn.attention.bias.CausalVariant"><code class="xref py py-class docutils literal notranslate"><span class="pre">CausalVariant</span></code></a> enum.</p>
 <p>This class is used for defining causal (triangular) attention biases. For construing the bias, there exist
 two factory functions: <a class="reference internal" href="torch.nn.attention.bias.causal_upper_left.html#torch.nn.attention.bias.causal_upper_left" title="torch.nn.attention.bias.causal_upper_left"><code class="xref py py-func docutils literal notranslate"><span class="pre">causal_upper_left()</span></code></a> and <a class="reference internal" href="torch.nn.attention.bias.causal_lower_right.html#torch.nn.attention.bias.causal_lower_right" title="torch.nn.attention.bias.causal_lower_right"><code class="xref py py-func docutils literal notranslate"><span class="pre">causal_lower_right()</span></code></a>.</p>
diff --git a/main/generated/torch.nn.attention.bias.CausalVariant.html b/main/generated/torch.nn.attention.bias.CausalVariant.html
index ea600092237..e65106cfb98 100644
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 <h1>CausalVariant<a class="headerlink" href="#causalvariant" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.attention.bias.CausalVariant">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.attention.bias.</span></span><span class="sig-name descname"><span class="pre">CausalVariant</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/attention/bias.html#CausalVariant"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/attention/bias.py#L33"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.attention.bias.CausalVariant" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.attention.bias.</span></span><span class="sig-name descname"><span class="pre">CausalVariant</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/attention/bias.html#CausalVariant"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/attention/bias.py#L33"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.attention.bias.CausalVariant" title="Permalink to this definition">¶</a></dt>
 <dd><p>Enum for causal variants used in attention mechanisms.</p>
 <p>Defines two types of causal biases:</p>
 <p><cite>UPPER_LEFT</cite>: Represents upper-left triangular bias for standard causal attention.
diff --git a/main/generated/torch.nn.attention.bias.causal_lower_right.html b/main/generated/torch.nn.attention.bias.causal_lower_right.html
index f74f74f4271..a8d63bed672 100644
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 <h1>torch.nn.attention.bias.causal_lower_right<a class="headerlink" href="#torch-nn-attention-bias-causal-lower-right" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.attention.bias.causal_lower_right">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.attention.bias.</span></span><span class="sig-name descname"><span class="pre">causal_lower_right</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">size</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/attention/bias.html#causal_lower_right"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/attention/bias.py#L329"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.attention.bias.causal_lower_right" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.attention.bias.</span></span><span class="sig-name descname"><span class="pre">causal_lower_right</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">size</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/attention/bias.html#causal_lower_right"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/attention/bias.py#L329"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.attention.bias.causal_lower_right" title="Permalink to this definition">¶</a></dt>
 <dd><p>Creates a lower-right triangular causal bias.</p>
 <p>This function generates a lower-right triangular matrix to represent causal attention bias with a
 diagonal offset set so that the inclusive values are aligned to the lower right corner of the matrix.</p>
diff --git a/main/generated/torch.nn.attention.bias.causal_upper_left.html b/main/generated/torch.nn.attention.bias.causal_upper_left.html
index cc3ab89f105..846a1e5b22b 100644
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 <h1>torch.nn.attention.bias.causal_upper_left<a class="headerlink" href="#torch-nn-attention-bias-causal-upper-left" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.attention.bias.causal_upper_left">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.attention.bias.</span></span><span class="sig-name descname"><span class="pre">causal_upper_left</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">size</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/attention/bias.html#causal_upper_left"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/attention/bias.py#L296"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.attention.bias.causal_upper_left" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.attention.bias.</span></span><span class="sig-name descname"><span class="pre">causal_upper_left</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">size</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/attention/bias.html#causal_upper_left"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/attention/bias.py#L296"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.attention.bias.causal_upper_left" title="Permalink to this definition">¶</a></dt>
 <dd><p>Creates an upper-left triangular causal bias.</p>
 <p>This function generates a upper-left triangular matrix to represent causal attention bias with a
 diagonal offset set so that the inclusive values are aligned to the upper left corner of the matrix.
diff --git a/main/generated/torch.nn.attention.sdpa_kernel.html b/main/generated/torch.nn.attention.sdpa_kernel.html
index 80860f792ba..6d5e1b4ac64 100644
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 <h1>torch.nn.attention.sdpa_kernel<a class="headerlink" href="#torch-nn-attention-sdpa-kernel" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.attention.sdpa_kernel">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.attention.</span></span><span class="sig-name descname"><span class="pre">sdpa_kernel</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">backends</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">set_priority</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/attention.html#sdpa_kernel"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/attention/__init__.py#L90"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.attention.sdpa_kernel" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.attention.</span></span><span class="sig-name descname"><span class="pre">sdpa_kernel</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">backends</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">set_priority</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/attention.html#sdpa_kernel"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/attention/__init__.py#L90"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.attention.sdpa_kernel" title="Permalink to this definition">¶</a></dt>
 <dd><p>Context manager to select which backend to use for scaled dot product attention.</p>
 <div class="admonition warning">
 <p class="admonition-title">Warning</p>
diff --git a/main/generated/torch.nn.functional.adaptive_avg_pool1d.html b/main/generated/torch.nn.functional.adaptive_avg_pool1d.html
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 <h1>torch.nn.functional.adaptive_avg_pool2d<a class="headerlink" href="#torch-nn-functional-adaptive-avg-pool2d" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.functional.adaptive_avg_pool2d">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">adaptive_avg_pool2d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_size</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/functional.html#adaptive_avg_pool2d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/functional.py#L1370"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.adaptive_avg_pool2d" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">adaptive_avg_pool2d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_size</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/functional.html#adaptive_avg_pool2d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/functional.py#L1370"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.adaptive_avg_pool2d" title="Permalink to this definition">¶</a></dt>
 <dd><p>Apply a 2D adaptive average pooling over an input signal composed of several input planes.</p>
 <p>See <a class="reference internal" href="torch.nn.AdaptiveAvgPool2d.html#torch.nn.AdaptiveAvgPool2d" title="torch.nn.AdaptiveAvgPool2d"><code class="xref py py-class docutils literal notranslate"><span class="pre">AdaptiveAvgPool2d</span></code></a> for details and output shape.</p>
 <dl class="field-list simple">
diff --git a/main/generated/torch.nn.functional.adaptive_avg_pool3d.html b/main/generated/torch.nn.functional.adaptive_avg_pool3d.html
index 2422716de9d..9812705467d 100644
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 <h1>torch.nn.functional.adaptive_avg_pool3d<a class="headerlink" href="#torch-nn-functional-adaptive-avg-pool3d" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.functional.adaptive_avg_pool3d">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">adaptive_avg_pool3d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_size</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/functional.html#adaptive_avg_pool3d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/functional.py#L1385"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.adaptive_avg_pool3d" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">adaptive_avg_pool3d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_size</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/functional.html#adaptive_avg_pool3d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/functional.py#L1385"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.adaptive_avg_pool3d" title="Permalink to this definition">¶</a></dt>
 <dd><p>Apply a 3D adaptive average pooling over an input signal composed of several input planes.</p>
 <p>See <a class="reference internal" href="torch.nn.AdaptiveAvgPool3d.html#torch.nn.AdaptiveAvgPool3d" title="torch.nn.AdaptiveAvgPool3d"><code class="xref py py-class docutils literal notranslate"><span class="pre">AdaptiveAvgPool3d</span></code></a> for details and output shape.</p>
 <dl class="field-list simple">
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 <h1>torch.nn.functional.adaptive_max_pool1d<a class="headerlink" href="#torch-nn-functional-adaptive-max-pool1d" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.functional.adaptive_max_pool1d">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">adaptive_max_pool1d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_indices</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/_jit_internal.py#L613"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.adaptive_max_pool1d" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">adaptive_max_pool1d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_indices</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/_jit_internal.py#L613"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.adaptive_max_pool1d" title="Permalink to this definition">¶</a></dt>
 <dd><p>Applies a 1D adaptive max pooling over an input signal composed of
 several input planes.</p>
 <p>See <a class="reference internal" href="torch.nn.AdaptiveMaxPool1d.html#torch.nn.AdaptiveMaxPool1d" title="torch.nn.AdaptiveMaxPool1d"><code class="xref py py-class docutils literal notranslate"><span class="pre">AdaptiveMaxPool1d</span></code></a> for details and output shape.</p>
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@@ -612,7 +612,7 @@
 <h1>torch.nn.functional.adaptive_max_pool2d<a class="headerlink" href="#torch-nn-functional-adaptive-max-pool2d" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.functional.adaptive_max_pool2d">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">adaptive_max_pool2d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_indices</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/_jit_internal.py#L613"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.adaptive_max_pool2d" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">adaptive_max_pool2d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_indices</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/_jit_internal.py#L613"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.adaptive_max_pool2d" title="Permalink to this definition">¶</a></dt>
 <dd><p>Applies a 2D adaptive max pooling over an input signal composed of
 several input planes.</p>
 <p>See <a class="reference internal" href="torch.nn.AdaptiveMaxPool2d.html#torch.nn.AdaptiveMaxPool2d" title="torch.nn.AdaptiveMaxPool2d"><code class="xref py py-class docutils literal notranslate"><span class="pre">AdaptiveMaxPool2d</span></code></a> for details and output shape.</p>
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@@ -612,7 +612,7 @@
 <h1>torch.nn.functional.adaptive_max_pool3d<a class="headerlink" href="#torch-nn-functional-adaptive-max-pool3d" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.functional.adaptive_max_pool3d">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">adaptive_max_pool3d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_indices</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/_jit_internal.py#L613"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.adaptive_max_pool3d" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">adaptive_max_pool3d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_indices</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/_jit_internal.py#L613"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.adaptive_max_pool3d" title="Permalink to this definition">¶</a></dt>
 <dd><p>Applies a 3D adaptive max pooling over an input signal composed of
 several input planes.</p>
 <p>See <a class="reference internal" href="torch.nn.AdaptiveMaxPool3d.html#torch.nn.AdaptiveMaxPool3d" title="torch.nn.AdaptiveMaxPool3d"><code class="xref py py-class docutils literal notranslate"><span class="pre">AdaptiveMaxPool3d</span></code></a> for details and output shape.</p>
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 <h1>torch.nn.functional.affine_grid<a class="headerlink" href="#torch-nn-functional-affine-grid" title="Permalink to this heading">¶</a></h1>
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 <dt class="sig sig-object py" id="torch.nn.functional.affine_grid">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">affine_grid</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">theta</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">align_corners</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/functional.html#affine_grid"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/functional.py#L5026"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.affine_grid" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">affine_grid</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">theta</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">align_corners</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/functional.html#affine_grid"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/functional.py#L5026"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.affine_grid" title="Permalink to this definition">¶</a></dt>
 <dd><p>Generate 2D or 3D flow field (sampling grid), given a batch of affine matrices <code class="xref py py-attr docutils literal notranslate"><span class="pre">theta</span></code>.</p>
 <div class="admonition note">
 <p class="admonition-title">Note</p>
diff --git a/main/generated/torch.nn.functional.alpha_dropout.html b/main/generated/torch.nn.functional.alpha_dropout.html
index 112ce583c6e..71da1fd2d14 100644
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 <h1>torch.nn.functional.alpha_dropout<a class="headerlink" href="#torch-nn-functional-alpha-dropout" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.functional.alpha_dropout">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">alpha_dropout</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">p</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">training</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/functional.html#alpha_dropout"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/functional.py#L1429"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.alpha_dropout" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">alpha_dropout</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">p</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">training</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/functional.html#alpha_dropout"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/functional.py#L1429"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.alpha_dropout" title="Permalink to this definition">¶</a></dt>
 <dd><p>Apply alpha dropout to the input.</p>
 <p>See <a class="reference internal" href="torch.nn.AlphaDropout.html#torch.nn.AlphaDropout" title="torch.nn.AlphaDropout"><code class="xref py py-class docutils literal notranslate"><span class="pre">AlphaDropout</span></code></a> for details.</p>
 <dl class="field-list simple">
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@@ -612,7 +612,7 @@
 <h1>torch.nn.functional.batch_norm<a class="headerlink" href="#torch-nn-functional-batch-norm" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.functional.batch_norm">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">batch_norm</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">running_mean</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">running_var</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">training</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">momentum</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-05</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/functional.html#batch_norm"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/functional.py#L2791"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.batch_norm" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">batch_norm</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">running_mean</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">running_var</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">training</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">momentum</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-05</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/functional.html#batch_norm"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/functional.py#L2791"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.batch_norm" title="Permalink to this definition">¶</a></dt>
 <dd><p>Apply Batch Normalization for each channel across a batch of data.</p>
 <p>See <a class="reference internal" href="torch.nn.BatchNorm1d.html#torch.nn.BatchNorm1d" title="torch.nn.BatchNorm1d"><code class="xref py py-class docutils literal notranslate"><span class="pre">BatchNorm1d</span></code></a>, <a class="reference internal" href="torch.nn.BatchNorm2d.html#torch.nn.BatchNorm2d" title="torch.nn.BatchNorm2d"><code class="xref py py-class docutils literal notranslate"><span class="pre">BatchNorm2d</span></code></a>,
 <a class="reference internal" href="torch.nn.BatchNorm3d.html#torch.nn.BatchNorm3d" title="torch.nn.BatchNorm3d"><code class="xref py py-class docutils literal notranslate"><span class="pre">BatchNorm3d</span></code></a> for details.</p>
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diff --git a/main/generated/torch.nn.functional.binary_cross_entropy.html b/main/generated/torch.nn.functional.binary_cross_entropy.html
index 4feca16632c..ab35b472e27 100644
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@@ -612,7 +612,7 @@
 <h1>torch.nn.functional.binary_cross_entropy<a class="headerlink" href="#torch-nn-functional-binary-cross-entropy" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.functional.binary_cross_entropy">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">binary_cross_entropy</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">size_average</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduce</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mean'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/functional.html#binary_cross_entropy"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/functional.py#L3504"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.binary_cross_entropy" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">binary_cross_entropy</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">size_average</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduce</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mean'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/functional.html#binary_cross_entropy"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/functional.py#L3504"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.binary_cross_entropy" title="Permalink to this definition">¶</a></dt>
 <dd><p>Measure Binary Cross Entropy between the target and input probabilities.</p>
 <p>See <a class="reference internal" href="torch.nn.BCELoss.html#torch.nn.BCELoss" title="torch.nn.BCELoss"><code class="xref py py-class docutils literal notranslate"><span class="pre">BCELoss</span></code></a> for details.</p>
 <dl class="field-list simple">
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 <h1>torch.nn.functional.binary_cross_entropy_with_logits<a class="headerlink" href="#torch-nn-functional-binary-cross-entropy-with-logits" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.functional.binary_cross_entropy_with_logits">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">binary_cross_entropy_with_logits</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">size_average</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduce</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mean'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pos_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/functional.html#binary_cross_entropy_with_logits"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/functional.py#L3572"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.binary_cross_entropy_with_logits" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">binary_cross_entropy_with_logits</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">size_average</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduce</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mean'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pos_weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/functional.html#binary_cross_entropy_with_logits"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/functional.py#L3572"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.binary_cross_entropy_with_logits" title="Permalink to this definition">¶</a></dt>
 <dd><p>Calculate Binary Cross Entropy between target and input logits.</p>
 <p>See <a class="reference internal" href="torch.nn.BCEWithLogitsLoss.html#torch.nn.BCEWithLogitsLoss" title="torch.nn.BCEWithLogitsLoss"><code class="xref py py-class docutils literal notranslate"><span class="pre">BCEWithLogitsLoss</span></code></a> for details.</p>
 <dl class="field-list simple">
diff --git a/main/generated/torch.nn.functional.celu.html b/main/generated/torch.nn.functional.celu.html
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@@ -612,7 +612,7 @@
 <h1>torch.nn.functional.celu<a class="headerlink" href="#torch-nn-functional-celu" title="Permalink to this heading">¶</a></h1>
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-<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">celu</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><span class="pre">Tensor</span></a></span></span><a class="reference internal" href="../_modules/torch/nn/functional.html#celu"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/functional.py#L1849"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.celu" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">celu</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><span class="pre">Tensor</span></a></span></span><a class="reference internal" href="../_modules/torch/nn/functional.html#celu"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/functional.py#L1849"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.celu" title="Permalink to this definition">¶</a></dt>
 <dd><p>Applies element-wise,
 <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mtext>CELU</mtext><mo stretchy="false">(</mo><mi>x</mi><mo stretchy="false">)</mo><mo>=</mo><mi>max</mi><mo>⁡</mo><mo stretchy="false">(</mo><mn>0</mn><mo separator="true">,</mo><mi>x</mi><mo stretchy="false">)</mo><mo>+</mo><mi>min</mi><mo>⁡</mo><mo stretchy="false">(</mo><mn>0</mn><mo separator="true">,</mo><mi>α</mi><mo>∗</mo><mo stretchy="false">(</mo><mi>exp</mi><mo>⁡</mo><mo stretchy="false">(</mo><mi>x</mi><mi mathvariant="normal">/</mi><mi>α</mi><mo stretchy="false">)</mo><mo>−</mo><mn>1</mn><mo stretchy="false">)</mo><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">\text{CELU}(x) = \max(0,x) + \min(0, \alpha * (\exp(x/\alpha) - 1))</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mord text"><span class="mord">CELU</span></span><span class="mopen">(</span><span class="mord mathnormal">x</span><span class="mclose">)</span><span class="mspace" style="margin-right:0.2778em;"></span><span class="mrel">=</span><span class="mspace" style="margin-right:0.2778em;"></span></span><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mop">max</span><span class="mopen">(</span><span class="mord">0</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord mathnormal">x</span><span class="mclose">)</span><span class="mspace" style="margin-right:0.2222em;"></span><span class="mbin">+</span><span class="mspace" style="margin-right:0.2222em;"></span></span><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mop">min</span><span class="mopen">(</span><span class="mord">0</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord mathnormal" style="margin-right:0.0037em;">α</span><span class="mspace" style="margin-right:0.2222em;"></span><span class="mbin">∗</span><span class="mspace" style="margin-right:0.2222em;"></span></span><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mopen">(</span><span class="mop">exp</span><span class="mopen">(</span><span class="mord mathnormal">x</span><span class="mord">/</span><span class="mord mathnormal" style="margin-right:0.0037em;">α</span><span class="mclose">)</span><span class="mspace" style="margin-right:0.2222em;"></span><span class="mbin">−</span><span class="mspace" style="margin-right:0.2222em;"></span></span><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mord">1</span><span class="mclose">))</span></span></span></span></span>.</p>
 <p>See <a class="reference internal" href="torch.nn.CELU.html#torch.nn.CELU" title="torch.nn.CELU"><code class="xref py py-class docutils literal notranslate"><span class="pre">CELU</span></code></a> for more details.</p>
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 <h1>torch.nn.functional.cosine_embedding_loss<a class="headerlink" href="#torch-nn-functional-cosine-embedding-loss" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.functional.cosine_embedding_loss">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">cosine_embedding_loss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">margin</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">size_average</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduce</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mean'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><span class="pre">Tensor</span></a></span></span><a class="reference internal" href="../_modules/torch/nn/functional.html#cosine_embedding_loss"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/functional.py#L4081"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.cosine_embedding_loss" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">cosine_embedding_loss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">margin</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">size_average</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduce</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mean'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><span class="pre">Tensor</span></a></span></span><a class="reference internal" href="../_modules/torch/nn/functional.html#cosine_embedding_loss"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/functional.py#L4081"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.cosine_embedding_loss" title="Permalink to this definition">¶</a></dt>
 <dd><p>See <a class="reference internal" href="torch.nn.CosineEmbeddingLoss.html#torch.nn.CosineEmbeddingLoss" title="torch.nn.CosineEmbeddingLoss"><code class="xref py py-class docutils literal notranslate"><span class="pre">CosineEmbeddingLoss</span></code></a> for details.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
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diff --git a/main/generated/torch.nn.functional.cross_entropy.html b/main/generated/torch.nn.functional.cross_entropy.html
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@@ -612,7 +612,7 @@
 <h1>torch.nn.functional.cross_entropy<a class="headerlink" href="#torch-nn-functional-cross-entropy" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.functional.cross_entropy">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">cross_entropy</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">size_average</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ignore_index</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-100</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduce</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mean'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">label_smoothing</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/functional.html#cross_entropy"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/functional.py#L3404"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.cross_entropy" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">cross_entropy</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">size_average</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ignore_index</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-100</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduce</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mean'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">label_smoothing</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/functional.html#cross_entropy"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/functional.py#L3404"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.cross_entropy" title="Permalink to this definition">¶</a></dt>
 <dd><p>Compute the cross entropy loss between input logits and target.</p>
 <p>See <a class="reference internal" href="torch.nn.CrossEntropyLoss.html#torch.nn.CrossEntropyLoss" title="torch.nn.CrossEntropyLoss"><code class="xref py py-class docutils literal notranslate"><span class="pre">CrossEntropyLoss</span></code></a> for details.</p>
 <dl class="field-list simple">
diff --git a/main/generated/torch.nn.functional.ctc_loss.html b/main/generated/torch.nn.functional.ctc_loss.html
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@@ -612,7 +612,7 @@
 <h1>torch.nn.functional.ctc_loss<a class="headerlink" href="#torch-nn-functional-ctc-loss" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.functional.ctc_loss">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">ctc_loss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">log_probs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">targets</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_lengths</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target_lengths</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">blank</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mean'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">zero_infinity</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/functional.html#ctc_loss"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/functional.py#L3015"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.ctc_loss" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">ctc_loss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">log_probs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">targets</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_lengths</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target_lengths</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">blank</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mean'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">zero_infinity</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/functional.html#ctc_loss"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/functional.py#L3015"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.ctc_loss" title="Permalink to this definition">¶</a></dt>
 <dd><p>Apply the Connectionist Temporal Classification loss.</p>
 <p>See <a class="reference internal" href="torch.nn.CTCLoss.html#torch.nn.CTCLoss" title="torch.nn.CTCLoss"><code class="xref py py-class docutils literal notranslate"><span class="pre">CTCLoss</span></code></a> for details.</p>
 <div class="admonition note">
diff --git a/main/generated/torch.nn.functional.dropout.html b/main/generated/torch.nn.functional.dropout.html
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     <div id="searchBox">
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@@ -612,7 +612,7 @@
 <h1>torch.nn.functional.dropout<a class="headerlink" href="#torch-nn-functional-dropout" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.functional.dropout">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">dropout</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">p</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">training</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/functional.html#dropout"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/functional.py#L1401"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.dropout" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">dropout</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">p</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">training</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/functional.html#dropout"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/functional.py#L1401"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.dropout" title="Permalink to this definition">¶</a></dt>
 <dd><p>During training, randomly zeroes some elements of the input tensor with probability <code class="xref py py-attr docutils literal notranslate"><span class="pre">p</span></code>.</p>
 <p>Uses samples from a Bernoulli distribution.</p>
 <p>See <a class="reference internal" href="torch.nn.Dropout.html#torch.nn.Dropout" title="torch.nn.Dropout"><code class="xref py py-class docutils literal notranslate"><span class="pre">Dropout</span></code></a> for details.</p>
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index 04d59ca3b2f..f3d1669ae2a 100644
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@@ -612,7 +612,7 @@
 <h1>torch.nn.functional.dropout1d<a class="headerlink" href="#torch-nn-functional-dropout1d" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.functional.dropout1d">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">dropout1d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">p</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">training</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/functional.html#dropout1d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/functional.py#L1452"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.dropout1d" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">dropout1d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">p</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">training</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/functional.html#dropout1d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/functional.py#L1452"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.dropout1d" title="Permalink to this definition">¶</a></dt>
 <dd><p>Randomly zero out entire channels (a channel is a 1D feature map).</p>
 <p>For example, the <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>j</mi></mrow><annotation encoding="application/x-tex">j</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.854em;vertical-align:-0.1944em;"></span><span class="mord mathnormal" style="margin-right:0.05724em;">j</span></span></span></span></span>-th channel of the <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>i</mi></mrow><annotation encoding="application/x-tex">i</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.6595em;"></span><span class="mord mathnormal">i</span></span></span></span></span>-th sample in the
 batched input is a 1D tensor <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mtext>input</mtext><mo stretchy="false">[</mo><mi>i</mi><mo separator="true">,</mo><mi>j</mi><mo stretchy="false">]</mo></mrow><annotation encoding="application/x-tex">\text{input}[i, j]</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mord text"><span class="mord">input</span></span><span class="mopen">[</span><span class="mord mathnormal">i</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord mathnormal" style="margin-right:0.05724em;">j</span><span class="mclose">]</span></span></span></span></span> of the input tensor.
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@@ -612,7 +612,7 @@
 <h1>torch.nn.functional.dropout2d<a class="headerlink" href="#torch-nn-functional-dropout2d" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.functional.dropout2d">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">dropout2d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">p</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">training</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/functional.html#dropout2d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/functional.py#L1503"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.dropout2d" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">dropout2d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">p</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">training</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/functional.html#dropout2d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/functional.py#L1503"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.dropout2d" title="Permalink to this definition">¶</a></dt>
 <dd><p>Randomly zero out entire channels (a channel is a 2D feature map).</p>
 <p>For example, the <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>j</mi></mrow><annotation encoding="application/x-tex">j</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.854em;vertical-align:-0.1944em;"></span><span class="mord mathnormal" style="margin-right:0.05724em;">j</span></span></span></span></span>-th channel of the <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>i</mi></mrow><annotation encoding="application/x-tex">i</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.6595em;"></span><span class="mord mathnormal">i</span></span></span></span></span>-th sample in the
 batched input is a 2D tensor <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mtext>input</mtext><mo stretchy="false">[</mo><mi>i</mi><mo separator="true">,</mo><mi>j</mi><mo stretchy="false">]</mo></mrow><annotation encoding="application/x-tex">\text{input}[i, j]</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mord text"><span class="mord">input</span></span><span class="mopen">[</span><span class="mord mathnormal">i</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord mathnormal" style="margin-right:0.05724em;">j</span><span class="mclose">]</span></span></span></span></span> of the input tensor.
diff --git a/main/generated/torch.nn.functional.dropout3d.html b/main/generated/torch.nn.functional.dropout3d.html
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@@ -612,7 +612,7 @@
 <h1>torch.nn.functional.dropout3d<a class="headerlink" href="#torch-nn-functional-dropout3d" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.functional.dropout3d">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">dropout3d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">p</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">training</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/functional.html#dropout3d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/functional.py#L1562"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.dropout3d" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">dropout3d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">p</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">training</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/functional.html#dropout3d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/functional.py#L1562"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.dropout3d" title="Permalink to this definition">¶</a></dt>
 <dd><p>Randomly zero out entire channels (a channel is a 3D feature map).</p>
 <p>For example, the <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>j</mi></mrow><annotation encoding="application/x-tex">j</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.854em;vertical-align:-0.1944em;"></span><span class="mord mathnormal" style="margin-right:0.05724em;">j</span></span></span></span></span>-th channel of the <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>i</mi></mrow><annotation encoding="application/x-tex">i</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.6595em;"></span><span class="mord mathnormal">i</span></span></span></span></span>-th sample in the
 batched input is a 3D tensor <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mtext>input</mtext><mo stretchy="false">[</mo><mi>i</mi><mo separator="true">,</mo><mi>j</mi><mo stretchy="false">]</mo></mrow><annotation encoding="application/x-tex">\text{input}[i, j]</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mord text"><span class="mord">input</span></span><span class="mopen">[</span><span class="mord mathnormal">i</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord mathnormal" style="margin-right:0.05724em;">j</span><span class="mclose">]</span></span></span></span></span> of the input tensor.
diff --git a/main/generated/torch.nn.functional.elu.html b/main/generated/torch.nn.functional.elu.html
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@@ -612,7 +612,7 @@
 <h1>torch.nn.functional.elu<a class="headerlink" href="#torch-nn-functional-elu" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.functional.elu">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">elu</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/functional.html#elu"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/functional.py#L1796"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.elu" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">elu</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/functional.html#elu"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/functional.py#L1796"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.elu" title="Permalink to this definition">¶</a></dt>
 <dd><p>Apply the Exponential Linear Unit (ELU) function element-wise.</p>
 <p>See <a class="reference internal" href="torch.nn.ELU.html#torch.nn.ELU" title="torch.nn.ELU"><code class="xref py py-class docutils literal notranslate"><span class="pre">ELU</span></code></a> for more details.</p>
 <dl class="field-list simple">
diff --git a/main/generated/torch.nn.functional.elu_.html b/main/generated/torch.nn.functional.elu_.html
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diff --git a/main/generated/torch.nn.functional.embedding.html b/main/generated/torch.nn.functional.embedding.html
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@@ -612,7 +612,7 @@
 <h1>torch.nn.functional.embedding<a class="headerlink" href="#torch-nn-functional-embedding" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.functional.embedding">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">embedding</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weight</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding_idx</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_norm</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">norm_type</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale_grad_by_freq</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sparse</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/functional.html#embedding"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/functional.py#L2437"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.embedding" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">embedding</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weight</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding_idx</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_norm</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">norm_type</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale_grad_by_freq</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sparse</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/functional.html#embedding"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/functional.py#L2437"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.embedding" title="Permalink to this definition">¶</a></dt>
 <dd><p>Generate a simple lookup table that looks up embeddings in a fixed dictionary and size.</p>
 <p>This module is often used to retrieve word embeddings using indices.
 The input to the module is a list of indices, and the embedding matrix,
diff --git a/main/generated/torch.nn.functional.embedding_bag.html b/main/generated/torch.nn.functional.embedding_bag.html
index 9be05825d65..aa4a565407d 100644
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@@ -612,7 +612,7 @@
 <h1>torch.nn.functional.embedding_bag<a class="headerlink" href="#torch-nn-functional-embedding-bag" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.functional.embedding_bag">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">embedding_bag</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weight</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">offsets</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_norm</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">norm_type</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale_grad_by_freq</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mean'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sparse</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">per_sample_weights</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">include_last_offset</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding_idx</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/functional.html#embedding_bag"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/functional.py#L2554"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.embedding_bag" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">embedding_bag</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weight</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">offsets</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_norm</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">norm_type</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale_grad_by_freq</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mean'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sparse</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">per_sample_weights</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">include_last_offset</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding_idx</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/functional.html#embedding_bag"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/functional.py#L2554"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.embedding_bag" title="Permalink to this definition">¶</a></dt>
 <dd><p>Compute sums, means or maxes of <cite>bags</cite> of embeddings.</p>
 <p>Calculation is done without instantiating the intermediate embeddings.
 See <a class="reference internal" href="torch.nn.EmbeddingBag.html#torch.nn.EmbeddingBag" title="torch.nn.EmbeddingBag"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.nn.EmbeddingBag</span></code></a> for more details.</p>
diff --git a/main/generated/torch.nn.functional.feature_alpha_dropout.html b/main/generated/torch.nn.functional.feature_alpha_dropout.html
index c3aa9b20cd8..9067f5f3a46 100644
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+++ b/main/generated/torch.nn.functional.feature_alpha_dropout.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
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-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
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     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.nn.functional.feature_alpha_dropout<a class="headerlink" href="#torch-nn-functional-feature-alpha-dropout" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.functional.feature_alpha_dropout">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">feature_alpha_dropout</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">p</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">training</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/functional.html#feature_alpha_dropout"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/functional.py#L1614"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.feature_alpha_dropout" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">feature_alpha_dropout</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">p</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">training</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/functional.html#feature_alpha_dropout"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/functional.py#L1614"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.feature_alpha_dropout" title="Permalink to this definition">¶</a></dt>
 <dd><p>Randomly masks out entire channels (a channel is a feature map).</p>
 <p>For example, the <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>j</mi></mrow><annotation encoding="application/x-tex">j</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.854em;vertical-align:-0.1944em;"></span><span class="mord mathnormal" style="margin-right:0.05724em;">j</span></span></span></span></span>-th channel of the <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>i</mi></mrow><annotation encoding="application/x-tex">i</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.6595em;"></span><span class="mord mathnormal">i</span></span></span></span></span>-th sample in the batch input
 is a tensor <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mtext>input</mtext><mo stretchy="false">[</mo><mi>i</mi><mo separator="true">,</mo><mi>j</mi><mo stretchy="false">]</mo></mrow><annotation encoding="application/x-tex">\text{input}[i, j]</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mord text"><span class="mord">input</span></span><span class="mopen">[</span><span class="mord mathnormal">i</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord mathnormal" style="margin-right:0.05724em;">j</span><span class="mclose">]</span></span></span></span></span> of the input tensor. Instead of
diff --git a/main/generated/torch.nn.functional.fold.html b/main/generated/torch.nn.functional.fold.html
index 77a5fead2dc..77e708b81dc 100644
--- a/main/generated/torch.nn.functional.fold.html
+++ b/main/generated/torch.nn.functional.fold.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
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@@ -612,7 +612,7 @@
 <h1>torch.nn.functional.fold<a class="headerlink" href="#torch-nn-functional-fold" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.functional.fold">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">fold</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dilation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/functional.html#fold"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/functional.py#L5535"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.fold" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">fold</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dilation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/functional.html#fold"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/functional.py#L5535"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.fold" title="Permalink to this definition">¶</a></dt>
 <dd><p>Combine an array of sliding local blocks into a large containing tensor.</p>
 <div class="admonition warning">
 <p class="admonition-title">Warning</p>
diff --git a/main/generated/torch.nn.functional.fractional_max_pool2d.html b/main/generated/torch.nn.functional.fractional_max_pool2d.html
index b1e6892421d..97b39761cc9 100644
--- a/main/generated/torch.nn.functional.fractional_max_pool2d.html
+++ b/main/generated/torch.nn.functional.fractional_max_pool2d.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.nn.functional.fractional_max_pool2d<a class="headerlink" href="#torch-nn-functional-fractional-max-pool2d" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.functional.fractional_max_pool2d">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">fractional_max_pool2d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_ratio</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_indices</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_random_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/_jit_internal.py#L613"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.fractional_max_pool2d" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">fractional_max_pool2d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_ratio</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_indices</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_random_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/_jit_internal.py#L613"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.fractional_max_pool2d" title="Permalink to this definition">¶</a></dt>
 <dd><p>Applies 2D fractional max pooling over an input signal composed of several input planes.</p>
 <p>Fractional MaxPooling is described in detail in the paper <a class="reference external" href="http://arxiv.org/abs/1412.6071">Fractional MaxPooling</a> by Ben Graham</p>
 <p>The max-pooling operation is applied in <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>k</mi><mi>H</mi><mo>×</mo><mi>k</mi><mi>W</mi></mrow><annotation encoding="application/x-tex">kH \times kW</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.7778em;vertical-align:-0.0833em;"></span><span class="mord mathnormal" style="margin-right:0.03148em;">k</span><span class="mord mathnormal" style="margin-right:0.08125em;">H</span><span class="mspace" style="margin-right:0.2222em;"></span><span class="mbin">×</span><span class="mspace" style="margin-right:0.2222em;"></span></span><span class="base"><span class="strut" style="height:0.6944em;"></span><span class="mord mathnormal" style="margin-right:0.13889em;">kW</span></span></span></span></span> regions by a stochastic
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@@ -612,7 +612,7 @@
 <h1>torch.nn.functional.fractional_max_pool3d<a class="headerlink" href="#torch-nn-functional-fractional-max-pool3d" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.functional.fractional_max_pool3d">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">fractional_max_pool3d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_ratio</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_indices</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_random_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/_jit_internal.py#L613"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.fractional_max_pool3d" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">fractional_max_pool3d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_ratio</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_indices</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_random_samples</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/_jit_internal.py#L613"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.fractional_max_pool3d" title="Permalink to this definition">¶</a></dt>
 <dd><p>Applies 3D fractional max pooling over an input signal composed of several input planes.</p>
 <p>Fractional MaxPooling is described in detail in the paper <a class="reference external" href="http://arxiv.org/abs/1412.6071">Fractional MaxPooling</a> by Ben Graham</p>
 <p>The max-pooling operation is applied in <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>k</mi><mi>T</mi><mo>×</mo><mi>k</mi><mi>H</mi><mo>×</mo><mi>k</mi><mi>W</mi></mrow><annotation encoding="application/x-tex">kT \times kH \times kW</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.7778em;vertical-align:-0.0833em;"></span><span class="mord mathnormal" style="margin-right:0.03148em;">k</span><span class="mord mathnormal" style="margin-right:0.13889em;">T</span><span class="mspace" style="margin-right:0.2222em;"></span><span class="mbin">×</span><span class="mspace" style="margin-right:0.2222em;"></span></span><span class="base"><span class="strut" style="height:0.7778em;vertical-align:-0.0833em;"></span><span class="mord mathnormal" style="margin-right:0.03148em;">k</span><span class="mord mathnormal" style="margin-right:0.08125em;">H</span><span class="mspace" style="margin-right:0.2222em;"></span><span class="mbin">×</span><span class="mspace" style="margin-right:0.2222em;"></span></span><span class="base"><span class="strut" style="height:0.6944em;"></span><span class="mord mathnormal" style="margin-right:0.13889em;">kW</span></span></span></span></span> regions by a stochastic
diff --git a/main/generated/torch.nn.functional.gaussian_nll_loss.html b/main/generated/torch.nn.functional.gaussian_nll_loss.html
index d8e4a7fbec9..3dc439bd015 100644
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+++ b/main/generated/torch.nn.functional.gaussian_nll_loss.html
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@@ -612,7 +612,7 @@
 <h1>torch.nn.functional.gaussian_nll_loss<a class="headerlink" href="#torch-nn-functional-gaussian-nll-loss" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.functional.gaussian_nll_loss">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">gaussian_nll_loss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">var</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">full</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-06</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mean'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/functional.html#gaussian_nll_loss"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/functional.py#L3230"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.gaussian_nll_loss" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">gaussian_nll_loss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">var</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">full</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-06</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mean'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/functional.html#gaussian_nll_loss"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/functional.py#L3230"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.gaussian_nll_loss" title="Permalink to this definition">¶</a></dt>
 <dd><p>Gaussian negative log likelihood loss.</p>
 <p>See <a class="reference internal" href="torch.nn.GaussianNLLLoss.html#torch.nn.GaussianNLLLoss" title="torch.nn.GaussianNLLLoss"><code class="xref py py-class docutils literal notranslate"><span class="pre">GaussianNLLLoss</span></code></a> for details.</p>
 <dl class="field-list simple">
diff --git a/main/generated/torch.nn.functional.gelu.html b/main/generated/torch.nn.functional.gelu.html
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diff --git a/main/generated/torch.nn.functional.glu.html b/main/generated/torch.nn.functional.glu.html
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@@ -612,7 +612,7 @@
 <h1>torch.nn.functional.glu<a class="headerlink" href="#torch-nn-functional-glu" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.functional.glu">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">glu</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-1</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><span class="pre">Tensor</span></a></span></span><a class="reference internal" href="../_modules/torch/nn/functional.html#glu"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/functional.py#L1718"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.glu" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">glu</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-1</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><span class="pre">Tensor</span></a></span></span><a class="reference internal" href="../_modules/torch/nn/functional.html#glu"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/functional.py#L1718"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.glu" title="Permalink to this definition">¶</a></dt>
 <dd><p>The gated linear unit. Computes:</p>
 <div class="math">
 <span class="katex-display"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML" display="block"><semantics><mrow><mtext>GLU</mtext><mo stretchy="false">(</mo><mi>a</mi><mo separator="true">,</mo><mi>b</mi><mo stretchy="false">)</mo><mo>=</mo><mi>a</mi><mo>⊗</mo><mi>σ</mi><mo stretchy="false">(</mo><mi>b</mi><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">\text{GLU}(a, b) = a \otimes \sigma(b)
diff --git a/main/generated/torch.nn.functional.grid_sample.html b/main/generated/torch.nn.functional.grid_sample.html
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     </div>
     <div id="searchBox">
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@@ -612,7 +612,7 @@
 <h1>torch.nn.functional.grid_sample<a class="headerlink" href="#torch-nn-functional-grid-sample" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.functional.grid_sample">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">grid_sample</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">grid</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'bilinear'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'zeros'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">align_corners</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/functional.html#grid_sample"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/functional.py#L4867"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.grid_sample" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">grid_sample</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">grid</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'bilinear'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'zeros'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">align_corners</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/functional.html#grid_sample"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/functional.py#L4867"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.grid_sample" title="Permalink to this definition">¶</a></dt>
 <dd><p>Compute grid sample.</p>
 <p>Given an <code class="xref py py-attr docutils literal notranslate"><span class="pre">input</span></code> and a flow-field <code class="xref py py-attr docutils literal notranslate"><span class="pre">grid</span></code>, computes the
 <code class="docutils literal notranslate"><span class="pre">output</span></code> using <code class="xref py py-attr docutils literal notranslate"><span class="pre">input</span></code> values and pixel locations from <code class="xref py py-attr docutils literal notranslate"><span class="pre">grid</span></code>.</p>
diff --git a/main/generated/torch.nn.functional.group_norm.html b/main/generated/torch.nn.functional.group_norm.html
index 708f5d756db..6cd6a7401a9 100644
--- a/main/generated/torch.nn.functional.group_norm.html
+++ b/main/generated/torch.nn.functional.group_norm.html
@@ -300,7 +300,7 @@
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     </div>
     <div id="searchBox">
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@@ -612,7 +612,7 @@
 <h1>torch.nn.functional.group_norm<a class="headerlink" href="#torch-nn-functional-group-norm" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.functional.group_norm">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">group_norm</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_groups</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-05</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/functional.html#group_norm"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/functional.py#L2932"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.group_norm" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">group_norm</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_groups</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-05</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/functional.html#group_norm"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/functional.py#L2932"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.group_norm" title="Permalink to this definition">¶</a></dt>
 <dd><p>Apply Group Normalization for last certain number of dimensions.</p>
 <p>See <a class="reference internal" href="torch.nn.GroupNorm.html#torch.nn.GroupNorm" title="torch.nn.GroupNorm"><code class="xref py py-class docutils literal notranslate"><span class="pre">GroupNorm</span></code></a> for details.</p>
 <dl class="field-list simple">
diff --git a/main/generated/torch.nn.functional.gumbel_softmax.html b/main/generated/torch.nn.functional.gumbel_softmax.html
index a49422f2002..7c8e6bd8d67 100644
--- a/main/generated/torch.nn.functional.gumbel_softmax.html
+++ b/main/generated/torch.nn.functional.gumbel_softmax.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
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     <div id="searchBox">
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@@ -612,7 +612,7 @@
 <h1>torch.nn.functional.gumbel_softmax<a class="headerlink" href="#torch-nn-functional-gumbel-softmax" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.functional.gumbel_softmax">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">gumbel_softmax</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">logits</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tau</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">hard</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-1</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/functional.html#gumbel_softmax"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/functional.py#L2146"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.gumbel_softmax" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">gumbel_softmax</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">logits</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tau</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">hard</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-1</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/functional.html#gumbel_softmax"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/functional.py#L2146"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.gumbel_softmax" title="Permalink to this definition">¶</a></dt>
 <dd><p>Sample from the Gumbel-Softmax distribution (<a class="reference external" href="https://arxiv.org/abs/1611.00712">Link 1</a>  <a class="reference external" href="https://arxiv.org/abs/1611.01144">Link 2</a>) and optionally discretize.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.nn.functional.hardshrink.html b/main/generated/torch.nn.functional.hardshrink.html
index 2bc731e7326..5eec1f60de0 100644
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@@ -300,7 +300,7 @@
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diff --git a/main/generated/torch.nn.functional.hardsigmoid.html b/main/generated/torch.nn.functional.hardsigmoid.html
index 4d10fd91e6d..2bb0000d525 100644
--- a/main/generated/torch.nn.functional.hardsigmoid.html
+++ b/main/generated/torch.nn.functional.hardsigmoid.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
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-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
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     </div>
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@@ -612,7 +612,7 @@
 <h1>torch.nn.functional.hardsigmoid<a class="headerlink" href="#torch-nn-functional-hardsigmoid" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.functional.hardsigmoid">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">hardsigmoid</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/functional.html#hardsigmoid"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/functional.py#L2287"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.hardsigmoid" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">hardsigmoid</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/functional.html#hardsigmoid"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/functional.py#L2287"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.hardsigmoid" title="Permalink to this definition">¶</a></dt>
 <dd><p>Apply the Hardsigmoid function element-wise.</p>
 <div class="math">
 <span class="katex-display"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML" display="block"><semantics><mrow><mtext>Hardsigmoid</mtext><mo stretchy="false">(</mo><mi>x</mi><mo stretchy="false">)</mo><mo>=</mo><mrow><mo fence="true">{</mo><mtable rowspacing="0.36em" columnalign="left left" columnspacing="1em"><mtr><mtd><mstyle scriptlevel="0" displaystyle="false"><mn>0</mn></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="false"><mrow><mtext>if </mtext><mi>x</mi><mo>≤</mo><mo>−</mo><mn>3</mn><mo separator="true">,</mo></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="false"><mn>1</mn></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="false"><mrow><mtext>if </mtext><mi>x</mi><mo>≥</mo><mo>+</mo><mn>3</mn><mo separator="true">,</mo></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="false"><mrow><mi>x</mi><mi mathvariant="normal">/</mi><mn>6</mn><mo>+</mo><mn>1</mn><mi mathvariant="normal">/</mi><mn>2</mn></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="false"><mtext>otherwise</mtext></mstyle></mtd></mtr></mtable></mrow></mrow><annotation encoding="application/x-tex">\text{Hardsigmoid}(x) = \begin{cases}
diff --git a/main/generated/torch.nn.functional.hardswish.html b/main/generated/torch.nn.functional.hardswish.html
index 6ed1eb47ab7..d9ce75f9bd7 100644
--- a/main/generated/torch.nn.functional.hardswish.html
+++ b/main/generated/torch.nn.functional.hardswish.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
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@@ -612,7 +612,7 @@
 <h1>torch.nn.functional.hardswish<a class="headerlink" href="#torch-nn-functional-hardswish" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.functional.hardswish">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">hardswish</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/functional.html#hardswish"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/functional.py#L2403"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.hardswish" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">hardswish</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/functional.html#hardswish"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/functional.py#L2403"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.hardswish" title="Permalink to this definition">¶</a></dt>
 <dd><p>Apply hardswish function, element-wise.</p>
 <p>Follows implementation as described in the paper:
 <a class="reference external" href="https://arxiv.org/abs/1905.02244">Searching for MobileNetV3</a>.</p>
diff --git a/main/generated/torch.nn.functional.hardtanh.html b/main/generated/torch.nn.functional.hardtanh.html
index 1d758a0f90c..f0b23f442b1 100644
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+++ b/main/generated/torch.nn.functional.hardtanh.html
@@ -300,7 +300,7 @@
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@@ -612,7 +612,7 @@
 <h1>torch.nn.functional.hardtanh<a class="headerlink" href="#torch-nn-functional-hardtanh" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.functional.hardtanh">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">hardtanh</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_val</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-1.</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_val</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><span class="pre">Tensor</span></a></span></span><a class="reference internal" href="../_modules/torch/nn/functional.html#hardtanh"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/functional.py#L1745"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.hardtanh" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">hardtanh</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_val</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-1.</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_val</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><span class="pre">Tensor</span></a></span></span><a class="reference internal" href="../_modules/torch/nn/functional.html#hardtanh"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/functional.py#L1745"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.hardtanh" title="Permalink to this definition">¶</a></dt>
 <dd><p>Applies the HardTanh function element-wise. See <a class="reference internal" href="torch.nn.Hardtanh.html#torch.nn.Hardtanh" title="torch.nn.Hardtanh"><code class="xref py py-class docutils literal notranslate"><span class="pre">Hardtanh</span></code></a> for more
 details.</p>
 <dl class="field-list simple">
diff --git a/main/generated/torch.nn.functional.hardtanh_.html b/main/generated/torch.nn.functional.hardtanh_.html
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+++ b/main/generated/torch.nn.functional.hardtanh_.html
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diff --git a/main/generated/torch.nn.functional.hinge_embedding_loss.html b/main/generated/torch.nn.functional.hinge_embedding_loss.html
index 54902b24484..a1a1554f8ba 100644
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     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.nn.functional.hinge_embedding_loss<a class="headerlink" href="#torch-nn-functional-hinge-embedding-loss" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.functional.hinge_embedding_loss">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">hinge_embedding_loss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">margin</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">size_average</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduce</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mean'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><span class="pre">Tensor</span></a></span></span><a class="reference internal" href="../_modules/torch/nn/functional.html#hinge_embedding_loss"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/functional.py#L3947"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.hinge_embedding_loss" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">hinge_embedding_loss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">margin</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">size_average</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduce</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mean'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><span class="pre">Tensor</span></a></span></span><a class="reference internal" href="../_modules/torch/nn/functional.html#hinge_embedding_loss"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/functional.py#L3947"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.hinge_embedding_loss" title="Permalink to this definition">¶</a></dt>
 <dd><p>See <a class="reference internal" href="torch.nn.HingeEmbeddingLoss.html#torch.nn.HingeEmbeddingLoss" title="torch.nn.HingeEmbeddingLoss"><code class="xref py py-class docutils literal notranslate"><span class="pre">HingeEmbeddingLoss</span></code></a> for details.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
diff --git a/main/generated/torch.nn.functional.huber_loss.html b/main/generated/torch.nn.functional.huber_loss.html
index c0b8ffe6e61..a3b4da7b3d0 100644
--- a/main/generated/torch.nn.functional.huber_loss.html
+++ b/main/generated/torch.nn.functional.huber_loss.html
@@ -300,7 +300,7 @@
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     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
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     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.nn.functional.huber_loss<a class="headerlink" href="#torch-nn-functional-huber-loss" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.functional.huber_loss">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">huber_loss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mean'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">delta</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><span class="pre">Tensor</span></a></span></span><a class="reference internal" href="../_modules/torch/nn/functional.html#huber_loss"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/functional.py#L3696"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.huber_loss" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">huber_loss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mean'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">delta</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><span class="pre">Tensor</span></a></span></span><a class="reference internal" href="../_modules/torch/nn/functional.html#huber_loss"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/functional.py#L3696"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.huber_loss" title="Permalink to this definition">¶</a></dt>
 <dd><p>Computes the Huber loss, with optional weighting.</p>
 <p>Function uses a squared term if the absolute
 element-wise error falls below delta and a delta-scaled L1 term otherwise.</p>
diff --git a/main/generated/torch.nn.functional.instance_norm.html b/main/generated/torch.nn.functional.instance_norm.html
index 0409665c6de..c17b82ae24a 100644
--- a/main/generated/torch.nn.functional.instance_norm.html
+++ b/main/generated/torch.nn.functional.instance_norm.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.nn.functional.instance_norm<a class="headerlink" href="#torch-nn-functional-instance-norm" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.functional.instance_norm">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">instance_norm</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">running_mean</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">running_var</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_input_stats</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">momentum</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-05</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/functional.html#instance_norm"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/functional.py#L2846"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.instance_norm" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">instance_norm</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">running_mean</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">running_var</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_input_stats</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">momentum</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-05</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/functional.html#instance_norm"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/functional.py#L2846"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.instance_norm" title="Permalink to this definition">¶</a></dt>
 <dd><p>Apply Instance Normalization independently for each channel in every data sample within a batch.</p>
 <p>See <a class="reference internal" href="torch.nn.InstanceNorm1d.html#torch.nn.InstanceNorm1d" title="torch.nn.InstanceNorm1d"><code class="xref py py-class docutils literal notranslate"><span class="pre">InstanceNorm1d</span></code></a>, <a class="reference internal" href="torch.nn.InstanceNorm2d.html#torch.nn.InstanceNorm2d" title="torch.nn.InstanceNorm2d"><code class="xref py py-class docutils literal notranslate"><span class="pre">InstanceNorm2d</span></code></a>,
 <a class="reference internal" href="torch.nn.InstanceNorm3d.html#torch.nn.InstanceNorm3d" title="torch.nn.InstanceNorm3d"><code class="xref py py-class docutils literal notranslate"><span class="pre">InstanceNorm3d</span></code></a> for details.</p>
diff --git a/main/generated/torch.nn.functional.interpolate.html b/main/generated/torch.nn.functional.interpolate.html
index d787611d337..ae1b084bc4d 100644
--- a/main/generated/torch.nn.functional.interpolate.html
+++ b/main/generated/torch.nn.functional.interpolate.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.nn.functional.interpolate<a class="headerlink" href="#torch-nn-functional-interpolate" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.functional.interpolate">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">interpolate</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale_factor</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'nearest'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">align_corners</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">recompute_scale_factor</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">antialias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/functional.html#interpolate"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/functional.py#L4453"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.interpolate" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">interpolate</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale_factor</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'nearest'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">align_corners</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">recompute_scale_factor</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">antialias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/functional.html#interpolate"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/functional.py#L4453"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.interpolate" title="Permalink to this definition">¶</a></dt>
 <dd><p>Down/up samples the input.</p>
 <p>Tensor interpolated to either the given <code class="xref py py-attr docutils literal notranslate"><span class="pre">size</span></code> or the given
 <code class="xref py py-attr docutils literal notranslate"><span class="pre">scale_factor</span></code></p>
diff --git a/main/generated/torch.nn.functional.kl_div.html b/main/generated/torch.nn.functional.kl_div.html
index 7b7a42ecca3..6e65ab2d137 100644
--- a/main/generated/torch.nn.functional.kl_div.html
+++ b/main/generated/torch.nn.functional.kl_div.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.nn.functional.kl_div<a class="headerlink" href="#torch-nn-functional-kl-div" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.functional.kl_div">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">kl_div</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">size_average</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduce</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mean'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">log_target</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/functional.html#kl_div"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/functional.py#L3321"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.kl_div" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">kl_div</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">size_average</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduce</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mean'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">log_target</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/functional.html#kl_div"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/functional.py#L3321"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.kl_div" title="Permalink to this definition">¶</a></dt>
 <dd><p>Compute the KL Divergence loss.</p>
 <p>Refer - The <a class="reference external" href="https://en.wikipedia.org/wiki/Kullback-Leibler_divergence">Kullback-Leibler divergence Loss</a></p>
 <p>See <a class="reference internal" href="torch.nn.KLDivLoss.html#torch.nn.KLDivLoss" title="torch.nn.KLDivLoss"><code class="xref py py-class docutils literal notranslate"><span class="pre">KLDivLoss</span></code></a> for details.</p>
diff --git a/main/generated/torch.nn.functional.l1_loss.html b/main/generated/torch.nn.functional.l1_loss.html
index c85c1724f52..6a275e165c2 100644
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 <h1>torch.nn.functional.l1_loss<a class="headerlink" href="#torch-nn-functional-l1-loss" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.functional.l1_loss">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">l1_loss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">size_average</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduce</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mean'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><span class="pre">Tensor</span></a></span></span><a class="reference internal" href="../_modules/torch/nn/functional.html#l1_loss"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/functional.py#L3776"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.l1_loss" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">l1_loss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">size_average</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduce</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mean'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><span class="pre">Tensor</span></a></span></span><a class="reference internal" href="../_modules/torch/nn/functional.html#l1_loss"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/functional.py#L3776"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.l1_loss" title="Permalink to this definition">¶</a></dt>
 <dd><p>Function that takes the mean element-wise absolute value difference.</p>
 <p>See <a class="reference internal" href="torch.nn.L1Loss.html#torch.nn.L1Loss" title="torch.nn.L1Loss"><code class="xref py py-class docutils literal notranslate"><span class="pre">L1Loss</span></code></a> for details.</p>
 <dl class="field-list simple">
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@@ -612,7 +612,7 @@
 <h1>torch.nn.functional.layer_norm<a class="headerlink" href="#torch-nn-functional-layer-norm" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.functional.layer_norm">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">layer_norm</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">normalized_shape</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-05</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/functional.html#layer_norm"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/functional.py#L2889"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.layer_norm" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">layer_norm</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">normalized_shape</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bias</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-05</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/functional.html#layer_norm"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/functional.py#L2889"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.layer_norm" title="Permalink to this definition">¶</a></dt>
 <dd><p>Apply Layer Normalization for last certain number of dimensions.</p>
 <p>See <a class="reference internal" href="torch.nn.LayerNorm.html#torch.nn.LayerNorm" title="torch.nn.LayerNorm"><code class="xref py py-class docutils literal notranslate"><span class="pre">LayerNorm</span></code></a> for details.</p>
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@@ -612,7 +612,7 @@
 <h1>torch.nn.functional.leaky_relu<a class="headerlink" href="#torch-nn-functional-leaky-relu" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.functional.leaky_relu">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">leaky_relu</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">negative_slope</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.01</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><span class="pre">Tensor</span></a></span></span><a class="reference internal" href="../_modules/torch/nn/functional.html#leaky_relu"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/functional.py#L1882"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.leaky_relu" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">leaky_relu</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">negative_slope</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.01</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><span class="pre">Tensor</span></a></span></span><a class="reference internal" href="../_modules/torch/nn/functional.html#leaky_relu"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/functional.py#L1882"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.leaky_relu" title="Permalink to this definition">¶</a></dt>
 <dd><p>Applies element-wise,
 <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mtext>LeakyReLU</mtext><mo stretchy="false">(</mo><mi>x</mi><mo stretchy="false">)</mo><mo>=</mo><mi>max</mi><mo>⁡</mo><mo stretchy="false">(</mo><mn>0</mn><mo separator="true">,</mo><mi>x</mi><mo stretchy="false">)</mo><mo>+</mo><mtext>negative_slope</mtext><mo>∗</mo><mi>min</mi><mo>⁡</mo><mo stretchy="false">(</mo><mn>0</mn><mo separator="true">,</mo><mi>x</mi><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">\text{LeakyReLU}(x) = \max(0, x) + \text{negative\_slope} * \min(0, x)</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mord text"><span class="mord">LeakyReLU</span></span><span class="mopen">(</span><span class="mord mathnormal">x</span><span class="mclose">)</span><span class="mspace" style="margin-right:0.2778em;"></span><span class="mrel">=</span><span class="mspace" style="margin-right:0.2778em;"></span></span><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mop">max</span><span class="mopen">(</span><span class="mord">0</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord mathnormal">x</span><span class="mclose">)</span><span class="mspace" style="margin-right:0.2222em;"></span><span class="mbin">+</span><span class="mspace" style="margin-right:0.2222em;"></span></span><span class="base"><span class="strut" style="height:1.0044em;vertical-align:-0.31em;"></span><span class="mord text"><span class="mord">negative_slope</span></span><span class="mspace" style="margin-right:0.2222em;"></span><span class="mbin">∗</span><span class="mspace" style="margin-right:0.2222em;"></span></span><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mop">min</span><span class="mopen">(</span><span class="mord">0</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord mathnormal">x</span><span class="mclose">)</span></span></span></span></span></p>
 <p>See <a class="reference internal" href="torch.nn.LeakyReLU.html#torch.nn.LeakyReLU" title="torch.nn.LeakyReLU"><code class="xref py py-class docutils literal notranslate"><span class="pre">LeakyReLU</span></code></a> for more details.</p>
diff --git a/main/generated/torch.nn.functional.leaky_relu_.html b/main/generated/torch.nn.functional.leaky_relu_.html
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     <div id="searchBox">
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diff --git a/main/generated/torch.nn.functional.linear.html b/main/generated/torch.nn.functional.linear.html
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diff --git a/main/generated/torch.nn.functional.local_response_norm.html b/main/generated/torch.nn.functional.local_response_norm.html
index c7122dc17de..cb0dbcefc4a 100644
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@@ -612,7 +612,7 @@
 <h1>torch.nn.functional.local_response_norm<a class="headerlink" href="#torch-nn-functional-local-response-norm" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.functional.local_response_norm">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">local_response_norm</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">beta</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.75</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">k</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/functional.html#local_response_norm"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/functional.py#L2970"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.local_response_norm" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">local_response_norm</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">beta</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.75</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">k</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/functional.html#local_response_norm"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/functional.py#L2970"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.local_response_norm" title="Permalink to this definition">¶</a></dt>
 <dd><p>Apply local response normalization over an input signal.</p>
 <p>The input signal is composed of several input planes, where channels occupy the second dimension.
 Normalization is applied across channels.</p>
diff --git a/main/generated/torch.nn.functional.log_softmax.html b/main/generated/torch.nn.functional.log_softmax.html
index ad4af7adf97..e9633d00f2a 100644
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@@ -612,7 +612,7 @@
 <h1>torch.nn.functional.log_softmax<a class="headerlink" href="#torch-nn-functional-log-softmax" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.functional.log_softmax">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">log_softmax</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_stacklevel</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">3</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/functional.html#log_softmax"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/functional.py#L2220"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.log_softmax" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">log_softmax</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_stacklevel</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">3</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/functional.html#log_softmax"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/functional.py#L2220"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.log_softmax" title="Permalink to this definition">¶</a></dt>
 <dd><p>Apply a softmax followed by a logarithm.</p>
 <p>While mathematically equivalent to log(softmax(x)), doing these two
 operations separately is slower and numerically unstable. This function
diff --git a/main/generated/torch.nn.functional.logsigmoid.html b/main/generated/torch.nn.functional.logsigmoid.html
index 3cd8ce00415..5c9b18fa370 100644
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diff --git a/main/generated/torch.nn.functional.lp_pool1d.html b/main/generated/torch.nn.functional.lp_pool1d.html
index f23041f6e4a..6498b0dbf85 100644
--- a/main/generated/torch.nn.functional.lp_pool1d.html
+++ b/main/generated/torch.nn.functional.lp_pool1d.html
@@ -300,7 +300,7 @@
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     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
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     </div>
     <div id="searchBox">
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@@ -612,7 +612,7 @@
 <h1>torch.nn.functional.lp_pool1d<a class="headerlink" href="#torch-nn-functional-lp-pool1d" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.functional.lp_pool1d">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">lp_pool1d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">norm_type</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ceil_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/functional.html#lp_pool1d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/functional.py#L1150"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.lp_pool1d" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">lp_pool1d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">norm_type</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ceil_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/functional.html#lp_pool1d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/functional.py#L1150"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.lp_pool1d" title="Permalink to this definition">¶</a></dt>
 <dd><p>Apply a 1D power-average pooling over an input signal composed of several input planes.</p>
 <p>If the sum of all inputs to the power of <cite>p</cite> is
 zero, the gradient is set to zero as well.</p>
diff --git a/main/generated/torch.nn.functional.lp_pool2d.html b/main/generated/torch.nn.functional.lp_pool2d.html
index 610e3facd21..04ce3959be1 100644
--- a/main/generated/torch.nn.functional.lp_pool2d.html
+++ b/main/generated/torch.nn.functional.lp_pool2d.html
@@ -300,7 +300,7 @@
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     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
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     </div>
     <div id="searchBox">
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@@ -612,7 +612,7 @@
 <h1>torch.nn.functional.lp_pool2d<a class="headerlink" href="#torch-nn-functional-lp-pool2d" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.functional.lp_pool2d">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">lp_pool2d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">norm_type</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ceil_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/functional.html#lp_pool2d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/functional.py#L1114"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.lp_pool2d" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">lp_pool2d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">norm_type</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ceil_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/functional.html#lp_pool2d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/functional.py#L1114"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.lp_pool2d" title="Permalink to this definition">¶</a></dt>
 <dd><p>Apply a 2D power-average pooling over an input signal composed of several input planes.</p>
 <p>If the sum of all inputs to the power of <cite>p</cite> is
 zero, the gradient is set to zero as well.</p>
diff --git a/main/generated/torch.nn.functional.lp_pool3d.html b/main/generated/torch.nn.functional.lp_pool3d.html
index 7b962e3e4f3..277927cc479 100644
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+++ b/main/generated/torch.nn.functional.lp_pool3d.html
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     <div id="searchBox">
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@@ -612,7 +612,7 @@
 <h1>torch.nn.functional.lp_pool3d<a class="headerlink" href="#torch-nn-functional-lp-pool3d" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.functional.lp_pool3d">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">lp_pool3d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">norm_type</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ceil_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/functional.html#lp_pool3d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/functional.py#L1076"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.lp_pool3d" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">lp_pool3d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">norm_type</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ceil_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/functional.html#lp_pool3d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/functional.py#L1076"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.lp_pool3d" title="Permalink to this definition">¶</a></dt>
 <dd><p>Apply a 3D power-average pooling over an input signal composed of several input planes.</p>
 <p>If the sum of all inputs to the power of <cite>p</cite> is
 zero, the gradient is set to zero as well.</p>
diff --git a/main/generated/torch.nn.functional.margin_ranking_loss.html b/main/generated/torch.nn.functional.margin_ranking_loss.html
index 3aa1e915234..9d10980cbda 100644
--- a/main/generated/torch.nn.functional.margin_ranking_loss.html
+++ b/main/generated/torch.nn.functional.margin_ranking_loss.html
@@ -300,7 +300,7 @@
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     <div id="searchBox">
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@@ -612,7 +612,7 @@
 <h1>torch.nn.functional.margin_ranking_loss<a class="headerlink" href="#torch-nn-functional-margin-ranking-loss" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.functional.margin_ranking_loss">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">margin_ranking_loss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">margin</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">size_average</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduce</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mean'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><span class="pre">Tensor</span></a></span></span><a class="reference internal" href="../_modules/torch/nn/functional.html#margin_ranking_loss"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/functional.py#L3910"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.margin_ranking_loss" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">margin_ranking_loss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">margin</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">size_average</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduce</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mean'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><span class="pre">Tensor</span></a></span></span><a class="reference internal" href="../_modules/torch/nn/functional.html#margin_ranking_loss"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/functional.py#L3910"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.margin_ranking_loss" title="Permalink to this definition">¶</a></dt>
 <dd><p>See <a class="reference internal" href="torch.nn.MarginRankingLoss.html#torch.nn.MarginRankingLoss" title="torch.nn.MarginRankingLoss"><code class="xref py py-class docutils literal notranslate"><span class="pre">MarginRankingLoss</span></code></a> for details.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
diff --git a/main/generated/torch.nn.functional.max_pool1d.html b/main/generated/torch.nn.functional.max_pool1d.html
index 463f33e3b91..a20a8f709e9 100644
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@@ -612,7 +612,7 @@
 <h1>torch.nn.functional.max_pool1d<a class="headerlink" href="#torch-nn-functional-max-pool1d" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.functional.max_pool1d">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">max_pool1d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dilation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ceil_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_indices</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/_jit_internal.py#L613"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.max_pool1d" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">max_pool1d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dilation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ceil_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_indices</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/_jit_internal.py#L613"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.max_pool1d" title="Permalink to this definition">¶</a></dt>
 <dd><p>Applies a 1D max pooling over an input signal composed of several input
 planes.</p>
 <div class="admonition note">
diff --git a/main/generated/torch.nn.functional.max_pool2d.html b/main/generated/torch.nn.functional.max_pool2d.html
index 183cbab2af4..fbbea91179b 100644
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@@ -612,7 +612,7 @@
 <h1>torch.nn.functional.max_pool2d<a class="headerlink" href="#torch-nn-functional-max-pool2d" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.functional.max_pool2d">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">max_pool2d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dilation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ceil_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_indices</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/_jit_internal.py#L613"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.max_pool2d" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">max_pool2d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dilation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ceil_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_indices</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/_jit_internal.py#L613"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.max_pool2d" title="Permalink to this definition">¶</a></dt>
 <dd><p>Applies a 2D max pooling over an input signal composed of several input
 planes.</p>
 <div class="admonition note">
diff --git a/main/generated/torch.nn.functional.max_pool3d.html b/main/generated/torch.nn.functional.max_pool3d.html
index eac0ff20fdd..76752777cb3 100644
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+++ b/main/generated/torch.nn.functional.max_pool3d.html
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     <div id="searchBox">
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@@ -612,7 +612,7 @@
 <h1>torch.nn.functional.max_pool3d<a class="headerlink" href="#torch-nn-functional-max-pool3d" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.functional.max_pool3d">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">max_pool3d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dilation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ceil_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_indices</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/_jit_internal.py#L613"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.max_pool3d" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">max_pool3d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dilation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ceil_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_indices</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/_jit_internal.py#L613"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.max_pool3d" title="Permalink to this definition">¶</a></dt>
 <dd><p>Applies a 3D max pooling over an input signal composed of several input
 planes.</p>
 <div class="admonition note">
diff --git a/main/generated/torch.nn.functional.max_unpool1d.html b/main/generated/torch.nn.functional.max_unpool1d.html
index b17cd62d7ae..69e23ed9abb 100644
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+++ b/main/generated/torch.nn.functional.max_unpool1d.html
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@@ -612,7 +612,7 @@
 <h1>torch.nn.functional.max_unpool1d<a class="headerlink" href="#torch-nn-functional-max-unpool1d" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.functional.max_unpool1d">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">max_unpool1d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">indices</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/functional.html#max_unpool1d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/functional.py#L971"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.max_unpool1d" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">max_unpool1d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">indices</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/functional.html#max_unpool1d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/functional.py#L971"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.max_unpool1d" title="Permalink to this definition">¶</a></dt>
 <dd><p>Compute a partial inverse of <code class="xref py py-class docutils literal notranslate"><span class="pre">MaxPool1d</span></code>.</p>
 <p>See <a class="reference internal" href="torch.nn.MaxUnpool1d.html#torch.nn.MaxUnpool1d" title="torch.nn.MaxUnpool1d"><code class="xref py py-class docutils literal notranslate"><span class="pre">MaxUnpool1d</span></code></a> for details.</p>
 <dl class="field-list simple">
diff --git a/main/generated/torch.nn.functional.max_unpool2d.html b/main/generated/torch.nn.functional.max_unpool2d.html
index cfd7eed8c7a..89d06833325 100644
--- a/main/generated/torch.nn.functional.max_unpool2d.html
+++ b/main/generated/torch.nn.functional.max_unpool2d.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.nn.functional.max_unpool2d<a class="headerlink" href="#torch-nn-functional-max-unpool2d" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.functional.max_unpool2d">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">max_unpool2d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">indices</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/functional.html#max_unpool2d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/functional.py#L1010"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.max_unpool2d" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">max_unpool2d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">indices</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/functional.html#max_unpool2d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/functional.py#L1010"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.max_unpool2d" title="Permalink to this definition">¶</a></dt>
 <dd><p>Compute a partial inverse of <code class="xref py py-class docutils literal notranslate"><span class="pre">MaxPool2d</span></code>.</p>
 <p>See <a class="reference internal" href="torch.nn.MaxUnpool2d.html#torch.nn.MaxUnpool2d" title="torch.nn.MaxUnpool2d"><code class="xref py py-class docutils literal notranslate"><span class="pre">MaxUnpool2d</span></code></a> for details.</p>
 <dl class="field-list simple">
diff --git a/main/generated/torch.nn.functional.max_unpool3d.html b/main/generated/torch.nn.functional.max_unpool3d.html
index 6c4cd953c7c..43cdf45c394 100644
--- a/main/generated/torch.nn.functional.max_unpool3d.html
+++ b/main/generated/torch.nn.functional.max_unpool3d.html
@@ -300,7 +300,7 @@
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@@ -612,7 +612,7 @@
 <h1>torch.nn.functional.max_unpool3d<a class="headerlink" href="#torch-nn-functional-max-unpool3d" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.functional.max_unpool3d">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">max_unpool3d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">indices</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/functional.html#max_unpool3d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/functional.py#L1043"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.max_unpool3d" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">max_unpool3d</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">indices</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/functional.html#max_unpool3d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/functional.py#L1043"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.max_unpool3d" title="Permalink to this definition">¶</a></dt>
 <dd><p>Compute a partial inverse of <code class="xref py py-class docutils literal notranslate"><span class="pre">MaxPool3d</span></code>.</p>
 <p>See <a class="reference internal" href="torch.nn.MaxUnpool3d.html#torch.nn.MaxUnpool3d" title="torch.nn.MaxUnpool3d"><code class="xref py py-class docutils literal notranslate"><span class="pre">MaxUnpool3d</span></code></a> for details.</p>
 <dl class="field-list simple">
diff --git a/main/generated/torch.nn.functional.mish.html b/main/generated/torch.nn.functional.mish.html
index 72d193b873b..c0bd1849316 100644
--- a/main/generated/torch.nn.functional.mish.html
+++ b/main/generated/torch.nn.functional.mish.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.nn.functional.mish<a class="headerlink" href="#torch-nn-functional-mish" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.functional.mish">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">mish</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/functional.html#mish"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/functional.py#L2383"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.mish" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">mish</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/functional.html#mish"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/functional.py#L2383"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.mish" title="Permalink to this definition">¶</a></dt>
 <dd><p>Apply the Mish function, element-wise.</p>
 <p>Mish: A Self Regularized Non-Monotonic Neural Activation Function.</p>
 <div class="math">
diff --git a/main/generated/torch.nn.functional.mse_loss.html b/main/generated/torch.nn.functional.mse_loss.html
index fbcf75fef16..c87491e30c2 100644
--- a/main/generated/torch.nn.functional.mse_loss.html
+++ b/main/generated/torch.nn.functional.mse_loss.html
@@ -300,7 +300,7 @@
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     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.nn.functional.mse_loss<a class="headerlink" href="#torch-nn-functional-mse-loss" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.functional.mse_loss">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">mse_loss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">size_average</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduce</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mean'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><span class="pre">Tensor</span></a></span></span><a class="reference internal" href="../_modules/torch/nn/functional.html#mse_loss"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/functional.py#L3835"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.mse_loss" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">mse_loss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">size_average</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduce</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mean'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><span class="pre">Tensor</span></a></span></span><a class="reference internal" href="../_modules/torch/nn/functional.html#mse_loss"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/functional.py#L3835"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.mse_loss" title="Permalink to this definition">¶</a></dt>
 <dd><p>Measures the element-wise mean squared error, with optional weighting.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.nn.functional.multi_margin_loss.html b/main/generated/torch.nn.functional.multi_margin_loss.html
index 754d09698b3..66fb5de3ea9 100644
--- a/main/generated/torch.nn.functional.multi_margin_loss.html
+++ b/main/generated/torch.nn.functional.multi_margin_loss.html
@@ -300,7 +300,7 @@
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     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
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     <div id="searchBox">
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@@ -612,7 +612,7 @@
 <h1>torch.nn.functional.multi_margin_loss<a class="headerlink" href="#torch-nn-functional-multi-margin-loss" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.functional.multi_margin_loss">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">multi_margin_loss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">p</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">margin</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">size_average</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduce</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mean'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><span class="pre">Tensor</span></a></span></span><a class="reference internal" href="../_modules/torch/nn/functional.html#multi_margin_loss"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/functional.py#L4113"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.multi_margin_loss" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">multi_margin_loss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">p</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">margin</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">size_average</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduce</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mean'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><span class="pre">Tensor</span></a></span></span><a class="reference internal" href="../_modules/torch/nn/functional.html#multi_margin_loss"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/functional.py#L4113"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.multi_margin_loss" title="Permalink to this definition">¶</a></dt>
 <dd><p>See <a class="reference internal" href="torch.nn.MultiMarginLoss.html#torch.nn.MultiMarginLoss" title="torch.nn.MultiMarginLoss"><code class="xref py py-class docutils literal notranslate"><span class="pre">MultiMarginLoss</span></code></a> for details.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
diff --git a/main/generated/torch.nn.functional.multilabel_margin_loss.html b/main/generated/torch.nn.functional.multilabel_margin_loss.html
index 264d1678008..e6de37598d4 100644
--- a/main/generated/torch.nn.functional.multilabel_margin_loss.html
+++ b/main/generated/torch.nn.functional.multilabel_margin_loss.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.nn.functional.multilabel_margin_loss<a class="headerlink" href="#torch-nn-functional-multilabel-margin-loss" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.functional.multilabel_margin_loss">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">multilabel_margin_loss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">size_average</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduce</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mean'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><span class="pre">Tensor</span></a></span></span><a class="reference internal" href="../_modules/torch/nn/functional.html#multilabel_margin_loss"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/functional.py#L3977"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.multilabel_margin_loss" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">multilabel_margin_loss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">size_average</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduce</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mean'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><span class="pre">Tensor</span></a></span></span><a class="reference internal" href="../_modules/torch/nn/functional.html#multilabel_margin_loss"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/functional.py#L3977"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.multilabel_margin_loss" title="Permalink to this definition">¶</a></dt>
 <dd><p>See <a class="reference internal" href="torch.nn.MultiLabelMarginLoss.html#torch.nn.MultiLabelMarginLoss" title="torch.nn.MultiLabelMarginLoss"><code class="xref py py-class docutils literal notranslate"><span class="pre">MultiLabelMarginLoss</span></code></a> for details.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
diff --git a/main/generated/torch.nn.functional.multilabel_soft_margin_loss.html b/main/generated/torch.nn.functional.multilabel_soft_margin_loss.html
index 1fc430c0df8..6ab7995e369 100644
--- a/main/generated/torch.nn.functional.multilabel_soft_margin_loss.html
+++ b/main/generated/torch.nn.functional.multilabel_soft_margin_loss.html
@@ -300,7 +300,7 @@
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     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.nn.functional.multilabel_soft_margin_loss<a class="headerlink" href="#torch-nn-functional-multilabel-soft-margin-loss" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.functional.multilabel_soft_margin_loss">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">multilabel_soft_margin_loss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">size_average</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduce</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mean'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><span class="pre">Tensor</span></a></span></span><a class="reference internal" href="../_modules/torch/nn/functional.html#multilabel_soft_margin_loss"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/functional.py#L4034"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.multilabel_soft_margin_loss" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">multilabel_soft_margin_loss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">size_average</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduce</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mean'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><span class="pre">Tensor</span></a></span></span><a class="reference internal" href="../_modules/torch/nn/functional.html#multilabel_soft_margin_loss"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/functional.py#L4034"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.multilabel_soft_margin_loss" title="Permalink to this definition">¶</a></dt>
 <dd><p>See <a class="reference internal" href="torch.nn.MultiLabelSoftMarginLoss.html#torch.nn.MultiLabelSoftMarginLoss" title="torch.nn.MultiLabelSoftMarginLoss"><code class="xref py py-class docutils literal notranslate"><span class="pre">MultiLabelSoftMarginLoss</span></code></a> for details.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
diff --git a/main/generated/torch.nn.functional.nll_loss.html b/main/generated/torch.nn.functional.nll_loss.html
index bf337cac1c2..6f458005881 100644
--- a/main/generated/torch.nn.functional.nll_loss.html
+++ b/main/generated/torch.nn.functional.nll_loss.html
@@ -300,7 +300,7 @@
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     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.nn.functional.nll_loss<a class="headerlink" href="#torch-nn-functional-nll-loss" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.functional.nll_loss">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">nll_loss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">size_average</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ignore_index</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-100</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduce</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mean'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/functional.html#nll_loss"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/functional.py#L3094"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.nll_loss" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">nll_loss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">size_average</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ignore_index</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-100</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduce</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mean'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/functional.html#nll_loss"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/functional.py#L3094"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.nll_loss" title="Permalink to this definition">¶</a></dt>
 <dd><p>Compute the negative log likelihood loss.</p>
 <p>See <a class="reference internal" href="torch.nn.NLLLoss.html#torch.nn.NLLLoss" title="torch.nn.NLLLoss"><code class="xref py py-class docutils literal notranslate"><span class="pre">NLLLoss</span></code></a> for details.</p>
 <dl class="field-list simple">
diff --git a/main/generated/torch.nn.functional.normalize.html b/main/generated/torch.nn.functional.normalize.html
index 97a24065a45..ec2f11cefe5 100644
--- a/main/generated/torch.nn.functional.normalize.html
+++ b/main/generated/torch.nn.functional.normalize.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.nn.functional.normalize<a class="headerlink" href="#torch-nn-functional-normalize" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.functional.normalize">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">normalize</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">p</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-12</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/functional.html#normalize"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/functional.py#L5456"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.normalize" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">normalize</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">p</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-12</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/functional.html#normalize"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/functional.py#L5456"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.normalize" title="Permalink to this definition">¶</a></dt>
 <dd><p>Perform <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><msub><mi>L</mi><mi>p</mi></msub></mrow><annotation encoding="application/x-tex">L_p</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.9694em;vertical-align:-0.2861em;"></span><span class="mord"><span class="mord mathnormal">L</span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.1514em;"><span style="top:-2.55em;margin-left:0em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mathnormal mtight">p</span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.2861em;"><span></span></span></span></span></span></span></span></span></span></span> normalization of inputs over specified dimension.</p>
 <p>For a tensor <code class="xref py py-attr docutils literal notranslate"><span class="pre">input</span></code> of sizes <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mo stretchy="false">(</mo><msub><mi>n</mi><mn>0</mn></msub><mo separator="true">,</mo><mi mathvariant="normal">.</mi><mi mathvariant="normal">.</mi><mi mathvariant="normal">.</mi><mo separator="true">,</mo><msub><mi>n</mi><mrow><mi>d</mi><mi>i</mi><mi>m</mi></mrow></msub><mo separator="true">,</mo><mi mathvariant="normal">.</mi><mi mathvariant="normal">.</mi><mi mathvariant="normal">.</mi><mo separator="true">,</mo><msub><mi>n</mi><mi>k</mi></msub><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">(n_0, ..., n_{dim}, ..., n_k)</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mopen">(</span><span class="mord"><span class="mord mathnormal">n</span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.3011em;"><span style="top:-2.55em;margin-left:0em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight">0</span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.15em;"><span></span></span></span></span></span></span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord">...</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord"><span class="mord mathnormal">n</span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.3361em;"><span style="top:-2.55em;margin-left:0em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mord mathnormal mtight">d</span><span class="mord mathnormal mtight">im</span></span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.15em;"><span></span></span></span></span></span></span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord">...</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord"><span class="mord mathnormal">n</span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.3361em;"><span style="top:-2.55em;margin-left:0em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mathnormal mtight" style="margin-right:0.03148em;">k</span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.15em;"><span></span></span></span></span></span></span><span class="mclose">)</span></span></span></span></span>, each
 <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><msub><mi>n</mi><mrow><mi>d</mi><mi>i</mi><mi>m</mi></mrow></msub></mrow><annotation encoding="application/x-tex">n_{dim}</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.5806em;vertical-align:-0.15em;"></span><span class="mord"><span class="mord mathnormal">n</span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.3361em;"><span style="top:-2.55em;margin-left:0em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mord mathnormal mtight">d</span><span class="mord mathnormal mtight">im</span></span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.15em;"><span></span></span></span></span></span></span></span></span></span></span> -element vector <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>v</mi></mrow><annotation encoding="application/x-tex">v</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.4306em;"></span><span class="mord mathnormal" style="margin-right:0.03588em;">v</span></span></span></span></span> along dimension <code class="xref py py-attr docutils literal notranslate"><span class="pre">dim</span></code> is transformed as</p>
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diff --git a/main/generated/torch.nn.functional.pad.html b/main/generated/torch.nn.functional.pad.html
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 <h1>torch.nn.functional.pad<a class="headerlink" href="#torch-nn-functional-pad" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.functional.pad">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">pad</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pad</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'constant'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">value</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><span class="pre">Tensor</span></a></span></span><a class="reference internal" href="../_modules/torch/nn/functional.html#pad"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/functional.py#L5128"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.pad" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">pad</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pad</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'constant'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">value</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><span class="pre">Tensor</span></a></span></span><a class="reference internal" href="../_modules/torch/nn/functional.html#pad"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/functional.py#L5128"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.pad" title="Permalink to this definition">¶</a></dt>
 <dd><p>Pads tensor.</p>
 <dl class="simple">
 <dt>Padding size:</dt><dd><p>The padding size by which to pad some dimensions of <code class="xref py py-attr docutils literal notranslate"><span class="pre">input</span></code>
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@@ -612,7 +612,7 @@
 <h1>torch.nn.functional.poisson_nll_loss<a class="headerlink" href="#torch-nn-functional-poisson-nll-loss" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.functional.poisson_nll_loss">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">poisson_nll_loss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">log_input</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">full</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">size_average</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-08</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduce</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mean'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/functional.html#poisson_nll_loss"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/functional.py#L3163"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.poisson_nll_loss" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">poisson_nll_loss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">log_input</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">full</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">size_average</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-08</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduce</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mean'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/functional.html#poisson_nll_loss"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/functional.py#L3163"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.poisson_nll_loss" title="Permalink to this definition">¶</a></dt>
 <dd><p>Poisson negative log likelihood loss.</p>
 <p>See <a class="reference internal" href="torch.nn.PoissonNLLLoss.html#torch.nn.PoissonNLLLoss" title="torch.nn.PoissonNLLLoss"><code class="xref py py-class docutils literal notranslate"><span class="pre">PoissonNLLLoss</span></code></a> for details.</p>
 <dl class="field-list simple">
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@@ -612,7 +612,7 @@
 <h1>torch.nn.functional.relu<a class="headerlink" href="#torch-nn-functional-relu" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.functional.relu">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">relu</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><span class="pre">Tensor</span></a></span></span><a class="reference internal" href="../_modules/torch/nn/functional.html#relu"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/functional.py#L1693"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.relu" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">relu</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><span class="pre">Tensor</span></a></span></span><a class="reference internal" href="../_modules/torch/nn/functional.html#relu"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/functional.py#L1693"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.relu" title="Permalink to this definition">¶</a></dt>
 <dd><p>Applies the rectified linear unit function element-wise. See
 <a class="reference internal" href="torch.nn.ReLU.html#torch.nn.ReLU" title="torch.nn.ReLU"><code class="xref py py-class docutils literal notranslate"><span class="pre">ReLU</span></code></a> for more details.</p>
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 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.functional.relu6">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">relu6</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><span class="pre">Tensor</span></a></span></span><a class="reference internal" href="../_modules/torch/nn/functional.html#relu6"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/functional.py#L1780"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.relu6" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">relu6</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><span class="pre">Tensor</span></a></span></span><a class="reference internal" href="../_modules/torch/nn/functional.html#relu6"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/functional.py#L1780"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.relu6" title="Permalink to this definition">¶</a></dt>
 <dd><p>Applies the element-wise function <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mtext>ReLU6</mtext><mo stretchy="false">(</mo><mi>x</mi><mo stretchy="false">)</mo><mo>=</mo><mi>min</mi><mo>⁡</mo><mo stretchy="false">(</mo><mi>max</mi><mo>⁡</mo><mo stretchy="false">(</mo><mn>0</mn><mo separator="true">,</mo><mi>x</mi><mo stretchy="false">)</mo><mo separator="true">,</mo><mn>6</mn><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">\text{ReLU6}(x) = \min(\max(0,x), 6)</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mord text"><span class="mord">ReLU6</span></span><span class="mopen">(</span><span class="mord mathnormal">x</span><span class="mclose">)</span><span class="mspace" style="margin-right:0.2778em;"></span><span class="mrel">=</span><span class="mspace" style="margin-right:0.2778em;"></span></span><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mop">min</span><span class="mopen">(</span><span class="mop">max</span><span class="mopen">(</span><span class="mord">0</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord mathnormal">x</span><span class="mclose">)</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord">6</span><span class="mclose">)</span></span></span></span></span>.</p>
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@@ -612,7 +612,7 @@
 <h1>torch.nn.functional.rms_norm<a class="headerlink" href="#torch-nn-functional-rms-norm" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
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-<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">rms_norm</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">normalized_shape</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/functional.html#rms_norm"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/functional.py#L2915"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.rms_norm" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">rms_norm</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">normalized_shape</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weight</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/functional.html#rms_norm"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/functional.py#L2915"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.rms_norm" title="Permalink to this definition">¶</a></dt>
 <dd><p>Apply Root Mean Square Layer Normalization.</p>
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+<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">rrelu</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lower</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.</span> <span class="pre">/</span> <span class="pre">8</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">upper</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.</span> <span class="pre">/</span> <span class="pre">3</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">training</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><span class="pre">Tensor</span></a></span></span><a class="reference internal" href="../_modules/torch/nn/functional.html#rrelu"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/functional.py#L1937"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.rrelu" title="Permalink to this definition">¶</a></dt>
 <dd><p>Randomized leaky ReLU.</p>
 <p>See <a class="reference internal" href="torch.nn.RReLU.html#torch.nn.RReLU" title="torch.nn.RReLU"><code class="xref py py-class docutils literal notranslate"><span class="pre">RReLU</span></code></a> for more details.</p>
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+<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">selu</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><span class="pre">Tensor</span></a></span></span><a class="reference internal" href="../_modules/torch/nn/functional.html#selu"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/functional.py#L1820"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.selu" title="Permalink to this definition">¶</a></dt>
 <dd><p>Applies element-wise,
 <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mtext>SELU</mtext><mo stretchy="false">(</mo><mi>x</mi><mo stretchy="false">)</mo><mo>=</mo><mi>s</mi><mi>c</mi><mi>a</mi><mi>l</mi><mi>e</mi><mo>∗</mo><mo stretchy="false">(</mo><mi>max</mi><mo>⁡</mo><mo stretchy="false">(</mo><mn>0</mn><mo separator="true">,</mo><mi>x</mi><mo stretchy="false">)</mo><mo>+</mo><mi>min</mi><mo>⁡</mo><mo stretchy="false">(</mo><mn>0</mn><mo separator="true">,</mo><mi>α</mi><mo>∗</mo><mo stretchy="false">(</mo><mi>exp</mi><mo>⁡</mo><mo stretchy="false">(</mo><mi>x</mi><mo stretchy="false">)</mo><mo>−</mo><mn>1</mn><mo stretchy="false">)</mo><mo stretchy="false">)</mo><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">\text{SELU}(x) = scale * (\max(0,x) + \min(0, \alpha * (\exp(x) - 1)))</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mord text"><span class="mord">SELU</span></span><span class="mopen">(</span><span class="mord mathnormal">x</span><span class="mclose">)</span><span class="mspace" style="margin-right:0.2778em;"></span><span class="mrel">=</span><span class="mspace" style="margin-right:0.2778em;"></span></span><span class="base"><span class="strut" style="height:0.6944em;"></span><span class="mord mathnormal">sc</span><span class="mord mathnormal">a</span><span class="mord mathnormal" style="margin-right:0.01968em;">l</span><span class="mord mathnormal">e</span><span class="mspace" style="margin-right:0.2222em;"></span><span class="mbin">∗</span><span class="mspace" style="margin-right:0.2222em;"></span></span><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mopen">(</span><span class="mop">max</span><span class="mopen">(</span><span class="mord">0</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord mathnormal">x</span><span class="mclose">)</span><span class="mspace" style="margin-right:0.2222em;"></span><span class="mbin">+</span><span class="mspace" style="margin-right:0.2222em;"></span></span><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mop">min</span><span class="mopen">(</span><span class="mord">0</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord mathnormal" style="margin-right:0.0037em;">α</span><span class="mspace" style="margin-right:0.2222em;"></span><span class="mbin">∗</span><span class="mspace" style="margin-right:0.2222em;"></span></span><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mopen">(</span><span class="mop">exp</span><span class="mopen">(</span><span class="mord mathnormal">x</span><span class="mclose">)</span><span class="mspace" style="margin-right:0.2222em;"></span><span class="mbin">−</span><span class="mspace" style="margin-right:0.2222em;"></span></span><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mord">1</span><span class="mclose">)))</span></span></span></span></span>,
 with <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>α</mi><mo>=</mo><mn>1.6732632423543772848170429916717</mn></mrow><annotation encoding="application/x-tex">\alpha=1.6732632423543772848170429916717</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.4306em;"></span><span class="mord mathnormal" style="margin-right:0.0037em;">α</span><span class="mspace" style="margin-right:0.2778em;"></span><span class="mrel">=</span><span class="mspace" style="margin-right:0.2778em;"></span></span><span class="base"><span class="strut" style="height:0.6444em;"></span><span class="mord">1.6732632423543772848170429916717</span></span></span></span></span> and
diff --git a/main/generated/torch.nn.functional.sigmoid.html b/main/generated/torch.nn.functional.sigmoid.html
index 56914047879..820fc036dc3 100644
--- a/main/generated/torch.nn.functional.sigmoid.html
+++ b/main/generated/torch.nn.functional.sigmoid.html
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@@ -612,7 +612,7 @@
 <h1>torch.nn.functional.sigmoid<a class="headerlink" href="#torch-nn-functional-sigmoid" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.functional.sigmoid">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">sigmoid</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><span class="pre">Tensor</span></a></span></span><a class="reference internal" href="../_modules/torch/nn/functional.html#sigmoid"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/functional.py#L2277"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.sigmoid" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">sigmoid</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><span class="pre">Tensor</span></a></span></span><a class="reference internal" href="../_modules/torch/nn/functional.html#sigmoid"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/functional.py#L2277"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.sigmoid" title="Permalink to this definition">¶</a></dt>
 <dd><p>Applies the element-wise function <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mtext>Sigmoid</mtext><mo stretchy="false">(</mo><mi>x</mi><mo stretchy="false">)</mo><mo>=</mo><mfrac><mn>1</mn><mrow><mn>1</mn><mo>+</mo><mi>exp</mi><mo>⁡</mo><mo stretchy="false">(</mo><mo>−</mo><mi>x</mi><mo stretchy="false">)</mo></mrow></mfrac></mrow><annotation encoding="application/x-tex">\text{Sigmoid}(x) = \frac{1}{1 + \exp(-x)}</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mord text"><span class="mord">Sigmoid</span></span><span class="mopen">(</span><span class="mord mathnormal">x</span><span class="mclose">)</span><span class="mspace" style="margin-right:0.2778em;"></span><span class="mrel">=</span><span class="mspace" style="margin-right:0.2778em;"></span></span><span class="base"><span class="strut" style="height:1.3651em;vertical-align:-0.52em;"></span><span class="mord"><span class="mopen nulldelimiter"></span><span class="mfrac"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.8451em;"><span style="top:-2.655em;"><span class="pstrut" style="height:3em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mord mtight">1</span><span class="mbin mtight">+</span><span class="mop mtight"><span class="mtight">e</span><span class="mtight">x</span><span class="mtight">p</span></span><span class="mopen mtight">(</span><span class="mord mtight">−</span><span class="mord mathnormal mtight">x</span><span class="mclose mtight">)</span></span></span></span><span style="top:-3.23em;"><span class="pstrut" style="height:3em;"></span><span class="frac-line" style="border-bottom-width:0.04em;"></span></span><span style="top:-3.394em;"><span class="pstrut" style="height:3em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mord mtight">1</span></span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.52em;"><span></span></span></span></span></span><span class="mclose nulldelimiter"></span></span></span></span></span></span></p>
 <p>See <a class="reference internal" href="torch.nn.Sigmoid.html#torch.nn.Sigmoid" title="torch.nn.Sigmoid"><code class="xref py py-class docutils literal notranslate"><span class="pre">Sigmoid</span></code></a> for more details.</p>
 </dd></dl>
diff --git a/main/generated/torch.nn.functional.silu.html b/main/generated/torch.nn.functional.silu.html
index fe835935690..fdbfbeb5584 100644
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+++ b/main/generated/torch.nn.functional.silu.html
@@ -300,7 +300,7 @@
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     <div class="version">
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+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.nn.functional.silu<a class="headerlink" href="#torch-nn-functional-silu" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.functional.silu">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">silu</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/functional.html#silu"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/functional.py#L2358"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.silu" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">silu</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/functional.html#silu"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/functional.py#L2358"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.silu" title="Permalink to this definition">¶</a></dt>
 <dd><p>Apply the Sigmoid Linear Unit (SiLU) function, element-wise.</p>
 <p>The SiLU function is also known as the swish function.</p>
 <div class="math">
diff --git a/main/generated/torch.nn.functional.smooth_l1_loss.html b/main/generated/torch.nn.functional.smooth_l1_loss.html
index bdee734713e..8091bc55d0a 100644
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+++ b/main/generated/torch.nn.functional.smooth_l1_loss.html
@@ -300,7 +300,7 @@
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-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.nn.functional.smooth_l1_loss<a class="headerlink" href="#torch-nn-functional-smooth-l1-loss" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.functional.smooth_l1_loss">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">smooth_l1_loss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">size_average</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduce</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mean'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">beta</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/functional.html#smooth_l1_loss"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/functional.py#L3648"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.smooth_l1_loss" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">smooth_l1_loss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">size_average</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduce</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mean'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">beta</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/functional.html#smooth_l1_loss"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/functional.py#L3648"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.smooth_l1_loss" title="Permalink to this definition">¶</a></dt>
 <dd><p>Compute the Smooth L1 loss.</p>
 <p>Function uses a squared term if the absolute
 element-wise error falls below beta and an L1 term otherwise.</p>
diff --git a/main/generated/torch.nn.functional.soft_margin_loss.html b/main/generated/torch.nn.functional.soft_margin_loss.html
index 99372ae8e2e..af4980bd9c7 100644
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+++ b/main/generated/torch.nn.functional.soft_margin_loss.html
@@ -300,7 +300,7 @@
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+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
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@@ -612,7 +612,7 @@
 <h1>torch.nn.functional.soft_margin_loss<a class="headerlink" href="#torch-nn-functional-soft-margin-loss" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.functional.soft_margin_loss">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">soft_margin_loss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">size_average</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduce</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mean'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><span class="pre">Tensor</span></a></span></span><a class="reference internal" href="../_modules/torch/nn/functional.html#soft_margin_loss"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/functional.py#L4005"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.soft_margin_loss" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">soft_margin_loss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">size_average</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduce</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mean'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><span class="pre">Tensor</span></a></span></span><a class="reference internal" href="../_modules/torch/nn/functional.html#soft_margin_loss"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/functional.py#L4005"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.soft_margin_loss" title="Permalink to this definition">¶</a></dt>
 <dd><p>See <a class="reference internal" href="torch.nn.SoftMarginLoss.html#torch.nn.SoftMarginLoss" title="torch.nn.SoftMarginLoss"><code class="xref py py-class docutils literal notranslate"><span class="pre">SoftMarginLoss</span></code></a> for details.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
diff --git a/main/generated/torch.nn.functional.softmax.html b/main/generated/torch.nn.functional.softmax.html
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@@ -612,7 +612,7 @@
 <h1>torch.nn.functional.softmax<a class="headerlink" href="#torch-nn-functional-softmax" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.functional.softmax">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">softmax</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_stacklevel</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">3</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/functional.html#softmax"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/functional.py#L2103"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.softmax" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">softmax</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_stacklevel</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">3</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/functional.html#softmax"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/functional.py#L2103"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.softmax" title="Permalink to this definition">¶</a></dt>
 <dd><p>Apply a softmax function.</p>
 <p>Softmax is defined as:</p>
 <p><span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mtext>Softmax</mtext><mo stretchy="false">(</mo><msub><mi>x</mi><mi>i</mi></msub><mo stretchy="false">)</mo><mo>=</mo><mfrac><mrow><mi>exp</mi><mo>⁡</mo><mo stretchy="false">(</mo><msub><mi>x</mi><mi>i</mi></msub><mo stretchy="false">)</mo></mrow><mrow><msub><mo>∑</mo><mi>j</mi></msub><mi>exp</mi><mo>⁡</mo><mo stretchy="false">(</mo><msub><mi>x</mi><mi>j</mi></msub><mo stretchy="false">)</mo></mrow></mfrac></mrow><annotation encoding="application/x-tex">\text{Softmax}(x_{i}) = \frac{\exp(x_i)}{\sum_j \exp(x_j)}</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mord text"><span class="mord">Softmax</span></span><span class="mopen">(</span><span class="mord"><span class="mord mathnormal">x</span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.3117em;"><span style="top:-2.55em;margin-left:0em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mord mathnormal mtight">i</span></span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.15em;"><span></span></span></span></span></span></span><span class="mclose">)</span><span class="mspace" style="margin-right:0.2778em;"></span><span class="mrel">=</span><span class="mspace" style="margin-right:0.2778em;"></span></span><span class="base"><span class="strut" style="height:1.6772em;vertical-align:-0.6672em;"></span><span class="mord"><span class="mopen nulldelimiter"></span><span class="mfrac"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:1.01em;"><span style="top:-2.655em;"><span class="pstrut" style="height:3em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mop mtight"><span class="mop op-symbol small-op mtight" style="position:relative;top:0em;">∑</span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.1496em;"><span style="top:-2.1786em;margin-left:0em;margin-right:0.0714em;"><span class="pstrut" style="height:2.5em;"></span><span class="sizing reset-size3 size1 mtight"><span class="mord mathnormal mtight" style="margin-right:0.05724em;">j</span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.4603em;"><span></span></span></span></span></span></span><span class="mspace mtight" style="margin-right:0.1952em;"></span><span class="mop mtight"><span class="mtight">e</span><span class="mtight">x</span><span class="mtight">p</span></span><span class="mopen mtight">(</span><span class="mord mtight"><span class="mord mathnormal mtight">x</span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.3281em;"><span style="top:-2.357em;margin-left:0em;margin-right:0.0714em;"><span class="pstrut" style="height:2.5em;"></span><span class="sizing reset-size3 size1 mtight"><span class="mord mathnormal mtight" style="margin-right:0.05724em;">j</span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.2819em;"><span></span></span></span></span></span></span><span class="mclose mtight">)</span></span></span></span><span style="top:-3.23em;"><span class="pstrut" style="height:3em;"></span><span class="frac-line" style="border-bottom-width:0.04em;"></span></span><span style="top:-3.485em;"><span class="pstrut" style="height:3em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mop mtight"><span class="mtight">e</span><span class="mtight">x</span><span class="mtight">p</span></span><span class="mopen mtight">(</span><span class="mord mtight"><span class="mord mathnormal mtight">x</span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.3281em;"><span style="top:-2.357em;margin-left:0em;margin-right:0.0714em;"><span class="pstrut" style="height:2.5em;"></span><span class="sizing reset-size3 size1 mtight"><span class="mord mathnormal mtight">i</span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.143em;"><span></span></span></span></span></span></span><span class="mclose mtight">)</span></span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.6672em;"><span></span></span></span></span></span><span class="mclose nulldelimiter"></span></span></span></span></span></span></p>
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 <h1>torch.nn.functional.softmin<a class="headerlink" href="#torch-nn-functional-softmin" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.functional.softmin">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">softmin</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_stacklevel</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">3</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/functional.html#softmin"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/functional.py#L2070"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.softmin" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">softmin</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_stacklevel</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">3</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/functional.html#softmin"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/functional.py#L2070"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.softmin" title="Permalink to this definition">¶</a></dt>
 <dd><p>Apply a softmin function.</p>
 <p>Note that <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mtext>Softmin</mtext><mo stretchy="false">(</mo><mi>x</mi><mo stretchy="false">)</mo><mo>=</mo><mtext>Softmax</mtext><mo stretchy="false">(</mo><mo>−</mo><mi>x</mi><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">\text{Softmin}(x) = \text{Softmax}(-x)</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mord text"><span class="mord">Softmin</span></span><span class="mopen">(</span><span class="mord mathnormal">x</span><span class="mclose">)</span><span class="mspace" style="margin-right:0.2778em;"></span><span class="mrel">=</span><span class="mspace" style="margin-right:0.2778em;"></span></span><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mord text"><span class="mord">Softmax</span></span><span class="mopen">(</span><span class="mord">−</span><span class="mord mathnormal">x</span><span class="mclose">)</span></span></span></span></span>. See softmax definition for mathematical formula.</p>
 <p>See <a class="reference internal" href="torch.nn.Softmin.html#torch.nn.Softmin" title="torch.nn.Softmin"><code class="xref py py-class docutils literal notranslate"><span class="pre">Softmin</span></code></a> for more details.</p>
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diff --git a/main/generated/torch.nn.functional.softsign.html b/main/generated/torch.nn.functional.softsign.html
index a012c86d114..1ded7bbae4f 100644
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     <div id="searchBox">
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@@ -612,7 +612,7 @@
 <h1>torch.nn.functional.softsign<a class="headerlink" href="#torch-nn-functional-softsign" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.functional.softsign">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">softsign</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><span class="pre">Tensor</span></a></span></span><a class="reference internal" href="../_modules/torch/nn/functional.html#softsign"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/functional.py#L2030"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.softsign" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">softsign</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><span class="pre">Tensor</span></a></span></span><a class="reference internal" href="../_modules/torch/nn/functional.html#softsign"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/functional.py#L2030"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.softsign" title="Permalink to this definition">¶</a></dt>
 <dd><p>Applies element-wise, the function <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mtext>SoftSign</mtext><mo stretchy="false">(</mo><mi>x</mi><mo stretchy="false">)</mo><mo>=</mo><mfrac><mi>x</mi><mrow><mn>1</mn><mo>+</mo><mi mathvariant="normal">∣</mi><mi>x</mi><mi mathvariant="normal">∣</mi></mrow></mfrac></mrow><annotation encoding="application/x-tex">\text{SoftSign}(x) = \frac{x}{1 + |x|}</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mord text"><span class="mord">SoftSign</span></span><span class="mopen">(</span><span class="mord mathnormal">x</span><span class="mclose">)</span><span class="mspace" style="margin-right:0.2778em;"></span><span class="mrel">=</span><span class="mspace" style="margin-right:0.2778em;"></span></span><span class="base"><span class="strut" style="height:1.2154em;vertical-align:-0.52em;"></span><span class="mord"><span class="mopen nulldelimiter"></span><span class="mfrac"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.6954em;"><span style="top:-2.655em;"><span class="pstrut" style="height:3em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mord mtight">1</span><span class="mbin mtight">+</span><span class="mord mtight">∣</span><span class="mord mathnormal mtight">x</span><span class="mord mtight">∣</span></span></span></span><span style="top:-3.23em;"><span class="pstrut" style="height:3em;"></span><span class="frac-line" style="border-bottom-width:0.04em;"></span></span><span style="top:-3.394em;"><span class="pstrut" style="height:3em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mord mathnormal mtight">x</span></span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.52em;"><span></span></span></span></span></span><span class="mclose nulldelimiter"></span></span></span></span></span></span></p>
 <p>See <a class="reference internal" href="torch.nn.Softsign.html#torch.nn.Softsign" title="torch.nn.Softsign"><code class="xref py py-class docutils literal notranslate"><span class="pre">Softsign</span></code></a> for more details.</p>
 </dd></dl>
diff --git a/main/generated/torch.nn.functional.tanh.html b/main/generated/torch.nn.functional.tanh.html
index e36031912a0..53c5af58798 100644
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+++ b/main/generated/torch.nn.functional.tanh.html
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@@ -612,7 +612,7 @@
 <h1>torch.nn.functional.tanh<a class="headerlink" href="#torch-nn-functional-tanh" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.functional.tanh">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">tanh</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><span class="pre">Tensor</span></a></span></span><a class="reference internal" href="../_modules/torch/nn/functional.html#tanh"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/functional.py#L2266"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.tanh" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">tanh</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><span class="pre">Tensor</span></a></span></span><a class="reference internal" href="../_modules/torch/nn/functional.html#tanh"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/functional.py#L2266"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.tanh" title="Permalink to this definition">¶</a></dt>
 <dd><p>Applies element-wise,
 <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mtext>Tanh</mtext><mo stretchy="false">(</mo><mi>x</mi><mo stretchy="false">)</mo><mo>=</mo><mi>tanh</mi><mo>⁡</mo><mo stretchy="false">(</mo><mi>x</mi><mo stretchy="false">)</mo><mo>=</mo><mfrac><mrow><mi>exp</mi><mo>⁡</mo><mo stretchy="false">(</mo><mi>x</mi><mo stretchy="false">)</mo><mo>−</mo><mi>exp</mi><mo>⁡</mo><mo stretchy="false">(</mo><mo>−</mo><mi>x</mi><mo stretchy="false">)</mo></mrow><mrow><mi>exp</mi><mo>⁡</mo><mo stretchy="false">(</mo><mi>x</mi><mo stretchy="false">)</mo><mo>+</mo><mi>exp</mi><mo>⁡</mo><mo stretchy="false">(</mo><mo>−</mo><mi>x</mi><mo stretchy="false">)</mo></mrow></mfrac></mrow><annotation encoding="application/x-tex">\text{Tanh}(x) = \tanh(x) = \frac{\exp(x) - \exp(-x)}{\exp(x) + \exp(-x)}</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mord text"><span class="mord">Tanh</span></span><span class="mopen">(</span><span class="mord mathnormal">x</span><span class="mclose">)</span><span class="mspace" style="margin-right:0.2778em;"></span><span class="mrel">=</span><span class="mspace" style="margin-right:0.2778em;"></span></span><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mop">tanh</span><span class="mopen">(</span><span class="mord mathnormal">x</span><span class="mclose">)</span><span class="mspace" style="margin-right:0.2778em;"></span><span class="mrel">=</span><span class="mspace" style="margin-right:0.2778em;"></span></span><span class="base"><span class="strut" style="height:1.53em;vertical-align:-0.52em;"></span><span class="mord"><span class="mopen nulldelimiter"></span><span class="mfrac"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:1.01em;"><span style="top:-2.655em;"><span class="pstrut" style="height:3em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mop mtight"><span class="mtight">e</span><span class="mtight">x</span><span class="mtight">p</span></span><span class="mopen mtight">(</span><span class="mord mathnormal mtight">x</span><span class="mclose mtight">)</span><span class="mbin mtight">+</span><span class="mop mtight"><span class="mtight">e</span><span class="mtight">x</span><span class="mtight">p</span></span><span class="mopen mtight">(</span><span class="mord mtight">−</span><span class="mord mathnormal mtight">x</span><span class="mclose mtight">)</span></span></span></span><span style="top:-3.23em;"><span class="pstrut" style="height:3em;"></span><span class="frac-line" style="border-bottom-width:0.04em;"></span></span><span style="top:-3.485em;"><span class="pstrut" style="height:3em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mop mtight"><span class="mtight">e</span><span class="mtight">x</span><span class="mtight">p</span></span><span class="mopen mtight">(</span><span class="mord mathnormal mtight">x</span><span class="mclose mtight">)</span><span class="mbin mtight">−</span><span class="mop mtight"><span class="mtight">e</span><span class="mtight">x</span><span class="mtight">p</span></span><span class="mopen mtight">(</span><span class="mord mtight">−</span><span class="mord mathnormal mtight">x</span><span class="mclose mtight">)</span></span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.52em;"><span></span></span></span></span></span><span class="mclose nulldelimiter"></span></span></span></span></span></span></p>
 <p>See <a class="reference internal" href="torch.nn.Tanh.html#torch.nn.Tanh" title="torch.nn.Tanh"><code class="xref py py-class docutils literal notranslate"><span class="pre">Tanh</span></code></a> for more details.</p>
diff --git a/main/generated/torch.nn.functional.tanhshrink.html b/main/generated/torch.nn.functional.tanhshrink.html
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@@ -612,7 +612,7 @@
 <h1>torch.nn.functional.tanhshrink<a class="headerlink" href="#torch-nn-functional-tanhshrink" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.functional.tanhshrink">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">tanhshrink</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><span class="pre">Tensor</span></a></span></span><a class="reference internal" href="../_modules/torch/nn/functional.html#tanhshrink"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/functional.py#L2018"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.tanhshrink" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">tanhshrink</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><span class="pre">Tensor</span></a></span></span><a class="reference internal" href="../_modules/torch/nn/functional.html#tanhshrink"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/functional.py#L2018"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.tanhshrink" title="Permalink to this definition">¶</a></dt>
 <dd><p>Applies element-wise, <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mtext>Tanhshrink</mtext><mo stretchy="false">(</mo><mi>x</mi><mo stretchy="false">)</mo><mo>=</mo><mi>x</mi><mo>−</mo><mtext>Tanh</mtext><mo stretchy="false">(</mo><mi>x</mi><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">\text{Tanhshrink}(x) = x - \text{Tanh}(x)</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mord text"><span class="mord">Tanhshrink</span></span><span class="mopen">(</span><span class="mord mathnormal">x</span><span class="mclose">)</span><span class="mspace" style="margin-right:0.2778em;"></span><span class="mrel">=</span><span class="mspace" style="margin-right:0.2778em;"></span></span><span class="base"><span class="strut" style="height:0.6667em;vertical-align:-0.0833em;"></span><span class="mord mathnormal">x</span><span class="mspace" style="margin-right:0.2222em;"></span><span class="mbin">−</span><span class="mspace" style="margin-right:0.2222em;"></span></span><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mord text"><span class="mord">Tanh</span></span><span class="mopen">(</span><span class="mord mathnormal">x</span><span class="mclose">)</span></span></span></span></span></p>
 <p>See <a class="reference internal" href="torch.nn.Tanhshrink.html#torch.nn.Tanhshrink" title="torch.nn.Tanhshrink"><code class="xref py py-class docutils literal notranslate"><span class="pre">Tanhshrink</span></code></a> for more details.</p>
 </dd></dl>
diff --git a/main/generated/torch.nn.functional.threshold.html b/main/generated/torch.nn.functional.threshold.html
index f0bb922528e..614e008e501 100644
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@@ -612,7 +612,7 @@
 <h1>torch.nn.functional.threshold<a class="headerlink" href="#torch-nn-functional-threshold" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.functional.threshold">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">threshold</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">threshold</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">value</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/functional.py#L1657"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.threshold" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">threshold</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">threshold</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">value</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inplace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/functional.py#L1657"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.threshold" title="Permalink to this definition">¶</a></dt>
 <dd><p>Apply a threshold to each element of the input Tensor.</p>
 <p>See <a class="reference internal" href="torch.nn.Threshold.html#torch.nn.Threshold" title="torch.nn.Threshold"><code class="xref py py-class docutils literal notranslate"><span class="pre">Threshold</span></code></a> for more details.</p>
 <dl class="field-list simple">
diff --git a/main/generated/torch.nn.functional.threshold_.html b/main/generated/torch.nn.functional.threshold_.html
index 69a1c92aead..86005e42467 100644
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diff --git a/main/generated/torch.nn.functional.torch.nn.parallel.data_parallel.html b/main/generated/torch.nn.functional.torch.nn.parallel.data_parallel.html
index 45089020db3..af6ab9a7808 100644
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@@ -612,7 +612,7 @@
 <h1>torch.nn.functional.torch.nn.parallel.data_parallel<a class="headerlink" href="#torch-nn-functional-torch-nn-parallel-data-parallel" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.parallel.data_parallel">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.parallel.</span></span><span class="sig-name descname"><span class="pre">data_parallel</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inputs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device_ids</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">module_kwargs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/parallel/data_parallel.html#data_parallel"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/parallel/data_parallel.py#L221"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.parallel.data_parallel" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.parallel.</span></span><span class="sig-name descname"><span class="pre">data_parallel</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inputs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device_ids</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">module_kwargs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/parallel/data_parallel.html#data_parallel"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/parallel/data_parallel.py#L221"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.parallel.data_parallel" title="Permalink to this definition">¶</a></dt>
 <dd><p>Evaluate module(input) in parallel across the GPUs given in device_ids.</p>
 <p>This is the functional version of the DataParallel module.</p>
 <dl class="field-list simple">
diff --git a/main/generated/torch.nn.functional.triplet_margin_loss.html b/main/generated/torch.nn.functional.triplet_margin_loss.html
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@@ -612,7 +612,7 @@
 <h1>torch.nn.functional.triplet_margin_loss<a class="headerlink" href="#torch-nn-functional-triplet-margin-loss" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.functional.triplet_margin_loss">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">triplet_margin_loss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">anchor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">positive</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">negative</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">margin</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">p</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-06</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">swap</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">size_average</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduce</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mean'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/functional.html#triplet_margin_loss"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/functional.py#L5337"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.triplet_margin_loss" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">triplet_margin_loss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">anchor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">positive</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">negative</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">margin</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">p</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-06</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">swap</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">size_average</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduce</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mean'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/functional.html#triplet_margin_loss"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/functional.py#L5337"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.triplet_margin_loss" title="Permalink to this definition">¶</a></dt>
 <dd><p>Compute the triplet loss between given input tensors and a margin greater than 0.</p>
 <p>See <a class="reference internal" href="torch.nn.TripletMarginLoss.html#torch.nn.TripletMarginLoss" title="torch.nn.TripletMarginLoss"><code class="xref py py-class docutils literal notranslate"><span class="pre">TripletMarginLoss</span></code></a> for details.</p>
 <dl class="field-list simple">
diff --git a/main/generated/torch.nn.functional.triplet_margin_with_distance_loss.html b/main/generated/torch.nn.functional.triplet_margin_with_distance_loss.html
index e98ab33a959..40744bcfe30 100644
--- a/main/generated/torch.nn.functional.triplet_margin_with_distance_loss.html
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@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
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     </div>
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@@ -612,7 +612,7 @@
 <h1>torch.nn.functional.triplet_margin_with_distance_loss<a class="headerlink" href="#torch-nn-functional-triplet-margin-with-distance-loss" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.functional.triplet_margin_with_distance_loss">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">triplet_margin_with_distance_loss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">anchor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">positive</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">negative</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">distance_function</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">margin</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">swap</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mean'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/functional.html#triplet_margin_with_distance_loss"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/functional.py#L5379"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.triplet_margin_with_distance_loss" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">triplet_margin_with_distance_loss</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">anchor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">positive</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">negative</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">distance_function</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">margin</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">swap</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mean'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/functional.html#triplet_margin_with_distance_loss"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/functional.py#L5379"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.triplet_margin_with_distance_loss" title="Permalink to this definition">¶</a></dt>
 <dd><p>Compute the triplet margin loss for input tensors using a custom distance function.</p>
 <p>See <a class="reference internal" href="torch.nn.TripletMarginWithDistanceLoss.html#torch.nn.TripletMarginWithDistanceLoss" title="torch.nn.TripletMarginWithDistanceLoss"><code class="xref py py-class docutils literal notranslate"><span class="pre">TripletMarginWithDistanceLoss</span></code></a> for details.</p>
 <dl class="field-list simple">
diff --git a/main/generated/torch.nn.functional.unfold.html b/main/generated/torch.nn.functional.unfold.html
index fc4a3fa88a1..13ac395c089 100644
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+++ b/main/generated/torch.nn.functional.unfold.html
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           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
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     </div>
     <div id="searchBox">
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@@ -612,7 +612,7 @@
 <h1>torch.nn.functional.unfold<a class="headerlink" href="#torch-nn-functional-unfold" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.functional.unfold">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">unfold</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dilation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/functional.html#unfold"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/functional.py#L5497"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.unfold" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">unfold</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dilation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/functional.html#unfold"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/functional.py#L5497"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.unfold" title="Permalink to this definition">¶</a></dt>
 <dd><p>Extract sliding local blocks from a batched input tensor.</p>
 <div class="admonition warning">
 <p class="admonition-title">Warning</p>
diff --git a/main/generated/torch.nn.functional.upsample.html b/main/generated/torch.nn.functional.upsample.html
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+++ b/main/generated/torch.nn.functional.upsample.html
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           <div class="pytorch-left-menu-search">
             
     <div class="version">
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     </div>
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@@ -612,7 +612,7 @@
 <h1>torch.nn.functional.upsample<a class="headerlink" href="#torch-nn-functional-upsample" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.functional.upsample">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">upsample</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale_factor</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'nearest'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">align_corners</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/functional.html#upsample"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/functional.py#L4312"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.upsample" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">upsample</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale_factor</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'nearest'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">align_corners</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/functional.html#upsample"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/functional.py#L4312"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.upsample" title="Permalink to this definition">¶</a></dt>
 <dd><p>Upsample input.</p>
 <p>Provided tensor is upsampled to either the given <code class="xref py py-attr docutils literal notranslate"><span class="pre">size</span></code> or the given
 <code class="xref py py-attr docutils literal notranslate"><span class="pre">scale_factor</span></code></p>
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           <div class="pytorch-left-menu-search">
             
     <div class="version">
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@@ -612,7 +612,7 @@
 <h1>torch.nn.functional.upsample_bilinear<a class="headerlink" href="#torch-nn-functional-upsample-bilinear" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.functional.upsample_bilinear">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">upsample_bilinear</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale_factor</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/functional.html#upsample_bilinear"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/functional.py#L4821"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.upsample_bilinear" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">upsample_bilinear</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale_factor</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/functional.html#upsample_bilinear"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/functional.py#L4821"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.upsample_bilinear" title="Permalink to this definition">¶</a></dt>
 <dd><p>Upsamples the input, using bilinear upsampling.</p>
 <div class="admonition warning">
 <p class="admonition-title">Warning</p>
diff --git a/main/generated/torch.nn.functional.upsample_nearest.html b/main/generated/torch.nn.functional.upsample_nearest.html
index 1c31c63f259..6908f5d8774 100644
--- a/main/generated/torch.nn.functional.upsample_nearest.html
+++ b/main/generated/torch.nn.functional.upsample_nearest.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
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@@ -612,7 +612,7 @@
 <h1>torch.nn.functional.upsample_nearest<a class="headerlink" href="#torch-nn-functional-upsample-nearest" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.functional.upsample_nearest">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">upsample_nearest</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale_factor</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/functional.html#upsample_nearest"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/functional.py#L4753"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.upsample_nearest" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.functional.</span></span><span class="sig-name descname"><span class="pre">upsample_nearest</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale_factor</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/functional.html#upsample_nearest"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/functional.py#L4753"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.functional.upsample_nearest" title="Permalink to this definition">¶</a></dt>
 <dd><p>Upsamples the input, using nearest neighbours’ pixel values.</p>
 <div class="admonition warning">
 <p class="admonition-title">Warning</p>
diff --git a/main/generated/torch.nn.modules.lazy.LazyModuleMixin.html b/main/generated/torch.nn.modules.lazy.LazyModuleMixin.html
index 6e352624352..32cfcabe360 100644
--- a/main/generated/torch.nn.modules.lazy.LazyModuleMixin.html
+++ b/main/generated/torch.nn.modules.lazy.LazyModuleMixin.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>LazyModuleMixin<a class="headerlink" href="#lazymodulemixin" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.modules.lazy.LazyModuleMixin">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.modules.lazy.</span></span><span class="sig-name descname"><span class="pre">LazyModuleMixin</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/lazy.html#LazyModuleMixin"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/lazy.py#L63"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.modules.lazy.LazyModuleMixin" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.modules.lazy.</span></span><span class="sig-name descname"><span class="pre">LazyModuleMixin</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/lazy.html#LazyModuleMixin"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/lazy.py#L63"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.modules.lazy.LazyModuleMixin" title="Permalink to this definition">¶</a></dt>
 <dd><p>A mixin for modules that lazily initialize parameters, also known as “lazy modules”.</p>
 <p>Modules that lazily initialize parameters, or “lazy modules”,
 derive the shapes of their parameters from the first input(s)
@@ -720,7 +720,7 @@ <h1>LazyModuleMixin<a class="headerlink" href="#lazymodulemixin" title="Permalin
 when the state is loaded. This prevents using initialized modules in different contexts.</p>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.modules.lazy.LazyModuleMixin.has_uninitialized_params">
-<span class="sig-name descname"><span class="pre">has_uninitialized_params</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/lazy.html#LazyModuleMixin.has_uninitialized_params"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/lazy.py#L250"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.modules.lazy.LazyModuleMixin.has_uninitialized_params" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">has_uninitialized_params</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/lazy.html#LazyModuleMixin.has_uninitialized_params"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/lazy.py#L250"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.modules.lazy.LazyModuleMixin.has_uninitialized_params" title="Permalink to this definition">¶</a></dt>
 <dd><p>Check if a module has parameters that are not initialized.</p>
 <dl class="field-list simple">
 </dl>
@@ -728,7 +728,7 @@ <h1>LazyModuleMixin<a class="headerlink" href="#lazymodulemixin" title="Permalin
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.modules.lazy.LazyModuleMixin.initialize_parameters">
-<span class="sig-name descname"><span class="pre">initialize_parameters</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/lazy.html#LazyModuleMixin.initialize_parameters"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/lazy.py#L240"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.modules.lazy.LazyModuleMixin.initialize_parameters" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">initialize_parameters</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/lazy.html#LazyModuleMixin.initialize_parameters"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/lazy.py#L240"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.modules.lazy.LazyModuleMixin.initialize_parameters" title="Permalink to this definition">¶</a></dt>
 <dd><p>Initialize parameters according to the input batch properties.</p>
 <p>This adds an interface to isolate parameter initialization from the
 forward pass when doing parameter shape inference.</p>
diff --git a/main/generated/torch.nn.modules.module.register_module_backward_hook.html b/main/generated/torch.nn.modules.module.register_module_backward_hook.html
index 5b317ac8b9c..ee862fa4584 100644
--- a/main/generated/torch.nn.modules.module.register_module_backward_hook.html
+++ b/main/generated/torch.nn.modules.module.register_module_backward_hook.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.nn.modules.module.register_module_backward_hook<a class="headerlink" href="#torch-nn-modules-module-register-module-backward-hook" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.modules.module.register_module_backward_hook">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.modules.module.</span></span><span class="sig-name descname"><span class="pre">register_module_backward_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#register_module_backward_hook"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L279"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.modules.module.register_module_backward_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.modules.module.</span></span><span class="sig-name descname"><span class="pre">register_module_backward_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#register_module_backward_hook"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/module.py#L279"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.modules.module.register_module_backward_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register a backward hook common to all the modules.</p>
 <p>This function is deprecated in favor of
 <a class="reference internal" href="torch.nn.modules.module.register_module_full_backward_hook.html#torch.nn.modules.module.register_module_full_backward_hook" title="torch.nn.modules.module.register_module_full_backward_hook"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.nn.modules.module.register_module_full_backward_hook()</span></code></a>
diff --git a/main/generated/torch.nn.modules.module.register_module_buffer_registration_hook.html b/main/generated/torch.nn.modules.module.register_module_buffer_registration_hook.html
index dbfe52a8e0c..49cb618c94c 100644
--- a/main/generated/torch.nn.modules.module.register_module_buffer_registration_hook.html
+++ b/main/generated/torch.nn.modules.module.register_module_buffer_registration_hook.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.nn.modules.module.register_module_buffer_registration_hook<a class="headerlink" href="#torch-nn-modules-module-register-module-buffer-registration-hook" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.modules.module.register_module_buffer_registration_hook">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.modules.module.</span></span><span class="sig-name descname"><span class="pre">register_module_buffer_registration_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#register_module_buffer_registration_hook"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L122"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.modules.module.register_module_buffer_registration_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.modules.module.</span></span><span class="sig-name descname"><span class="pre">register_module_buffer_registration_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#register_module_buffer_registration_hook"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/module.py#L122"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.modules.module.register_module_buffer_registration_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register a buffer registration hook common to all modules.</p>
 <div class="admonition warning">
 <p class="admonition-title">Warning</p>
diff --git a/main/generated/torch.nn.modules.module.register_module_forward_hook.html b/main/generated/torch.nn.modules.module.register_module_forward_hook.html
index bdd3fdf91f1..12ea72f76a3 100644
--- a/main/generated/torch.nn.modules.module.register_module_forward_hook.html
+++ b/main/generated/torch.nn.modules.module.register_module_forward_hook.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.nn.modules.module.register_module_forward_hook<a class="headerlink" href="#torch-nn-modules-module-register-module-forward-hook" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.modules.module.register_module_forward_hook">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.modules.module.</span></span><span class="sig-name descname"><span class="pre">register_module_forward_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">with_kwargs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">always_call</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#register_module_forward_hook"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L232"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.modules.module.register_module_forward_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.modules.module.</span></span><span class="sig-name descname"><span class="pre">register_module_forward_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">with_kwargs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">always_call</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#register_module_forward_hook"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/module.py#L232"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.modules.module.register_module_forward_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register a global forward hook for all the modules.</p>
 <div class="admonition warning">
 <p class="admonition-title">Warning</p>
diff --git a/main/generated/torch.nn.modules.module.register_module_forward_pre_hook.html b/main/generated/torch.nn.modules.module.register_module_forward_pre_hook.html
index ac1b871826f..d6238dc95e4 100644
--- a/main/generated/torch.nn.modules.module.register_module_forward_pre_hook.html
+++ b/main/generated/torch.nn.modules.module.register_module_forward_pre_hook.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.nn.modules.module.register_module_forward_pre_hook<a class="headerlink" href="#torch-nn-modules-module-register-module-forward-pre-hook" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.modules.module.register_module_forward_pre_hook">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.modules.module.</span></span><span class="sig-name descname"><span class="pre">register_module_forward_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#register_module_forward_pre_hook"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L200"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.modules.module.register_module_forward_pre_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.modules.module.</span></span><span class="sig-name descname"><span class="pre">register_module_forward_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#register_module_forward_pre_hook"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/module.py#L200"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.modules.module.register_module_forward_pre_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register a forward pre-hook common to all modules.</p>
 <div class="admonition warning">
 <p class="admonition-title">Warning</p>
diff --git a/main/generated/torch.nn.modules.module.register_module_full_backward_hook.html b/main/generated/torch.nn.modules.module.register_module_full_backward_hook.html
index ce8c1235342..67e6db13fc7 100644
--- a/main/generated/torch.nn.modules.module.register_module_full_backward_hook.html
+++ b/main/generated/torch.nn.modules.module.register_module_full_backward_hook.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.nn.modules.module.register_module_full_backward_hook<a class="headerlink" href="#torch-nn-modules-module-register-module-full-backward-hook" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.modules.module.register_module_full_backward_hook">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.modules.module.</span></span><span class="sig-name descname"><span class="pre">register_module_full_backward_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#register_module_full_backward_hook"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L335"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.modules.module.register_module_full_backward_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.modules.module.</span></span><span class="sig-name descname"><span class="pre">register_module_full_backward_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#register_module_full_backward_hook"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/module.py#L335"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.modules.module.register_module_full_backward_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register a backward hook common to all the modules.</p>
 <div class="admonition warning">
 <p class="admonition-title">Warning</p>
diff --git a/main/generated/torch.nn.modules.module.register_module_full_backward_pre_hook.html b/main/generated/torch.nn.modules.module.register_module_full_backward_pre_hook.html
index d387fd8fafd..51ff0c4663e 100644
--- a/main/generated/torch.nn.modules.module.register_module_full_backward_pre_hook.html
+++ b/main/generated/torch.nn.modules.module.register_module_full_backward_pre_hook.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.nn.modules.module.register_module_full_backward_pre_hook<a class="headerlink" href="#torch-nn-modules-module-register-module-full-backward-pre-hook" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.modules.module.register_module_full_backward_pre_hook">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.modules.module.</span></span><span class="sig-name descname"><span class="pre">register_module_full_backward_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#register_module_full_backward_pre_hook"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L308"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.modules.module.register_module_full_backward_pre_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.modules.module.</span></span><span class="sig-name descname"><span class="pre">register_module_full_backward_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#register_module_full_backward_pre_hook"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/module.py#L308"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.modules.module.register_module_full_backward_pre_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register a backward pre-hook common to all the modules.</p>
 <div class="admonition warning">
 <p class="admonition-title">Warning</p>
diff --git a/main/generated/torch.nn.modules.module.register_module_module_registration_hook.html b/main/generated/torch.nn.modules.module.register_module_module_registration_hook.html
index 1b9d9ceeb20..90396d73930 100644
--- a/main/generated/torch.nn.modules.module.register_module_module_registration_hook.html
+++ b/main/generated/torch.nn.modules.module.register_module_module_registration_hook.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.nn.modules.module.register_module_module_registration_hook<a class="headerlink" href="#torch-nn-modules-module-register-module-module-registration-hook" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.modules.module.register_module_module_registration_hook">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.modules.module.</span></span><span class="sig-name descname"><span class="pre">register_module_module_registration_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#register_module_module_registration_hook"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L148"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.modules.module.register_module_module_registration_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.modules.module.</span></span><span class="sig-name descname"><span class="pre">register_module_module_registration_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#register_module_module_registration_hook"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/module.py#L148"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.modules.module.register_module_module_registration_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register a module registration hook common to all modules.</p>
 <div class="admonition warning">
 <p class="admonition-title">Warning</p>
diff --git a/main/generated/torch.nn.modules.module.register_module_parameter_registration_hook.html b/main/generated/torch.nn.modules.module.register_module_parameter_registration_hook.html
index 80f25b2c6a3..9c1a2981207 100644
--- a/main/generated/torch.nn.modules.module.register_module_parameter_registration_hook.html
+++ b/main/generated/torch.nn.modules.module.register_module_parameter_registration_hook.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.nn.modules.module.register_module_parameter_registration_hook<a class="headerlink" href="#torch-nn-modules-module-register-module-parameter-registration-hook" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.modules.module.register_module_parameter_registration_hook">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.modules.module.</span></span><span class="sig-name descname"><span class="pre">register_module_parameter_registration_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#register_module_parameter_registration_hook"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L174"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.modules.module.register_module_parameter_registration_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.modules.module.</span></span><span class="sig-name descname"><span class="pre">register_module_parameter_registration_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/module.html#register_module_parameter_registration_hook"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/module.py#L174"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.modules.module.register_module_parameter_registration_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register a parameter registration hook common to all modules.</p>
 <div class="admonition warning">
 <p class="admonition-title">Warning</p>
diff --git a/main/generated/torch.nn.modules.normalization.RMSNorm.html b/main/generated/torch.nn.modules.normalization.RMSNorm.html
index adc274ef4b2..eeabae9797d 100644
--- a/main/generated/torch.nn.modules.normalization.RMSNorm.html
+++ b/main/generated/torch.nn.modules.normalization.RMSNorm.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>RMSNorm<a class="headerlink" href="#rmsnorm" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.modules.normalization.RMSNorm">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.modules.normalization.</span></span><span class="sig-name descname"><span class="pre">RMSNorm</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">normalized_shape</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">elementwise_affine</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/normalization.html#RMSNorm"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/normalization.py#L321"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.modules.normalization.RMSNorm" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.modules.normalization.</span></span><span class="sig-name descname"><span class="pre">RMSNorm</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">normalized_shape</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">elementwise_affine</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/normalization.html#RMSNorm"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/normalization.py#L321"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.modules.normalization.RMSNorm" title="Permalink to this definition">¶</a></dt>
 <dd><p>Applies Root Mean Square Layer Normalization over a mini-batch of inputs.</p>
 <p>This layer implements the operation as described in
 the paper <a class="reference external" href="https://arxiv.org/pdf/1910.07467.pdf">Root Mean Square Layer Normalization</a></p>
@@ -661,7 +661,7 @@ <h1>RMSNorm<a class="headerlink" href="#rmsnorm" title="Permalink to this headin
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.modules.normalization.RMSNorm.extra_repr">
-<span class="sig-name descname"><span class="pre">extra_repr</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/normalization.html#RMSNorm.extra_repr"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/normalization.py#L403"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.modules.normalization.RMSNorm.extra_repr" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">extra_repr</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/normalization.html#RMSNorm.extra_repr"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/normalization.py#L403"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.modules.normalization.RMSNorm.extra_repr" title="Permalink to this definition">¶</a></dt>
 <dd><p>Extra information about the module.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -672,7 +672,7 @@ <h1>RMSNorm<a class="headerlink" href="#rmsnorm" title="Permalink to this headin
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.modules.normalization.RMSNorm.forward">
-<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/normalization.html#RMSNorm.forward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/normalization.py#L397"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.modules.normalization.RMSNorm.forward" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/normalization.html#RMSNorm.forward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/normalization.py#L397"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.modules.normalization.RMSNorm.forward" title="Permalink to this definition">¶</a></dt>
 <dd><p>Runs forward pass.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -683,7 +683,7 @@ <h1>RMSNorm<a class="headerlink" href="#rmsnorm" title="Permalink to this headin
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.modules.normalization.RMSNorm.reset_parameters">
-<span class="sig-name descname"><span class="pre">reset_parameters</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/normalization.html#RMSNorm.reset_parameters"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/normalization.py#L390"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.modules.normalization.RMSNorm.reset_parameters" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">reset_parameters</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/modules/normalization.html#RMSNorm.reset_parameters"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/normalization.py#L390"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.modules.normalization.RMSNorm.reset_parameters" title="Permalink to this definition">¶</a></dt>
 <dd><p>Resets parameters based on their initialization used in __init__.</p>
 <dl class="field-list simple">
 </dl>
diff --git a/main/generated/torch.nn.parallel.DistributedDataParallel.html b/main/generated/torch.nn.parallel.DistributedDataParallel.html
index 4e2e7a2a5af..8afed06698a 100644
--- a/main/generated/torch.nn.parallel.DistributedDataParallel.html
+++ b/main/generated/torch.nn.parallel.DistributedDataParallel.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>DistributedDataParallel<a class="headerlink" href="#distributeddataparallel" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.parallel.DistributedDataParallel">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.parallel.</span></span><span class="sig-name descname"><span class="pre">DistributedDataParallel</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device_ids</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">broadcast_buffers</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">init_sync</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">process_group</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bucket_cap_mb</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">find_unused_parameters</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">check_reduction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">gradient_as_bucket_view</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">static_graph</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">delay_all_reduce_named_params</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">param_to_hook_all_reduce</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mixed_precision</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device_mesh</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/parallel/distributed.html#DistributedDataParallel"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/parallel/distributed.py#L326"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.parallel.DistributedDataParallel" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.parallel.</span></span><span class="sig-name descname"><span class="pre">DistributedDataParallel</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device_ids</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">broadcast_buffers</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">init_sync</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">process_group</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bucket_cap_mb</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">find_unused_parameters</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">check_reduction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">gradient_as_bucket_view</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">static_graph</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">delay_all_reduce_named_params</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">param_to_hook_all_reduce</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mixed_precision</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device_mesh</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/parallel/distributed.html#DistributedDataParallel"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/parallel/distributed.py#L326"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.parallel.DistributedDataParallel" title="Permalink to this definition">¶</a></dt>
 <dd><p>Implement distributed data parallelism based on <code class="docutils literal notranslate"><span class="pre">torch.distributed</span></code> at module level.</p>
 <p>This container provides data parallelism by synchronizing gradients
 across each model replica. The devices to synchronize across are
@@ -936,7 +936,7 @@ <h1>DistributedDataParallel<a class="headerlink" href="#distributeddataparallel"
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.parallel.DistributedDataParallel.join">
-<span class="sig-name descname"><span class="pre">join</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">divide_by_initial_world_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">enable</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">throw_on_early_termination</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/parallel/distributed.html#DistributedDataParallel.join"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/parallel/distributed.py#L1753"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.parallel.DistributedDataParallel.join" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">join</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">divide_by_initial_world_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">enable</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">throw_on_early_termination</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/parallel/distributed.html#DistributedDataParallel.join"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/parallel/distributed.py#L1753"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.parallel.DistributedDataParallel.join" title="Permalink to this definition">¶</a></dt>
 <dd><p>Context manager for training with uneven inputs across processes in DDP.</p>
 <p>This context manager will keep track of already-joined DDP processes,
 and “shadow” the forward and backward passes by inserting collective
@@ -1032,7 +1032,7 @@ <h1>DistributedDataParallel<a class="headerlink" href="#distributeddataparallel"
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.parallel.DistributedDataParallel.join_hook">
-<span class="sig-name descname"><span class="pre">join_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/parallel/distributed.html#DistributedDataParallel.join_hook"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/parallel/distributed.py#L1859"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.parallel.DistributedDataParallel.join_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">join_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/parallel/distributed.html#DistributedDataParallel.join_hook"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/parallel/distributed.py#L1859"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.parallel.DistributedDataParallel.join_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>DDP join hook enables training on uneven inputs by mirroring communications in forward and backward passes.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -1061,7 +1061,7 @@ <h1>DistributedDataParallel<a class="headerlink" href="#distributeddataparallel"
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.parallel.DistributedDataParallel.no_sync">
-<span class="sig-name descname"><span class="pre">no_sync</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/parallel/distributed.html#DistributedDataParallel.no_sync"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/parallel/distributed.py#L1419"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.parallel.DistributedDataParallel.no_sync" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">no_sync</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/parallel/distributed.html#DistributedDataParallel.no_sync"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/parallel/distributed.py#L1419"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.parallel.DistributedDataParallel.no_sync" title="Permalink to this definition">¶</a></dt>
 <dd><p>Context manager to disable gradient synchronizations across DDP processes.</p>
 <p>Within this context, gradients will be accumulated on module
 variables, which will later be synchronized in the first
@@ -1083,7 +1083,7 @@ <h1>DistributedDataParallel<a class="headerlink" href="#distributeddataparallel"
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.parallel.DistributedDataParallel.register_comm_hook">
-<span class="sig-name descname"><span class="pre">register_comm_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/parallel/distributed.html#DistributedDataParallel.register_comm_hook"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/parallel/distributed.py#L1941"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.parallel.DistributedDataParallel.register_comm_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_comm_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/parallel/distributed.html#DistributedDataParallel.register_comm_hook"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/parallel/distributed.py#L1941"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.parallel.DistributedDataParallel.register_comm_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register communication hook for user-defined DDP aggregation of gradients across multiple workers.</p>
 <p>This hook would be very useful for researchers to try out new ideas. For
 example, this hook can be used to implement several algorithms like GossipGrad
diff --git a/main/generated/torch.nn.parameter.Buffer.html b/main/generated/torch.nn.parameter.Buffer.html
index 7986adcc355..5ce318548c3 100644
--- a/main/generated/torch.nn.parameter.Buffer.html
+++ b/main/generated/torch.nn.parameter.Buffer.html
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@@ -612,7 +612,7 @@
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 <dt class="sig sig-object py" id="torch.nn.parameter.Buffer">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.parameter.</span></span><span class="sig-name descname"><span class="pre">Buffer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">data</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">persistent</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/parameter.html#Buffer"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/parameter.py#L225"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.parameter.Buffer" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.parameter.</span></span><span class="sig-name descname"><span class="pre">Buffer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">data</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">persistent</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/parameter.html#Buffer"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/parameter.py#L225"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.parameter.Buffer" title="Permalink to this definition">¶</a></dt>
 <dd><p>A kind of Tensor that should not be considered a model
 parameter. For example, BatchNorm’s <code class="docutils literal notranslate"><span class="pre">running_mean</span></code> is not a parameter, but is part of the module’s state.</p>
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diff --git a/main/generated/torch.nn.parameter.Parameter.html b/main/generated/torch.nn.parameter.Parameter.html
index d46c507b350..c5851c235a7 100644
--- a/main/generated/torch.nn.parameter.Parameter.html
+++ b/main/generated/torch.nn.parameter.Parameter.html
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@@ -612,7 +612,7 @@
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 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.parameter.Parameter">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.parameter.</span></span><span class="sig-name descname"><span class="pre">Parameter</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">data</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">requires_grad</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/parameter.html#Parameter"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/parameter.py#L19"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.parameter.Parameter" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.parameter.</span></span><span class="sig-name descname"><span class="pre">Parameter</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">data</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">requires_grad</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/parameter.html#Parameter"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/parameter.py#L19"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.parameter.Parameter" title="Permalink to this definition">¶</a></dt>
 <dd><p>A kind of Tensor that is to be considered a module parameter.</p>
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 very special property when used with <code class="xref py py-class docutils literal notranslate"><span class="pre">Module</span></code> s - when they’re
diff --git a/main/generated/torch.nn.parameter.UninitializedBuffer.html b/main/generated/torch.nn.parameter.UninitializedBuffer.html
index bf4fc670393..c25a73319c4 100644
--- a/main/generated/torch.nn.parameter.UninitializedBuffer.html
+++ b/main/generated/torch.nn.parameter.UninitializedBuffer.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>UninitializedBuffer<a class="headerlink" href="#uninitializedbuffer" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.parameter.UninitializedBuffer">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.parameter.</span></span><span class="sig-name descname"><span class="pre">UninitializedBuffer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">requires_grad</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">persistent</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/parameter.html#UninitializedBuffer"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/parameter.py#L254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.parameter.UninitializedBuffer" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.parameter.</span></span><span class="sig-name descname"><span class="pre">UninitializedBuffer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">requires_grad</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">persistent</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/parameter.html#UninitializedBuffer"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/parameter.py#L254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.parameter.UninitializedBuffer" title="Permalink to this definition">¶</a></dt>
 <dd><p>A buffer that is not initialized.</p>
 <p>Uninitialized Buffer is a a special case of <a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.Tensor</span></code></a>
 where the shape of the data is still unknown.</p>
diff --git a/main/generated/torch.nn.parameter.UninitializedParameter.html b/main/generated/torch.nn.parameter.UninitializedParameter.html
index d4ddcbfa9ac..a631668d5e5 100644
--- a/main/generated/torch.nn.parameter.UninitializedParameter.html
+++ b/main/generated/torch.nn.parameter.UninitializedParameter.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>UninitializedParameter<a class="headerlink" href="#uninitializedparameter" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.parameter.UninitializedParameter">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.parameter.</span></span><span class="sig-name descname"><span class="pre">UninitializedParameter</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">requires_grad</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/parameter.html#UninitializedParameter"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/parameter.py#L181"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.parameter.UninitializedParameter" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.parameter.</span></span><span class="sig-name descname"><span class="pre">UninitializedParameter</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">requires_grad</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/parameter.html#UninitializedParameter"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/parameter.py#L181"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.parameter.UninitializedParameter" title="Permalink to this definition">¶</a></dt>
 <dd><p>A parameter that is not initialized.</p>
 <p>Uninitialized Parameters are a special case of <code class="xref py py-class docutils literal notranslate"><span class="pre">torch.nn.Parameter</span></code>
 where the shape of the data is still unknown.</p>
@@ -627,7 +627,7 @@ <h1>UninitializedParameter<a class="headerlink" href="#uninitializedparameter" t
 </dl>
 <dl class="py attribute">
 <dt class="sig sig-object py" id="torch.nn.parameter.UninitializedParameter.cls_to_become">
-<span class="sig-name descname"><span class="pre">cls_to_become</span></span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/parameter.py#L19"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.parameter.UninitializedParameter.cls_to_become" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">cls_to_become</span></span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/parameter.py#L19"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.parameter.UninitializedParameter.cls_to_become" title="Permalink to this definition">¶</a></dt>
 <dd><p>alias of <a class="reference internal" href="torch.nn.parameter.Parameter.html#torch.nn.parameter.Parameter" title="torch.nn.parameter.Parameter"><code class="xref py py-class docutils literal notranslate"><span class="pre">Parameter</span></code></a></p>
 </dd></dl>
 
diff --git a/main/generated/torch.nn.utils.clip_grad_norm.html b/main/generated/torch.nn.utils.clip_grad_norm.html
index 52e50b45c63..d4ff8e37d4c 100644
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+++ b/main/generated/torch.nn.utils.clip_grad_norm.html
@@ -300,7 +300,7 @@
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-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
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     <div id="searchBox">
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@@ -612,7 +612,7 @@
 <h1>torch.nn.utils.clip_grad_norm<a class="headerlink" href="#torch-nn-utils-clip-grad-norm" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.utils.clip_grad_norm">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.utils.</span></span><span class="sig-name descname"><span class="pre">clip_grad_norm</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">parameters</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_norm</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">norm_type</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">error_if_nonfinite</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">foreach</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/clip_grad.html#clip_grad_norm"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/utils/clip_grad.py#L224"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.clip_grad_norm" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.utils.</span></span><span class="sig-name descname"><span class="pre">clip_grad_norm</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">parameters</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_norm</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">norm_type</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">error_if_nonfinite</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">foreach</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/clip_grad.html#clip_grad_norm"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/utils/clip_grad.py#L224"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.clip_grad_norm" title="Permalink to this definition">¶</a></dt>
 <dd><p>Clip the gradient norm of an iterable of parameters.</p>
 <div class="admonition warning">
 <p class="admonition-title">Warning</p>
diff --git a/main/generated/torch.nn.utils.clip_grad_norm_.html b/main/generated/torch.nn.utils.clip_grad_norm_.html
index b00e199c749..0a5f58925fe 100644
--- a/main/generated/torch.nn.utils.clip_grad_norm_.html
+++ b/main/generated/torch.nn.utils.clip_grad_norm_.html
@@ -300,7 +300,7 @@
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     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.nn.utils.clip_grad_norm_<a class="headerlink" href="#torch-nn-utils-clip-grad-norm" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.utils.clip_grad_norm_">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.utils.</span></span><span class="sig-name descname"><span class="pre">clip_grad_norm_</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">parameters</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_norm</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">norm_type</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">error_if_nonfinite</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">foreach</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/clip_grad.html#clip_grad_norm_"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/utils/clip_grad.py#L179"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.clip_grad_norm_" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.utils.</span></span><span class="sig-name descname"><span class="pre">clip_grad_norm_</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">parameters</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_norm</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">norm_type</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">error_if_nonfinite</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">foreach</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/clip_grad.html#clip_grad_norm_"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/utils/clip_grad.py#L179"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.clip_grad_norm_" title="Permalink to this definition">¶</a></dt>
 <dd><p>Clip the gradient norm of an iterable of parameters.</p>
 <p>The norm is computed over the norms of the individual gradients of all parameters,
 as if the norms of the individual gradients were concatenated into a single vector.
diff --git a/main/generated/torch.nn.utils.clip_grad_value_.html b/main/generated/torch.nn.utils.clip_grad_value_.html
index e68f9e5c034..29a5c86d44c 100644
--- a/main/generated/torch.nn.utils.clip_grad_value_.html
+++ b/main/generated/torch.nn.utils.clip_grad_value_.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.nn.utils.clip_grad_value_<a class="headerlink" href="#torch-nn-utils-clip-grad-value" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.utils.clip_grad_value_">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.utils.</span></span><span class="sig-name descname"><span class="pre">clip_grad_value_</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">parameters</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">clip_value</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">foreach</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/clip_grad.html#clip_grad_value_"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/utils/clip_grad.py#L245"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.clip_grad_value_" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.utils.</span></span><span class="sig-name descname"><span class="pre">clip_grad_value_</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">parameters</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">clip_value</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">foreach</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/clip_grad.html#clip_grad_value_"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/utils/clip_grad.py#L245"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.clip_grad_value_" title="Permalink to this definition">¶</a></dt>
 <dd><p>Clip the gradients of an iterable of parameters at specified value.</p>
 <p>Gradients are modified in-place.</p>
 <dl class="field-list simple">
diff --git a/main/generated/torch.nn.utils.clip_grads_with_norm_.html b/main/generated/torch.nn.utils.clip_grads_with_norm_.html
index 5a8bb0bdf83..66eef03d0a5 100644
--- a/main/generated/torch.nn.utils.clip_grads_with_norm_.html
+++ b/main/generated/torch.nn.utils.clip_grads_with_norm_.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
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     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.nn.utils.clip_grads_with_norm_<a class="headerlink" href="#torch-nn-utils-clip-grads-with-norm" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.utils.clip_grads_with_norm_">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.utils.</span></span><span class="sig-name descname"><span class="pre">clip_grads_with_norm_</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">parameters</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_norm</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">total_norm</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">foreach</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/utils/clip_grad.py#L115"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.clip_grads_with_norm_" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.utils.</span></span><span class="sig-name descname"><span class="pre">clip_grads_with_norm_</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">parameters</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_norm</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">total_norm</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">foreach</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/utils/clip_grad.py#L115"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.clip_grads_with_norm_" title="Permalink to this definition">¶</a></dt>
 <dd><p>Scale the gradients of an iterable of parameters given a pre-calculated total norm and desired max norm.</p>
 <p>The gradients will be scaled by the following calculation</p>
 <div class="math">
diff --git a/main/generated/torch.nn.utils.convert_conv2d_weight_memory_format.html b/main/generated/torch.nn.utils.convert_conv2d_weight_memory_format.html
index 7ae5dfc8477..5529cf01834 100644
--- a/main/generated/torch.nn.utils.convert_conv2d_weight_memory_format.html
+++ b/main/generated/torch.nn.utils.convert_conv2d_weight_memory_format.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
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     </div>
     <div id="searchBox">
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@@ -612,7 +612,7 @@
 <h1>torch.nn.utils.convert_conv2d_weight_memory_format<a class="headerlink" href="#torch-nn-utils-convert-conv2d-weight-memory-format" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.utils.convert_conv2d_weight_memory_format">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.utils.</span></span><span class="sig-name descname"><span class="pre">convert_conv2d_weight_memory_format</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">memory_format</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/memory_format.html#convert_conv2d_weight_memory_format"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/utils/memory_format.py#L5"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.convert_conv2d_weight_memory_format" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.utils.</span></span><span class="sig-name descname"><span class="pre">convert_conv2d_weight_memory_format</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">memory_format</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/memory_format.html#convert_conv2d_weight_memory_format"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/utils/memory_format.py#L5"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.convert_conv2d_weight_memory_format" title="Permalink to this definition">¶</a></dt>
 <dd><p>Convert <code class="docutils literal notranslate"><span class="pre">memory_format</span></code> of <code class="docutils literal notranslate"><span class="pre">nn.Conv2d.weight</span></code> to <code class="docutils literal notranslate"><span class="pre">memory_format</span></code>.</p>
 <p>The conversion recursively applies to nested <code class="docutils literal notranslate"><span class="pre">nn.Module</span></code>, including <code class="docutils literal notranslate"><span class="pre">module</span></code>.
 Note that it only changes the memory_format, but not the semantics of each dimensions.
diff --git a/main/generated/torch.nn.utils.convert_conv3d_weight_memory_format.html b/main/generated/torch.nn.utils.convert_conv3d_weight_memory_format.html
index 374c23e588e..2642d54ad07 100644
--- a/main/generated/torch.nn.utils.convert_conv3d_weight_memory_format.html
+++ b/main/generated/torch.nn.utils.convert_conv3d_weight_memory_format.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
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@@ -612,7 +612,7 @@
 <h1>torch.nn.utils.convert_conv3d_weight_memory_format<a class="headerlink" href="#torch-nn-utils-convert-conv3d-weight-memory-format" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.utils.convert_conv3d_weight_memory_format">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.utils.</span></span><span class="sig-name descname"><span class="pre">convert_conv3d_weight_memory_format</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">memory_format</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/memory_format.html#convert_conv3d_weight_memory_format"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/utils/memory_format.py#L80"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.convert_conv3d_weight_memory_format" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.utils.</span></span><span class="sig-name descname"><span class="pre">convert_conv3d_weight_memory_format</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">memory_format</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/memory_format.html#convert_conv3d_weight_memory_format"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/utils/memory_format.py#L80"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.convert_conv3d_weight_memory_format" title="Permalink to this definition">¶</a></dt>
 <dd><p>Convert <code class="docutils literal notranslate"><span class="pre">memory_format</span></code> of <code class="docutils literal notranslate"><span class="pre">nn.Conv3d.weight</span></code> to <code class="docutils literal notranslate"><span class="pre">memory_format</span></code>
 The conversion recursively applies to nested <code class="docutils literal notranslate"><span class="pre">nn.Module</span></code>, including <code class="docutils literal notranslate"><span class="pre">module</span></code>.
 Note that it only changes the memory_format, but not the semantics of each dimensions.
diff --git a/main/generated/torch.nn.utils.fuse_conv_bn_eval.html b/main/generated/torch.nn.utils.fuse_conv_bn_eval.html
index 60c6cd065d4..dbf003773e9 100644
--- a/main/generated/torch.nn.utils.fuse_conv_bn_eval.html
+++ b/main/generated/torch.nn.utils.fuse_conv_bn_eval.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
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@@ -612,7 +612,7 @@
 <h1>torch.nn.utils.fuse_conv_bn_eval<a class="headerlink" href="#torch-nn-utils-fuse-conv-bn-eval" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.utils.fuse_conv_bn_eval">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.utils.</span></span><span class="sig-name descname"><span class="pre">fuse_conv_bn_eval</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">conv</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bn</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transpose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/fusion.html#fuse_conv_bn_eval"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/utils/fusion.py#L20"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.fuse_conv_bn_eval" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.utils.</span></span><span class="sig-name descname"><span class="pre">fuse_conv_bn_eval</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">conv</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bn</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transpose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/fusion.html#fuse_conv_bn_eval"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/utils/fusion.py#L20"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.fuse_conv_bn_eval" title="Permalink to this definition">¶</a></dt>
 <dd><p>Fuse a convolutional module and a BatchNorm module into a single, new convolutional module.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.nn.utils.fuse_conv_bn_weights.html b/main/generated/torch.nn.utils.fuse_conv_bn_weights.html
index 2fa051b3c8f..96c362d86b9 100644
--- a/main/generated/torch.nn.utils.fuse_conv_bn_weights.html
+++ b/main/generated/torch.nn.utils.fuse_conv_bn_weights.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.nn.utils.fuse_conv_bn_weights<a class="headerlink" href="#torch-nn-utils-fuse-conv-bn-weights" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.utils.fuse_conv_bn_weights">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.utils.</span></span><span class="sig-name descname"><span class="pre">fuse_conv_bn_weights</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">conv_w</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">conv_b</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bn_rm</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bn_rv</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bn_eps</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bn_w</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bn_b</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transpose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/fusion.html#fuse_conv_bn_weights"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/utils/fusion.py#L56"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.fuse_conv_bn_weights" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.utils.</span></span><span class="sig-name descname"><span class="pre">fuse_conv_bn_weights</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">conv_w</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">conv_b</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bn_rm</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bn_rv</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bn_eps</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bn_w</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bn_b</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transpose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/fusion.html#fuse_conv_bn_weights"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/utils/fusion.py#L56"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.fuse_conv_bn_weights" title="Permalink to this definition">¶</a></dt>
 <dd><p>Fuse convolutional module parameters and BatchNorm module parameters into new convolutional module parameters.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.nn.utils.fuse_linear_bn_eval.html b/main/generated/torch.nn.utils.fuse_linear_bn_eval.html
index 304d9c66899..9f782c54f09 100644
--- a/main/generated/torch.nn.utils.fuse_linear_bn_eval.html
+++ b/main/generated/torch.nn.utils.fuse_linear_bn_eval.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.nn.utils.fuse_linear_bn_eval<a class="headerlink" href="#torch-nn-utils-fuse-linear-bn-eval" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.utils.fuse_linear_bn_eval">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.utils.</span></span><span class="sig-name descname"><span class="pre">fuse_linear_bn_eval</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">linear</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bn</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/fusion.html#fuse_linear_bn_eval"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/utils/fusion.py#L109"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.fuse_linear_bn_eval" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.utils.</span></span><span class="sig-name descname"><span class="pre">fuse_linear_bn_eval</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">linear</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bn</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/fusion.html#fuse_linear_bn_eval"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/utils/fusion.py#L109"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.fuse_linear_bn_eval" title="Permalink to this definition">¶</a></dt>
 <dd><p>Fuse a linear module and a BatchNorm module into a single, new linear module.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.nn.utils.fuse_linear_bn_weights.html b/main/generated/torch.nn.utils.fuse_linear_bn_weights.html
index 25bc1cbf4c5..2bafe690ddd 100644
--- a/main/generated/torch.nn.utils.fuse_linear_bn_weights.html
+++ b/main/generated/torch.nn.utils.fuse_linear_bn_weights.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.nn.utils.fuse_linear_bn_weights<a class="headerlink" href="#torch-nn-utils-fuse-linear-bn-weights" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.utils.fuse_linear_bn_weights">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.utils.</span></span><span class="sig-name descname"><span class="pre">fuse_linear_bn_weights</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">linear_w</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">linear_b</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bn_rm</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bn_rv</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bn_eps</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bn_w</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bn_b</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/fusion.html#fuse_linear_bn_weights"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/utils/fusion.py#L156"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.fuse_linear_bn_weights" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.utils.</span></span><span class="sig-name descname"><span class="pre">fuse_linear_bn_weights</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">linear_w</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">linear_b</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bn_rm</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bn_rv</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bn_eps</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bn_w</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bn_b</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/fusion.html#fuse_linear_bn_weights"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/utils/fusion.py#L156"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.fuse_linear_bn_weights" title="Permalink to this definition">¶</a></dt>
 <dd><p>Fuse linear module parameters and BatchNorm module parameters into new linear module parameters.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.nn.utils.get_total_norm.html b/main/generated/torch.nn.utils.get_total_norm.html
index 17c1d321b9c..5b2f6a988dd 100644
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@@ -612,7 +612,7 @@
 <h1>torch.nn.utils.get_total_norm<a class="headerlink" href="#torch-nn-utils-get-total-norm" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.utils.get_total_norm">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.utils.</span></span><span class="sig-name descname"><span class="pre">get_total_norm</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tensors</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">norm_type</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">error_if_nonfinite</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">foreach</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/utils/clip_grad.py#L44"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.get_total_norm" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.utils.</span></span><span class="sig-name descname"><span class="pre">get_total_norm</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tensors</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">norm_type</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">error_if_nonfinite</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">foreach</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/utils/clip_grad.py#L44"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.get_total_norm" title="Permalink to this definition">¶</a></dt>
 <dd><p>Compute the norm of an iterable of tensors.</p>
 <p>The norm is computed over the norms of the individual tensors, as if the norms of
 the individual tensors were concatenated into a single vector.</p>
diff --git a/main/generated/torch.nn.utils.parameters_to_vector.html b/main/generated/torch.nn.utils.parameters_to_vector.html
index 348098feea5..06c37b3c31a 100644
--- a/main/generated/torch.nn.utils.parameters_to_vector.html
+++ b/main/generated/torch.nn.utils.parameters_to_vector.html
@@ -300,7 +300,7 @@
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     </div>
     <div id="searchBox">
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@@ -612,7 +612,7 @@
 <h1>torch.nn.utils.parameters_to_vector<a class="headerlink" href="#torch-nn-utils-parameters-to-vector" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.utils.parameters_to_vector">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.utils.</span></span><span class="sig-name descname"><span class="pre">parameters_to_vector</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">parameters</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/convert_parameters.html#parameters_to_vector"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/utils/convert_parameters.py#L7"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.parameters_to_vector" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.utils.</span></span><span class="sig-name descname"><span class="pre">parameters_to_vector</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">parameters</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/convert_parameters.html#parameters_to_vector"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/utils/convert_parameters.py#L7"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.parameters_to_vector" title="Permalink to this definition">¶</a></dt>
 <dd><p>Flatten an iterable of parameters into a single vector.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.nn.utils.parametrizations.orthogonal.html b/main/generated/torch.nn.utils.parametrizations.orthogonal.html
index 9f2c706f1c3..7f7c6e6f669 100644
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+++ b/main/generated/torch.nn.utils.parametrizations.orthogonal.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
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@@ -612,7 +612,7 @@
 <h1>torch.nn.utils.parametrizations.orthogonal<a class="headerlink" href="#torch-nn-utils-parametrizations-orthogonal" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.utils.parametrizations.orthogonal">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.utils.parametrizations.</span></span><span class="sig-name descname"><span class="pre">orthogonal</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'weight'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">orthogonal_map</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_trivialization</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/parametrizations.html#orthogonal"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/utils/parametrizations.py#L191"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.parametrizations.orthogonal" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.utils.parametrizations.</span></span><span class="sig-name descname"><span class="pre">orthogonal</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'weight'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">orthogonal_map</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_trivialization</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/parametrizations.html#orthogonal"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/utils/parametrizations.py#L191"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.parametrizations.orthogonal" title="Permalink to this definition">¶</a></dt>
 <dd><p>Apply an orthogonal or unitary parametrization to a matrix or a batch of matrices.</p>
 <p>Letting <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi mathvariant="double-struck">K</mi></mrow><annotation encoding="application/x-tex">\mathbb{K}</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.6889em;"></span><span class="mord mathbb">K</span></span></span></span></span> be <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi mathvariant="double-struck">R</mi></mrow><annotation encoding="application/x-tex">\mathbb{R}</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.6889em;"></span><span class="mord mathbb">R</span></span></span></span></span> or <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi mathvariant="double-struck">C</mi></mrow><annotation encoding="application/x-tex">\mathbb{C}</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.6889em;"></span><span class="mord mathbb">C</span></span></span></span></span>, the parametrized
 matrix <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>Q</mi><mo>∈</mo><msup><mi mathvariant="double-struck">K</mi><mrow><mi>m</mi><mo>×</mo><mi>n</mi></mrow></msup></mrow><annotation encoding="application/x-tex">Q \in \mathbb{K}^{m \times n}</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.8778em;vertical-align:-0.1944em;"></span><span class="mord mathnormal">Q</span><span class="mspace" style="margin-right:0.2778em;"></span><span class="mrel">∈</span><span class="mspace" style="margin-right:0.2778em;"></span></span><span class="base"><span class="strut" style="height:0.7713em;"></span><span class="mord"><span class="mord mathbb">K</span><span class="msupsub"><span class="vlist-t"><span class="vlist-r"><span class="vlist" style="height:0.7713em;"><span style="top:-3.063em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mord mathnormal mtight">m</span><span class="mbin mtight">×</span><span class="mord mathnormal mtight">n</span></span></span></span></span></span></span></span></span></span></span></span></span> is <strong>orthogonal</strong> as</p>
diff --git a/main/generated/torch.nn.utils.parametrizations.spectral_norm.html b/main/generated/torch.nn.utils.parametrizations.spectral_norm.html
index 13c44cdb503..7e4f63612b7 100644
--- a/main/generated/torch.nn.utils.parametrizations.spectral_norm.html
+++ b/main/generated/torch.nn.utils.parametrizations.spectral_norm.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
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@@ -612,7 +612,7 @@
 <h1>torch.nn.utils.parametrizations.spectral_norm<a class="headerlink" href="#torch-nn-utils-parametrizations-spectral-norm" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.utils.parametrizations.spectral_norm">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.utils.parametrizations.</span></span><span class="sig-name descname"><span class="pre">spectral_norm</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'weight'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_power_iterations</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-12</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/parametrizations.html#spectral_norm"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/utils/parametrizations.py#L527"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.parametrizations.spectral_norm" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.utils.parametrizations.</span></span><span class="sig-name descname"><span class="pre">spectral_norm</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'weight'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_power_iterations</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-12</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/parametrizations.html#spectral_norm"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/utils/parametrizations.py#L527"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.parametrizations.spectral_norm" title="Permalink to this definition">¶</a></dt>
 <dd><p>Apply spectral normalization to a parameter in the given module.</p>
 <div class="math">
 <span class="katex-display"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML" display="block"><semantics><mrow><msub><mi mathvariant="bold">W</mi><mrow><mi>S</mi><mi>N</mi></mrow></msub><mo>=</mo><mfrac><mi mathvariant="bold">W</mi><mrow><mi>σ</mi><mo stretchy="false">(</mo><mi mathvariant="bold">W</mi><mo stretchy="false">)</mo></mrow></mfrac><mo separator="true">,</mo><mi>σ</mi><mo stretchy="false">(</mo><mi mathvariant="bold">W</mi><mo stretchy="false">)</mo><mo>=</mo><munder><mrow><mi>max</mi><mo>⁡</mo></mrow><mrow><mi mathvariant="bold">h</mi><mo>:</mo><mi mathvariant="bold">h</mi><mo mathvariant="normal">≠</mo><mn>0</mn></mrow></munder><mfrac><mrow><mi mathvariant="normal">∥</mi><mi mathvariant="bold">W</mi><mi mathvariant="bold">h</mi><msub><mi mathvariant="normal">∥</mi><mn>2</mn></msub></mrow><mrow><mi mathvariant="normal">∥</mi><mi mathvariant="bold">h</mi><msub><mi mathvariant="normal">∥</mi><mn>2</mn></msub></mrow></mfrac></mrow><annotation encoding="application/x-tex">\mathbf{W}_{SN} = \dfrac{\mathbf{W}}{\sigma(\mathbf{W})},
diff --git a/main/generated/torch.nn.utils.parametrizations.weight_norm.html b/main/generated/torch.nn.utils.parametrizations.weight_norm.html
index 072ccc70430..c4727840c96 100644
--- a/main/generated/torch.nn.utils.parametrizations.weight_norm.html
+++ b/main/generated/torch.nn.utils.parametrizations.weight_norm.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
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     <div id="searchBox">
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@@ -612,7 +612,7 @@
 <h1>torch.nn.utils.parametrizations.weight_norm<a class="headerlink" href="#torch-nn-utils-parametrizations-weight-norm" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.utils.parametrizations.weight_norm">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.utils.parametrizations.</span></span><span class="sig-name descname"><span class="pre">weight_norm</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'weight'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/parametrizations.html#weight_norm"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/utils/parametrizations.py#L334"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.parametrizations.weight_norm" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.utils.parametrizations.</span></span><span class="sig-name descname"><span class="pre">weight_norm</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'weight'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/parametrizations.html#weight_norm"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/utils/parametrizations.py#L334"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.parametrizations.weight_norm" title="Permalink to this definition">¶</a></dt>
 <dd><p>Apply weight normalization to a parameter in the given module.</p>
 <div class="math">
 <span class="katex-display"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML" display="block"><semantics><mrow><mi mathvariant="bold">w</mi><mo>=</mo><mi>g</mi><mfrac><mi mathvariant="bold">v</mi><mrow><mi mathvariant="normal">∥</mi><mi mathvariant="bold">v</mi><mi mathvariant="normal">∥</mi></mrow></mfrac></mrow><annotation encoding="application/x-tex">\mathbf{w} = g \dfrac{\mathbf{v}}{\|\mathbf{v}\|}
diff --git a/main/generated/torch.nn.utils.parametrize.ParametrizationList.html b/main/generated/torch.nn.utils.parametrize.ParametrizationList.html
index 5c965d0626e..1b703b96772 100644
--- a/main/generated/torch.nn.utils.parametrize.ParametrizationList.html
+++ b/main/generated/torch.nn.utils.parametrize.ParametrizationList.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
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     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>ParametrizationList<a class="headerlink" href="#parametrizationlist" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.utils.parametrize.ParametrizationList">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.utils.parametrize.</span></span><span class="sig-name descname"><span class="pre">ParametrizationList</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">modules</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">original</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">unsafe</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/parametrize.html#ParametrizationList"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/utils/parametrize.py#L93"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.parametrize.ParametrizationList" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.utils.parametrize.</span></span><span class="sig-name descname"><span class="pre">ParametrizationList</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">modules</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">original</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">unsafe</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/parametrize.html#ParametrizationList"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/utils/parametrize.py#L93"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.parametrize.ParametrizationList" title="Permalink to this definition">¶</a></dt>
 <dd><p>A sequential container that holds and manages the original parameters or buffers of a parametrized <a class="reference internal" href="torch.nn.Module.html#torch.nn.Module" title="torch.nn.Module"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.nn.Module</span></code></a>.</p>
 <p>It is the type of <code class="docutils literal notranslate"><span class="pre">module.parametrizations[tensor_name]</span></code> when <code class="docutils literal notranslate"><span class="pre">module[tensor_name]</span></code>
 has been parametrized with <a class="reference internal" href="torch.nn.utils.parametrize.register_parametrization.html#torch.nn.utils.parametrize.register_parametrization" title="torch.nn.utils.parametrize.register_parametrization"><code class="xref py py-func docutils literal notranslate"><span class="pre">register_parametrization()</span></code></a>.</p>
@@ -640,7 +640,7 @@ <h1>ParametrizationList<a class="headerlink" href="#parametrizationlist" title="
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.utils.parametrize.ParametrizationList.right_inverse">
-<span class="sig-name descname"><span class="pre">right_inverse</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/parametrize.html#ParametrizationList.right_inverse"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/utils/parametrize.py#L231"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.parametrize.ParametrizationList.right_inverse" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">right_inverse</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/parametrize.html#ParametrizationList.right_inverse"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/utils/parametrize.py#L231"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.parametrize.ParametrizationList.right_inverse" title="Permalink to this definition">¶</a></dt>
 <dd><p>Call the <code class="docutils literal notranslate"><span class="pre">right_inverse</span></code> methods of the parametrizations in the inverse registration order.</p>
 <p>Then, it stores the result in <code class="docutils literal notranslate"><span class="pre">self.original</span></code> if <code class="docutils literal notranslate"><span class="pre">right_inverse</span></code> outputs one tensor
 or in <code class="docutils literal notranslate"><span class="pre">self.original0</span></code>, <code class="docutils literal notranslate"><span class="pre">self.original1</span></code>, … if it outputs several.</p>
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index 9585d25a70e..b8b5fabb3ba 100644
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     </div>
     <div id="searchBox">
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@@ -612,7 +612,7 @@
 <h1>torch.nn.utils.parametrize.cached<a class="headerlink" href="#torch-nn-utils-parametrize-cached" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.utils.parametrize.cached">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.utils.parametrize.</span></span><span class="sig-name descname"><span class="pre">cached</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/parametrize.html#cached"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/utils/parametrize.py#L32"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.parametrize.cached" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.utils.parametrize.</span></span><span class="sig-name descname"><span class="pre">cached</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/parametrize.html#cached"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/utils/parametrize.py#L32"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.parametrize.cached" title="Permalink to this definition">¶</a></dt>
 <dd><p>Context manager that enables the caching system within parametrizations registered with <a class="reference internal" href="torch.nn.utils.parametrize.register_parametrization.html#torch.nn.utils.parametrize.register_parametrization" title="torch.nn.utils.parametrize.register_parametrization"><code class="xref py py-func docutils literal notranslate"><span class="pre">register_parametrization()</span></code></a>.</p>
 <p>The value of the parametrized objects is computed and cached the first time
 they are required when this context manager is active. The cached values are
diff --git a/main/generated/torch.nn.utils.parametrize.is_parametrized.html b/main/generated/torch.nn.utils.parametrize.is_parametrized.html
index 4d1cc4608ee..cab217fe86f 100644
--- a/main/generated/torch.nn.utils.parametrize.is_parametrized.html
+++ b/main/generated/torch.nn.utils.parametrize.is_parametrized.html
@@ -300,7 +300,7 @@
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+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
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@@ -612,7 +612,7 @@
 <h1>torch.nn.utils.parametrize.is_parametrized<a class="headerlink" href="#torch-nn-utils-parametrize-is-parametrized" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.utils.parametrize.is_parametrized">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.utils.parametrize.</span></span><span class="sig-name descname"><span class="pre">is_parametrized</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tensor_name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/parametrize.html#is_parametrized"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/utils/parametrize.py#L630"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.parametrize.is_parametrized" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.utils.parametrize.</span></span><span class="sig-name descname"><span class="pre">is_parametrized</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tensor_name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/parametrize.html#is_parametrized"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/utils/parametrize.py#L630"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.parametrize.is_parametrized" title="Permalink to this definition">¶</a></dt>
 <dd><p>Determine if a module has a parametrization.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.nn.utils.parametrize.register_parametrization.html b/main/generated/torch.nn.utils.parametrize.register_parametrization.html
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@@ -612,7 +612,7 @@
 <h1>torch.nn.utils.parametrize.register_parametrization<a class="headerlink" href="#torch-nn-utils-parametrize-register-parametrization" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.utils.parametrize.register_parametrization">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.utils.parametrize.</span></span><span class="sig-name descname"><span class="pre">register_parametrization</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tensor_name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">parametrization</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">unsafe</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/parametrize.html#register_parametrization"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/utils/parametrize.py#L416"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.parametrize.register_parametrization" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.utils.parametrize.</span></span><span class="sig-name descname"><span class="pre">register_parametrization</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tensor_name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">parametrization</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">unsafe</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/parametrize.html#register_parametrization"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/utils/parametrize.py#L416"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.parametrize.register_parametrization" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register a parametrization to a tensor in a module.</p>
 <p>Assume that <code class="docutils literal notranslate"><span class="pre">tensor_name=&quot;weight&quot;</span></code> for simplicity. When accessing <code class="docutils literal notranslate"><span class="pre">module.weight</span></code>,
 the module will return the parametrized version <code class="docutils literal notranslate"><span class="pre">parametrization(module.weight)</span></code>.
diff --git a/main/generated/torch.nn.utils.parametrize.remove_parametrizations.html b/main/generated/torch.nn.utils.parametrize.remove_parametrizations.html
index d5c7da1ee54..a4968a6f32a 100644
--- a/main/generated/torch.nn.utils.parametrize.remove_parametrizations.html
+++ b/main/generated/torch.nn.utils.parametrize.remove_parametrizations.html
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+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.nn.utils.parametrize.remove_parametrizations<a class="headerlink" href="#torch-nn-utils-parametrize-remove-parametrizations" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.utils.parametrize.remove_parametrizations">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.utils.parametrize.</span></span><span class="sig-name descname"><span class="pre">remove_parametrizations</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tensor_name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">leave_parametrized</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/parametrize.html#remove_parametrizations"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/utils/parametrize.py#L652"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.parametrize.remove_parametrizations" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.utils.parametrize.</span></span><span class="sig-name descname"><span class="pre">remove_parametrizations</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tensor_name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">leave_parametrized</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/parametrize.html#remove_parametrizations"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/utils/parametrize.py#L652"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.parametrize.remove_parametrizations" title="Permalink to this definition">¶</a></dt>
 <dd><p>Remove the parametrizations on a tensor in a module.</p>
 <ul class="simple">
 <li><p>If <code class="docutils literal notranslate"><span class="pre">leave_parametrized=True</span></code>, <code class="docutils literal notranslate"><span class="pre">module[tensor_name]</span></code> will be set to
diff --git a/main/generated/torch.nn.utils.prune.BasePruningMethod.html b/main/generated/torch.nn.utils.prune.BasePruningMethod.html
index 7e643b73d57..12a1dc09790 100644
--- a/main/generated/torch.nn.utils.prune.BasePruningMethod.html
+++ b/main/generated/torch.nn.utils.prune.BasePruningMethod.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,13 +612,13 @@
 <h1>BasePruningMethod<a class="headerlink" href="#basepruningmethod" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.utils.prune.BasePruningMethod">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.utils.prune.</span></span><span class="sig-name descname"><span class="pre">BasePruningMethod</span></span><a class="reference internal" href="../_modules/torch/nn/utils/prune.html#BasePruningMethod"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/utils/prune.py#L10"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.prune.BasePruningMethod" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.utils.prune.</span></span><span class="sig-name descname"><span class="pre">BasePruningMethod</span></span><a class="reference internal" href="../_modules/torch/nn/utils/prune.html#BasePruningMethod"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/utils/prune.py#L10"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.prune.BasePruningMethod" title="Permalink to this definition">¶</a></dt>
 <dd><p>Abstract base class for creation of new pruning techniques.</p>
 <p>Provides a skeleton for customization requiring the overriding of methods
 such as <a class="reference internal" href="#torch.nn.utils.prune.BasePruningMethod.compute_mask" title="torch.nn.utils.prune.BasePruningMethod.compute_mask"><code class="xref py py-meth docutils literal notranslate"><span class="pre">compute_mask()</span></code></a> and <a class="reference internal" href="#torch.nn.utils.prune.BasePruningMethod.apply" title="torch.nn.utils.prune.BasePruningMethod.apply"><code class="xref py py-meth docutils literal notranslate"><span class="pre">apply()</span></code></a>.</p>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.utils.prune.BasePruningMethod.apply">
-<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">apply</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">importance_scores</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/prune.html#BasePruningMethod.apply"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/utils/prune.py#L74"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.prune.BasePruningMethod.apply" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">apply</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">importance_scores</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/prune.html#BasePruningMethod.apply"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/utils/prune.py#L74"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.prune.BasePruningMethod.apply" title="Permalink to this definition">¶</a></dt>
 <dd><p>Add pruning on the fly and reparametrization of a tensor.</p>
 <p>Adds the forward pre-hook that enables pruning on the fly and
 the reparametrization of a tensor in terms of the original tensor
@@ -645,7 +645,7 @@ <h1>BasePruningMethod<a class="headerlink" href="#basepruningmethod" title="Perm
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.utils.prune.BasePruningMethod.apply_mask">
-<span class="sig-name descname"><span class="pre">apply_mask</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/prune.html#BasePruningMethod.apply_mask"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/utils/prune.py#L52"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.prune.BasePruningMethod.apply_mask" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">apply_mask</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/prune.html#BasePruningMethod.apply_mask"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/utils/prune.py#L52"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.prune.BasePruningMethod.apply_mask" title="Permalink to this definition">¶</a></dt>
 <dd><p>Simply handles the multiplication between the parameter being pruned and the generated mask.</p>
 <p>Fetches the mask and the original tensor from the module
 and returns the pruned version of the tensor.</p>
@@ -664,7 +664,7 @@ <h1>BasePruningMethod<a class="headerlink" href="#basepruningmethod" title="Perm
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.utils.prune.BasePruningMethod.compute_mask">
-<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">compute_mask</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">t</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">default_mask</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/prune.html#BasePruningMethod.compute_mask"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/utils/prune.py#L32"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.prune.BasePruningMethod.compute_mask" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">compute_mask</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">t</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">default_mask</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/prune.html#BasePruningMethod.compute_mask"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/utils/prune.py#L32"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.prune.BasePruningMethod.compute_mask" title="Permalink to this definition">¶</a></dt>
 <dd><p>Compute and returns a mask for the input tensor <code class="docutils literal notranslate"><span class="pre">t</span></code>.</p>
 <p>Starting from a base <code class="docutils literal notranslate"><span class="pre">default_mask</span></code> (which should be a mask of ones
 if the tensor has not been pruned yet), generate a random mask to
@@ -692,7 +692,7 @@ <h1>BasePruningMethod<a class="headerlink" href="#basepruningmethod" title="Perm
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.utils.prune.BasePruningMethod.prune">
-<span class="sig-name descname"><span class="pre">prune</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">t</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">default_mask</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">importance_scores</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/prune.html#BasePruningMethod.prune"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/utils/prune.py#L203"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.prune.BasePruningMethod.prune" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">prune</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">t</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">default_mask</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">importance_scores</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/prune.html#BasePruningMethod.prune"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/utils/prune.py#L203"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.prune.BasePruningMethod.prune" title="Permalink to this definition">¶</a></dt>
 <dd><p>Compute and returns a pruned version of input tensor <code class="docutils literal notranslate"><span class="pre">t</span></code>.</p>
 <p>According to the pruning rule specified in <a class="reference internal" href="#torch.nn.utils.prune.BasePruningMethod.compute_mask" title="torch.nn.utils.prune.BasePruningMethod.compute_mask"><code class="xref py py-meth docutils literal notranslate"><span class="pre">compute_mask()</span></code></a>.</p>
 <dl class="field-list simple">
@@ -719,7 +719,7 @@ <h1>BasePruningMethod<a class="headerlink" href="#basepruningmethod" title="Perm
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.utils.prune.BasePruningMethod.remove">
-<span class="sig-name descname"><span class="pre">remove</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/prune.html#BasePruningMethod.remove"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/utils/prune.py#L233"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.prune.BasePruningMethod.remove" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">remove</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/prune.html#BasePruningMethod.remove"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/utils/prune.py#L233"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.prune.BasePruningMethod.remove" title="Permalink to this definition">¶</a></dt>
 <dd><p>Remove the pruning reparameterization from a module.</p>
 <p>The pruned parameter named <code class="docutils literal notranslate"><span class="pre">name</span></code> remains permanently pruned,
 and the parameter named <code class="docutils literal notranslate"><span class="pre">name+'_orig'</span></code> is removed from the parameter list.
diff --git a/main/generated/torch.nn.utils.prune.CustomFromMask.html b/main/generated/torch.nn.utils.prune.CustomFromMask.html
index 0fb10d63885..5d134171ac7 100644
--- a/main/generated/torch.nn.utils.prune.CustomFromMask.html
+++ b/main/generated/torch.nn.utils.prune.CustomFromMask.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,10 +612,10 @@
 <h1>CustomFromMask<a class="headerlink" href="#customfrommask" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.utils.prune.CustomFromMask">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.utils.prune.</span></span><span class="sig-name descname"><span class="pre">CustomFromMask</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mask</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/prune.html#CustomFromMask"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/utils/prune.py#L791"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.prune.CustomFromMask" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.utils.prune.</span></span><span class="sig-name descname"><span class="pre">CustomFromMask</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mask</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/prune.html#CustomFromMask"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/utils/prune.py#L791"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.prune.CustomFromMask" title="Permalink to this definition">¶</a></dt>
 <dd><dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.utils.prune.CustomFromMask.apply">
-<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">apply</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mask</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/prune.html#CustomFromMask.apply"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/utils/prune.py#L802"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.prune.CustomFromMask.apply" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">apply</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mask</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/prune.html#CustomFromMask.apply"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/utils/prune.py#L802"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.prune.CustomFromMask.apply" title="Permalink to this definition">¶</a></dt>
 <dd><p>Add pruning on the fly and reparametrization of a tensor.</p>
 <p>Adds the forward pre-hook that enables pruning on the fly and
 the reparametrization of a tensor in terms of the original tensor
@@ -633,7 +633,7 @@ <h1>CustomFromMask<a class="headerlink" href="#customfrommask" title="Permalink
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.utils.prune.CustomFromMask.apply_mask">
-<span class="sig-name descname"><span class="pre">apply_mask</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/utils/prune.py#L52"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.prune.CustomFromMask.apply_mask" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">apply_mask</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/utils/prune.py#L52"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.prune.CustomFromMask.apply_mask" title="Permalink to this definition">¶</a></dt>
 <dd><p>Simply handles the multiplication between the parameter being pruned and the generated mask.</p>
 <p>Fetches the mask and the original tensor from the module
 and returns the pruned version of the tensor.</p>
@@ -652,7 +652,7 @@ <h1>CustomFromMask<a class="headerlink" href="#customfrommask" title="Permalink
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.utils.prune.CustomFromMask.prune">
-<span class="sig-name descname"><span class="pre">prune</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">t</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">default_mask</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">importance_scores</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/utils/prune.py#L203"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.prune.CustomFromMask.prune" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">prune</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">t</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">default_mask</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">importance_scores</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/utils/prune.py#L203"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.prune.CustomFromMask.prune" title="Permalink to this definition">¶</a></dt>
 <dd><p>Compute and returns a pruned version of input tensor <code class="docutils literal notranslate"><span class="pre">t</span></code>.</p>
 <p>According to the pruning rule specified in <code class="xref py py-meth docutils literal notranslate"><span class="pre">compute_mask()</span></code>.</p>
 <dl class="field-list simple">
@@ -679,7 +679,7 @@ <h1>CustomFromMask<a class="headerlink" href="#customfrommask" title="Permalink
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.utils.prune.CustomFromMask.remove">
-<span class="sig-name descname"><span class="pre">remove</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/utils/prune.py#L233"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.prune.CustomFromMask.remove" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">remove</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/utils/prune.py#L233"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.prune.CustomFromMask.remove" title="Permalink to this definition">¶</a></dt>
 <dd><p>Remove the pruning reparameterization from a module.</p>
 <p>The pruned parameter named <code class="docutils literal notranslate"><span class="pre">name</span></code> remains permanently pruned,
 and the parameter named <code class="docutils literal notranslate"><span class="pre">name+'_orig'</span></code> is removed from the parameter list.
diff --git a/main/generated/torch.nn.utils.prune.Identity.html b/main/generated/torch.nn.utils.prune.Identity.html
index 2935d34ef2c..e64c4bdcc1f 100644
--- a/main/generated/torch.nn.utils.prune.Identity.html
+++ b/main/generated/torch.nn.utils.prune.Identity.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,11 +612,11 @@
 <h1>Identity<a class="headerlink" href="#identity" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.utils.prune.Identity">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.utils.prune.</span></span><span class="sig-name descname"><span class="pre">Identity</span></span><a class="reference internal" href="../_modules/torch/nn/utils/prune.html#Identity"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/utils/prune.py#L407"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.prune.Identity" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.utils.prune.</span></span><span class="sig-name descname"><span class="pre">Identity</span></span><a class="reference internal" href="../_modules/torch/nn/utils/prune.html#Identity"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/utils/prune.py#L407"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.prune.Identity" title="Permalink to this definition">¶</a></dt>
 <dd><p>Utility pruning method that does not prune any units but generates the pruning parametrization with a mask of ones.</p>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.utils.prune.Identity.apply">
-<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">apply</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">name</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/prune.html#Identity.apply"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/utils/prune.py#L416"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.prune.Identity.apply" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">apply</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">name</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/prune.html#Identity.apply"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/utils/prune.py#L416"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.prune.Identity.apply" title="Permalink to this definition">¶</a></dt>
 <dd><p>Add pruning on the fly and reparametrization of a tensor.</p>
 <p>Adds the forward pre-hook that enables pruning on the fly and
 the reparametrization of a tensor in terms of the original tensor
@@ -634,7 +634,7 @@ <h1>Identity<a class="headerlink" href="#identity" title="Permalink to this head
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.utils.prune.Identity.apply_mask">
-<span class="sig-name descname"><span class="pre">apply_mask</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/utils/prune.py#L52"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.prune.Identity.apply_mask" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">apply_mask</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/utils/prune.py#L52"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.prune.Identity.apply_mask" title="Permalink to this definition">¶</a></dt>
 <dd><p>Simply handles the multiplication between the parameter being pruned and the generated mask.</p>
 <p>Fetches the mask and the original tensor from the module
 and returns the pruned version of the tensor.</p>
@@ -653,7 +653,7 @@ <h1>Identity<a class="headerlink" href="#identity" title="Permalink to this head
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.utils.prune.Identity.prune">
-<span class="sig-name descname"><span class="pre">prune</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">t</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">default_mask</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">importance_scores</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/utils/prune.py#L203"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.prune.Identity.prune" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">prune</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">t</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">default_mask</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">importance_scores</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/utils/prune.py#L203"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.prune.Identity.prune" title="Permalink to this definition">¶</a></dt>
 <dd><p>Compute and returns a pruned version of input tensor <code class="docutils literal notranslate"><span class="pre">t</span></code>.</p>
 <p>According to the pruning rule specified in <code class="xref py py-meth docutils literal notranslate"><span class="pre">compute_mask()</span></code>.</p>
 <dl class="field-list simple">
@@ -680,7 +680,7 @@ <h1>Identity<a class="headerlink" href="#identity" title="Permalink to this head
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.utils.prune.Identity.remove">
-<span class="sig-name descname"><span class="pre">remove</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/utils/prune.py#L233"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.prune.Identity.remove" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">remove</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/utils/prune.py#L233"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.prune.Identity.remove" title="Permalink to this definition">¶</a></dt>
 <dd><p>Remove the pruning reparameterization from a module.</p>
 <p>The pruned parameter named <code class="docutils literal notranslate"><span class="pre">name</span></code> remains permanently pruned,
 and the parameter named <code class="docutils literal notranslate"><span class="pre">name+'_orig'</span></code> is removed from the parameter list.
diff --git a/main/generated/torch.nn.utils.prune.L1Unstructured.html b/main/generated/torch.nn.utils.prune.L1Unstructured.html
index ea639454b92..301fbf38839 100644
--- a/main/generated/torch.nn.utils.prune.L1Unstructured.html
+++ b/main/generated/torch.nn.utils.prune.L1Unstructured.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>L1Unstructured<a class="headerlink" href="#l1unstructured" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.utils.prune.L1Unstructured">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.utils.prune.</span></span><span class="sig-name descname"><span class="pre">L1Unstructured</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">amount</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/prune.html#L1Unstructured"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/utils/prune.py#L491"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.prune.L1Unstructured" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.utils.prune.</span></span><span class="sig-name descname"><span class="pre">L1Unstructured</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">amount</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/prune.html#L1Unstructured"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/utils/prune.py#L491"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.prune.L1Unstructured" title="Permalink to this definition">¶</a></dt>
 <dd><p>Prune (currently unpruned) units in a tensor by zeroing out the ones with the lowest L1-norm.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -624,7 +624,7 @@ <h1>L1Unstructured<a class="headerlink" href="#l1unstructured" title="Permalink
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.utils.prune.L1Unstructured.apply">
-<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">apply</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">amount</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">importance_scores</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/prune.html#L1Unstructured.apply"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/utils/prune.py#L530"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.prune.L1Unstructured.apply" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">apply</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">amount</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">importance_scores</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/prune.html#L1Unstructured.apply"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/utils/prune.py#L530"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.prune.L1Unstructured.apply" title="Permalink to this definition">¶</a></dt>
 <dd><p>Add pruning on the fly and reparametrization of a tensor.</p>
 <p>Adds the forward pre-hook that enables pruning on the fly and
 the reparametrization of a tensor in terms of the original tensor
@@ -651,7 +651,7 @@ <h1>L1Unstructured<a class="headerlink" href="#l1unstructured" title="Permalink
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.utils.prune.L1Unstructured.apply_mask">
-<span class="sig-name descname"><span class="pre">apply_mask</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/utils/prune.py#L52"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.prune.L1Unstructured.apply_mask" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">apply_mask</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/utils/prune.py#L52"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.prune.L1Unstructured.apply_mask" title="Permalink to this definition">¶</a></dt>
 <dd><p>Simply handles the multiplication between the parameter being pruned and the generated mask.</p>
 <p>Fetches the mask and the original tensor from the module
 and returns the pruned version of the tensor.</p>
@@ -670,7 +670,7 @@ <h1>L1Unstructured<a class="headerlink" href="#l1unstructured" title="Permalink
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.utils.prune.L1Unstructured.prune">
-<span class="sig-name descname"><span class="pre">prune</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">t</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">default_mask</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">importance_scores</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/utils/prune.py#L203"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.prune.L1Unstructured.prune" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">prune</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">t</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">default_mask</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">importance_scores</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/utils/prune.py#L203"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.prune.L1Unstructured.prune" title="Permalink to this definition">¶</a></dt>
 <dd><p>Compute and returns a pruned version of input tensor <code class="docutils literal notranslate"><span class="pre">t</span></code>.</p>
 <p>According to the pruning rule specified in <code class="xref py py-meth docutils literal notranslate"><span class="pre">compute_mask()</span></code>.</p>
 <dl class="field-list simple">
@@ -697,7 +697,7 @@ <h1>L1Unstructured<a class="headerlink" href="#l1unstructured" title="Permalink
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.utils.prune.L1Unstructured.remove">
-<span class="sig-name descname"><span class="pre">remove</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/utils/prune.py#L233"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.prune.L1Unstructured.remove" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">remove</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/utils/prune.py#L233"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.prune.L1Unstructured.remove" title="Permalink to this definition">¶</a></dt>
 <dd><p>Remove the pruning reparameterization from a module.</p>
 <p>The pruned parameter named <code class="docutils literal notranslate"><span class="pre">name</span></code> remains permanently pruned,
 and the parameter named <code class="docutils literal notranslate"><span class="pre">name+'_orig'</span></code> is removed from the parameter list.
diff --git a/main/generated/torch.nn.utils.prune.LnStructured.html b/main/generated/torch.nn.utils.prune.LnStructured.html
index 8a34b0d8474..0ec98b01928 100644
--- a/main/generated/torch.nn.utils.prune.LnStructured.html
+++ b/main/generated/torch.nn.utils.prune.LnStructured.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>LnStructured<a class="headerlink" href="#lnstructured" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.utils.prune.LnStructured">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.utils.prune.</span></span><span class="sig-name descname"><span class="pre">LnStructured</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">amount</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-1</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/prune.html#LnStructured"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/utils/prune.py#L662"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.prune.LnStructured" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.utils.prune.</span></span><span class="sig-name descname"><span class="pre">LnStructured</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">amount</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-1</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/prune.html#LnStructured"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/utils/prune.py#L662"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.prune.LnStructured" title="Permalink to this definition">¶</a></dt>
 <dd><p>Prune entire (currently unpruned) channels in a tensor based on their L<code class="docutils literal notranslate"><span class="pre">n</span></code>-norm.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -630,7 +630,7 @@ <h1>LnStructured<a class="headerlink" href="#lnstructured" title="Permalink to t
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.utils.prune.LnStructured.apply">
-<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">apply</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">amount</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dim</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">importance_scores</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/prune.html#LnStructured.apply"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/utils/prune.py#L755"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.prune.LnStructured.apply" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">apply</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">amount</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dim</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">importance_scores</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/prune.html#LnStructured.apply"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/utils/prune.py#L755"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.prune.LnStructured.apply" title="Permalink to this definition">¶</a></dt>
 <dd><p>Add pruning on the fly and reparametrization of a tensor.</p>
 <p>Adds the forward pre-hook that enables pruning on the fly and
 the reparametrization of a tensor in terms of the original tensor
@@ -661,7 +661,7 @@ <h1>LnStructured<a class="headerlink" href="#lnstructured" title="Permalink to t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.utils.prune.LnStructured.apply_mask">
-<span class="sig-name descname"><span class="pre">apply_mask</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/utils/prune.py#L52"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.prune.LnStructured.apply_mask" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">apply_mask</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/utils/prune.py#L52"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.prune.LnStructured.apply_mask" title="Permalink to this definition">¶</a></dt>
 <dd><p>Simply handles the multiplication between the parameter being pruned and the generated mask.</p>
 <p>Fetches the mask and the original tensor from the module
 and returns the pruned version of the tensor.</p>
@@ -680,7 +680,7 @@ <h1>LnStructured<a class="headerlink" href="#lnstructured" title="Permalink to t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.utils.prune.LnStructured.compute_mask">
-<span class="sig-name descname"><span class="pre">compute_mask</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">t</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">default_mask</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/prune.html#LnStructured.compute_mask"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/utils/prune.py#L685"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.prune.LnStructured.compute_mask" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">compute_mask</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">t</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">default_mask</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/prune.html#LnStructured.compute_mask"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/utils/prune.py#L685"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.prune.LnStructured.compute_mask" title="Permalink to this definition">¶</a></dt>
 <dd><p>Compute and returns a mask for the input tensor <code class="docutils literal notranslate"><span class="pre">t</span></code>.</p>
 <p>Starting from a base <code class="docutils literal notranslate"><span class="pre">default_mask</span></code> (which should be a mask of ones
 if the tensor has not been pruned yet), generate a mask to apply on
@@ -709,7 +709,7 @@ <h1>LnStructured<a class="headerlink" href="#lnstructured" title="Permalink to t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.utils.prune.LnStructured.prune">
-<span class="sig-name descname"><span class="pre">prune</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">t</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">default_mask</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">importance_scores</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/utils/prune.py#L203"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.prune.LnStructured.prune" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">prune</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">t</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">default_mask</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">importance_scores</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/utils/prune.py#L203"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.prune.LnStructured.prune" title="Permalink to this definition">¶</a></dt>
 <dd><p>Compute and returns a pruned version of input tensor <code class="docutils literal notranslate"><span class="pre">t</span></code>.</p>
 <p>According to the pruning rule specified in <a class="reference internal" href="#torch.nn.utils.prune.LnStructured.compute_mask" title="torch.nn.utils.prune.LnStructured.compute_mask"><code class="xref py py-meth docutils literal notranslate"><span class="pre">compute_mask()</span></code></a>.</p>
 <dl class="field-list simple">
@@ -736,7 +736,7 @@ <h1>LnStructured<a class="headerlink" href="#lnstructured" title="Permalink to t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.utils.prune.LnStructured.remove">
-<span class="sig-name descname"><span class="pre">remove</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/utils/prune.py#L233"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.prune.LnStructured.remove" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">remove</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/utils/prune.py#L233"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.prune.LnStructured.remove" title="Permalink to this definition">¶</a></dt>
 <dd><p>Remove the pruning reparameterization from a module.</p>
 <p>The pruned parameter named <code class="docutils literal notranslate"><span class="pre">name</span></code> remains permanently pruned,
 and the parameter named <code class="docutils literal notranslate"><span class="pre">name+'_orig'</span></code> is removed from the parameter list.
diff --git a/main/generated/torch.nn.utils.prune.PruningContainer.html b/main/generated/torch.nn.utils.prune.PruningContainer.html
index fa37b8a413d..aab02774bce 100644
--- a/main/generated/torch.nn.utils.prune.PruningContainer.html
+++ b/main/generated/torch.nn.utils.prune.PruningContainer.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>PruningContainer<a class="headerlink" href="#pruningcontainer" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.utils.prune.PruningContainer">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.utils.prune.</span></span><span class="sig-name descname"><span class="pre">PruningContainer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/prune.html#PruningContainer"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/utils/prune.py#L261"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.prune.PruningContainer" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.utils.prune.</span></span><span class="sig-name descname"><span class="pre">PruningContainer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/prune.html#PruningContainer"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/utils/prune.py#L261"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.prune.PruningContainer" title="Permalink to this definition">¶</a></dt>
 <dd><p>Container holding a sequence of pruning methods for iterative pruning.</p>
 <p>Keeps track of the order in which pruning methods are applied and handles
 combining successive pruning calls.</p>
@@ -620,7 +620,7 @@ <h1>PruningContainer<a class="headerlink" href="#pruningcontainer" title="Permal
 them.</p>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.utils.prune.PruningContainer.add_pruning_method">
-<span class="sig-name descname"><span class="pre">add_pruning_method</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">method</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/prune.html#PruningContainer.add_pruning_method"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/utils/prune.py#L283"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.prune.PruningContainer.add_pruning_method" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">add_pruning_method</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">method</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/prune.html#PruningContainer.add_pruning_method"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/utils/prune.py#L283"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.prune.PruningContainer.add_pruning_method" title="Permalink to this definition">¶</a></dt>
 <dd><p>Add a child pruning <code class="docutils literal notranslate"><span class="pre">method</span></code> to the container.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -632,7 +632,7 @@ <h1>PruningContainer<a class="headerlink" href="#pruningcontainer" title="Permal
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.utils.prune.PruningContainer.apply">
-<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">apply</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">importance_scores</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/utils/prune.py#L74"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.prune.PruningContainer.apply" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">apply</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">importance_scores</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/utils/prune.py#L74"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.prune.PruningContainer.apply" title="Permalink to this definition">¶</a></dt>
 <dd><p>Add pruning on the fly and reparametrization of a tensor.</p>
 <p>Adds the forward pre-hook that enables pruning on the fly and
 the reparametrization of a tensor in terms of the original tensor
@@ -659,7 +659,7 @@ <h1>PruningContainer<a class="headerlink" href="#pruningcontainer" title="Permal
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.utils.prune.PruningContainer.apply_mask">
-<span class="sig-name descname"><span class="pre">apply_mask</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/utils/prune.py#L52"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.prune.PruningContainer.apply_mask" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">apply_mask</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/utils/prune.py#L52"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.prune.PruningContainer.apply_mask" title="Permalink to this definition">¶</a></dt>
 <dd><p>Simply handles the multiplication between the parameter being pruned and the generated mask.</p>
 <p>Fetches the mask and the original tensor from the module
 and returns the pruned version of the tensor.</p>
@@ -678,7 +678,7 @@ <h1>PruningContainer<a class="headerlink" href="#pruningcontainer" title="Permal
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.utils.prune.PruningContainer.compute_mask">
-<span class="sig-name descname"><span class="pre">compute_mask</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">t</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">default_mask</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/prune.html#PruningContainer.compute_mask"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/utils/prune.py#L311"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.prune.PruningContainer.compute_mask" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">compute_mask</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">t</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">default_mask</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/prune.html#PruningContainer.compute_mask"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/utils/prune.py#L311"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.prune.PruningContainer.compute_mask" title="Permalink to this definition">¶</a></dt>
 <dd><p>Apply the latest <code class="docutils literal notranslate"><span class="pre">method</span></code> by computing the new partial masks and returning its combination with the <code class="docutils literal notranslate"><span class="pre">default_mask</span></code>.</p>
 <p>The new partial mask should be computed on the entries or channels
 that were not zeroed out by the <code class="docutils literal notranslate"><span class="pre">default_mask</span></code>.
@@ -713,7 +713,7 @@ <h1>PruningContainer<a class="headerlink" href="#pruningcontainer" title="Permal
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.utils.prune.PruningContainer.prune">
-<span class="sig-name descname"><span class="pre">prune</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">t</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">default_mask</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">importance_scores</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/utils/prune.py#L203"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.prune.PruningContainer.prune" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">prune</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">t</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">default_mask</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">importance_scores</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/utils/prune.py#L203"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.prune.PruningContainer.prune" title="Permalink to this definition">¶</a></dt>
 <dd><p>Compute and returns a pruned version of input tensor <code class="docutils literal notranslate"><span class="pre">t</span></code>.</p>
 <p>According to the pruning rule specified in <a class="reference internal" href="#torch.nn.utils.prune.PruningContainer.compute_mask" title="torch.nn.utils.prune.PruningContainer.compute_mask"><code class="xref py py-meth docutils literal notranslate"><span class="pre">compute_mask()</span></code></a>.</p>
 <dl class="field-list simple">
@@ -740,7 +740,7 @@ <h1>PruningContainer<a class="headerlink" href="#pruningcontainer" title="Permal
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.utils.prune.PruningContainer.remove">
-<span class="sig-name descname"><span class="pre">remove</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/utils/prune.py#L233"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.prune.PruningContainer.remove" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">remove</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/utils/prune.py#L233"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.prune.PruningContainer.remove" title="Permalink to this definition">¶</a></dt>
 <dd><p>Remove the pruning reparameterization from a module.</p>
 <p>The pruned parameter named <code class="docutils literal notranslate"><span class="pre">name</span></code> remains permanently pruned,
 and the parameter named <code class="docutils literal notranslate"><span class="pre">name+'_orig'</span></code> is removed from the parameter list.
diff --git a/main/generated/torch.nn.utils.prune.RandomStructured.html b/main/generated/torch.nn.utils.prune.RandomStructured.html
index d881542991f..bfc3308871e 100644
--- a/main/generated/torch.nn.utils.prune.RandomStructured.html
+++ b/main/generated/torch.nn.utils.prune.RandomStructured.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>RandomStructured<a class="headerlink" href="#randomstructured" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.utils.prune.RandomStructured">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.utils.prune.</span></span><span class="sig-name descname"><span class="pre">RandomStructured</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">amount</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-1</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/prune.html#RandomStructured"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/utils/prune.py#L557"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.prune.RandomStructured" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.utils.prune.</span></span><span class="sig-name descname"><span class="pre">RandomStructured</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">amount</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-1</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/prune.html#RandomStructured"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/utils/prune.py#L557"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.prune.RandomStructured" title="Permalink to this definition">¶</a></dt>
 <dd><p>Prune entire (currently unpruned) channels in a tensor at random.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -628,7 +628,7 @@ <h1>RandomStructured<a class="headerlink" href="#randomstructured" title="Permal
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.utils.prune.RandomStructured.apply">
-<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">apply</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">amount</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-1</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/prune.html#RandomStructured.apply"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/utils/prune.py#L640"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.prune.RandomStructured.apply" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">apply</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">amount</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-1</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/prune.html#RandomStructured.apply"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/utils/prune.py#L640"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.prune.RandomStructured.apply" title="Permalink to this definition">¶</a></dt>
 <dd><p>Add pruning on the fly and reparametrization of a tensor.</p>
 <p>Adds the forward pre-hook that enables pruning on the fly and
 the reparametrization of a tensor in terms of the original tensor
@@ -652,7 +652,7 @@ <h1>RandomStructured<a class="headerlink" href="#randomstructured" title="Permal
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.utils.prune.RandomStructured.apply_mask">
-<span class="sig-name descname"><span class="pre">apply_mask</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/utils/prune.py#L52"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.prune.RandomStructured.apply_mask" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">apply_mask</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/utils/prune.py#L52"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.prune.RandomStructured.apply_mask" title="Permalink to this definition">¶</a></dt>
 <dd><p>Simply handles the multiplication between the parameter being pruned and the generated mask.</p>
 <p>Fetches the mask and the original tensor from the module
 and returns the pruned version of the tensor.</p>
@@ -671,7 +671,7 @@ <h1>RandomStructured<a class="headerlink" href="#randomstructured" title="Permal
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.utils.prune.RandomStructured.compute_mask">
-<span class="sig-name descname"><span class="pre">compute_mask</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">t</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">default_mask</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/prune.html#RandomStructured.compute_mask"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/utils/prune.py#L577"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.prune.RandomStructured.compute_mask" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">compute_mask</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">t</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">default_mask</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/prune.html#RandomStructured.compute_mask"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/utils/prune.py#L577"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.prune.RandomStructured.compute_mask" title="Permalink to this definition">¶</a></dt>
 <dd><p>Compute and returns a mask for the input tensor <code class="docutils literal notranslate"><span class="pre">t</span></code>.</p>
 <p>Starting from a base <code class="docutils literal notranslate"><span class="pre">default_mask</span></code> (which should be a mask of ones
 if the tensor has not been pruned yet), generate a random mask to
@@ -700,7 +700,7 @@ <h1>RandomStructured<a class="headerlink" href="#randomstructured" title="Permal
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.utils.prune.RandomStructured.prune">
-<span class="sig-name descname"><span class="pre">prune</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">t</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">default_mask</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">importance_scores</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/utils/prune.py#L203"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.prune.RandomStructured.prune" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">prune</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">t</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">default_mask</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">importance_scores</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/utils/prune.py#L203"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.prune.RandomStructured.prune" title="Permalink to this definition">¶</a></dt>
 <dd><p>Compute and returns a pruned version of input tensor <code class="docutils literal notranslate"><span class="pre">t</span></code>.</p>
 <p>According to the pruning rule specified in <a class="reference internal" href="#torch.nn.utils.prune.RandomStructured.compute_mask" title="torch.nn.utils.prune.RandomStructured.compute_mask"><code class="xref py py-meth docutils literal notranslate"><span class="pre">compute_mask()</span></code></a>.</p>
 <dl class="field-list simple">
@@ -727,7 +727,7 @@ <h1>RandomStructured<a class="headerlink" href="#randomstructured" title="Permal
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.utils.prune.RandomStructured.remove">
-<span class="sig-name descname"><span class="pre">remove</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/utils/prune.py#L233"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.prune.RandomStructured.remove" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">remove</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/utils/prune.py#L233"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.prune.RandomStructured.remove" title="Permalink to this definition">¶</a></dt>
 <dd><p>Remove the pruning reparameterization from a module.</p>
 <p>The pruned parameter named <code class="docutils literal notranslate"><span class="pre">name</span></code> remains permanently pruned,
 and the parameter named <code class="docutils literal notranslate"><span class="pre">name+'_orig'</span></code> is removed from the parameter list.
diff --git a/main/generated/torch.nn.utils.prune.RandomUnstructured.html b/main/generated/torch.nn.utils.prune.RandomUnstructured.html
index e250c3942ce..b0c3676833f 100644
--- a/main/generated/torch.nn.utils.prune.RandomUnstructured.html
+++ b/main/generated/torch.nn.utils.prune.RandomUnstructured.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>RandomUnstructured<a class="headerlink" href="#randomunstructured" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.utils.prune.RandomUnstructured">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.utils.prune.</span></span><span class="sig-name descname"><span class="pre">RandomUnstructured</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">amount</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/prune.html#RandomUnstructured"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/utils/prune.py#L432"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.prune.RandomUnstructured" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.utils.prune.</span></span><span class="sig-name descname"><span class="pre">RandomUnstructured</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">amount</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/prune.html#RandomUnstructured"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/utils/prune.py#L432"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.prune.RandomUnstructured" title="Permalink to this definition">¶</a></dt>
 <dd><p>Prune (currently unpruned) units in a tensor at random.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -628,7 +628,7 @@ <h1>RandomUnstructured<a class="headerlink" href="#randomunstructured" title="Pe
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.utils.prune.RandomUnstructured.apply">
-<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">apply</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">amount</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/prune.html#RandomUnstructured.apply"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/utils/prune.py#L471"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.prune.RandomUnstructured.apply" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">apply</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">amount</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/prune.html#RandomUnstructured.apply"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/utils/prune.py#L471"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.prune.RandomUnstructured.apply" title="Permalink to this definition">¶</a></dt>
 <dd><p>Add pruning on the fly and reparametrization of a tensor.</p>
 <p>Adds the forward pre-hook that enables pruning on the fly and
 the reparametrization of a tensor in terms of the original tensor
@@ -650,7 +650,7 @@ <h1>RandomUnstructured<a class="headerlink" href="#randomunstructured" title="Pe
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.utils.prune.RandomUnstructured.apply_mask">
-<span class="sig-name descname"><span class="pre">apply_mask</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/utils/prune.py#L52"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.prune.RandomUnstructured.apply_mask" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">apply_mask</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/utils/prune.py#L52"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.prune.RandomUnstructured.apply_mask" title="Permalink to this definition">¶</a></dt>
 <dd><p>Simply handles the multiplication between the parameter being pruned and the generated mask.</p>
 <p>Fetches the mask and the original tensor from the module
 and returns the pruned version of the tensor.</p>
@@ -669,7 +669,7 @@ <h1>RandomUnstructured<a class="headerlink" href="#randomunstructured" title="Pe
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.utils.prune.RandomUnstructured.prune">
-<span class="sig-name descname"><span class="pre">prune</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">t</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">default_mask</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">importance_scores</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/utils/prune.py#L203"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.prune.RandomUnstructured.prune" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">prune</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">t</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">default_mask</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">importance_scores</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/utils/prune.py#L203"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.prune.RandomUnstructured.prune" title="Permalink to this definition">¶</a></dt>
 <dd><p>Compute and returns a pruned version of input tensor <code class="docutils literal notranslate"><span class="pre">t</span></code>.</p>
 <p>According to the pruning rule specified in <code class="xref py py-meth docutils literal notranslate"><span class="pre">compute_mask()</span></code>.</p>
 <dl class="field-list simple">
@@ -696,7 +696,7 @@ <h1>RandomUnstructured<a class="headerlink" href="#randomunstructured" title="Pe
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.utils.prune.RandomUnstructured.remove">
-<span class="sig-name descname"><span class="pre">remove</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/utils/prune.py#L233"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.prune.RandomUnstructured.remove" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">remove</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/utils/prune.py#L233"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.prune.RandomUnstructured.remove" title="Permalink to this definition">¶</a></dt>
 <dd><p>Remove the pruning reparameterization from a module.</p>
 <p>The pruned parameter named <code class="docutils literal notranslate"><span class="pre">name</span></code> remains permanently pruned,
 and the parameter named <code class="docutils literal notranslate"><span class="pre">name+'_orig'</span></code> is removed from the parameter list.
diff --git a/main/generated/torch.nn.utils.prune.custom_from_mask.html b/main/generated/torch.nn.utils.prune.custom_from_mask.html
index 8fac694c47b..dc134553489 100644
--- a/main/generated/torch.nn.utils.prune.custom_from_mask.html
+++ b/main/generated/torch.nn.utils.prune.custom_from_mask.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.nn.utils.prune.custom_from_mask<a class="headerlink" href="#torch-nn-utils-prune-custom-from-mask" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.utils.prune.custom_from_mask">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.utils.prune.</span></span><span class="sig-name descname"><span class="pre">custom_from_mask</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mask</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/prune.html#custom_from_mask"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/utils/prune.py#L1141"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.prune.custom_from_mask" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.utils.prune.</span></span><span class="sig-name descname"><span class="pre">custom_from_mask</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mask</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/prune.html#custom_from_mask"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/utils/prune.py#L1141"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.prune.custom_from_mask" title="Permalink to this definition">¶</a></dt>
 <dd><p>Prune tensor corresponding to parameter called <code class="docutils literal notranslate"><span class="pre">name</span></code> in <code class="docutils literal notranslate"><span class="pre">module</span></code> by applying the pre-computed mask in <code class="docutils literal notranslate"><span class="pre">mask</span></code>.</p>
 <p>Modifies module in place (and also return the modified module) by:</p>
 <ol class="arabic simple">
diff --git a/main/generated/torch.nn.utils.prune.global_unstructured.html b/main/generated/torch.nn.utils.prune.global_unstructured.html
index e6047a7c5b9..07f4283212a 100644
--- a/main/generated/torch.nn.utils.prune.global_unstructured.html
+++ b/main/generated/torch.nn.utils.prune.global_unstructured.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.nn.utils.prune.global_unstructured<a class="headerlink" href="#torch-nn-utils-prune-global-unstructured" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.utils.prune.global_unstructured">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.utils.prune.</span></span><span class="sig-name descname"><span class="pre">global_unstructured</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">parameters</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pruning_method</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">importance_scores</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/prune.html#global_unstructured"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/utils/prune.py#L1022"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.prune.global_unstructured" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.utils.prune.</span></span><span class="sig-name descname"><span class="pre">global_unstructured</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">parameters</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pruning_method</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">importance_scores</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/prune.html#global_unstructured"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/utils/prune.py#L1022"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.prune.global_unstructured" title="Permalink to this definition">¶</a></dt>
 <dd><p>Globally prunes tensors corresponding to all parameters in <code class="docutils literal notranslate"><span class="pre">parameters</span></code> by applying the specified <code class="docutils literal notranslate"><span class="pre">pruning_method</span></code>.</p>
 <p>Modifies modules in place by:</p>
 <ol class="arabic simple">
diff --git a/main/generated/torch.nn.utils.prune.identity.html b/main/generated/torch.nn.utils.prune.identity.html
index a218574a44e..a0ec9502b63 100644
--- a/main/generated/torch.nn.utils.prune.identity.html
+++ b/main/generated/torch.nn.utils.prune.identity.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.nn.utils.prune.identity<a class="headerlink" href="#torch-nn-utils-prune-identity" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.utils.prune.identity">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.utils.prune.</span></span><span class="sig-name descname"><span class="pre">identity</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">name</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/prune.html#identity"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/utils/prune.py#L818"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.prune.identity" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.utils.prune.</span></span><span class="sig-name descname"><span class="pre">identity</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">name</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/prune.html#identity"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/utils/prune.py#L818"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.prune.identity" title="Permalink to this definition">¶</a></dt>
 <dd><p>Apply pruning reparametrization without pruning any units.</p>
 <p>Applies pruning reparametrization to the tensor corresponding to the
 parameter called <code class="docutils literal notranslate"><span class="pre">name</span></code> in <code class="docutils literal notranslate"><span class="pre">module</span></code> without actually pruning any
diff --git a/main/generated/torch.nn.utils.prune.is_pruned.html b/main/generated/torch.nn.utils.prune.is_pruned.html
index 4d7d2c3f95b..e35976802d5 100644
--- a/main/generated/torch.nn.utils.prune.is_pruned.html
+++ b/main/generated/torch.nn.utils.prune.is_pruned.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
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@@ -612,7 +612,7 @@
 <h1>torch.nn.utils.prune.is_pruned<a class="headerlink" href="#torch-nn-utils-prune-is-pruned" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.utils.prune.is_pruned">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.utils.prune.</span></span><span class="sig-name descname"><span class="pre">is_pruned</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/prune.html#is_pruned"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/utils/prune.py#L1204"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.prune.is_pruned" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.utils.prune.</span></span><span class="sig-name descname"><span class="pre">is_pruned</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/prune.html#is_pruned"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/utils/prune.py#L1204"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.prune.is_pruned" title="Permalink to this definition">¶</a></dt>
 <dd><p>Check if a module is pruned by looking for pruning pre-hooks.</p>
 <p>Check whether <code class="docutils literal notranslate"><span class="pre">module</span></code> is pruned by looking for
 <code class="docutils literal notranslate"><span class="pre">forward_pre_hooks</span></code> in its modules that inherit from the
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index c2910a94a7e..8b173863c17 100644
--- a/main/generated/torch.nn.utils.prune.l1_unstructured.html
+++ b/main/generated/torch.nn.utils.prune.l1_unstructured.html
@@ -300,7 +300,7 @@
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-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
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@@ -612,7 +612,7 @@
 <h1>torch.nn.utils.prune.l1_unstructured<a class="headerlink" href="#torch-nn-utils-prune-l1-unstructured" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.utils.prune.l1_unstructured">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.utils.prune.</span></span><span class="sig-name descname"><span class="pre">l1_unstructured</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">amount</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">importance_scores</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/prune.html#l1_unstructured"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/utils/prune.py#L890"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.prune.l1_unstructured" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.utils.prune.</span></span><span class="sig-name descname"><span class="pre">l1_unstructured</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">amount</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">importance_scores</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/prune.html#l1_unstructured"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/utils/prune.py#L890"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.prune.l1_unstructured" title="Permalink to this definition">¶</a></dt>
 <dd><p>Prune tensor by removing units with the lowest L1-norm.</p>
 <p>Prunes tensor corresponding to parameter called <code class="docutils literal notranslate"><span class="pre">name</span></code> in <code class="docutils literal notranslate"><span class="pre">module</span></code>
 by removing the specified <cite>amount</cite> of (currently unpruned) units with the
diff --git a/main/generated/torch.nn.utils.prune.ln_structured.html b/main/generated/torch.nn.utils.prune.ln_structured.html
index 06e49a05436..702201425e5 100644
--- a/main/generated/torch.nn.utils.prune.ln_structured.html
+++ b/main/generated/torch.nn.utils.prune.ln_structured.html
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     <div id="searchBox">
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@@ -612,7 +612,7 @@
 <h1>torch.nn.utils.prune.ln_structured<a class="headerlink" href="#torch-nn-utils-prune-ln-structured" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.utils.prune.ln_structured">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.utils.prune.</span></span><span class="sig-name descname"><span class="pre">ln_structured</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">amount</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dim</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">importance_scores</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/prune.html#ln_structured"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/utils/prune.py#L975"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.prune.ln_structured" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.utils.prune.</span></span><span class="sig-name descname"><span class="pre">ln_structured</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">amount</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dim</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">importance_scores</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/prune.html#ln_structured"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/utils/prune.py#L975"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.prune.ln_structured" title="Permalink to this definition">¶</a></dt>
 <dd><p>Prune tensor by removing channels with the lowest L<code class="docutils literal notranslate"><span class="pre">n</span></code>-norm along the specified dimension.</p>
 <p>Prunes tensor corresponding to parameter called <code class="docutils literal notranslate"><span class="pre">name</span></code> in <code class="docutils literal notranslate"><span class="pre">module</span></code>
 by removing the specified <code class="docutils literal notranslate"><span class="pre">amount</span></code> of (currently unpruned) channels
diff --git a/main/generated/torch.nn.utils.prune.random_structured.html b/main/generated/torch.nn.utils.prune.random_structured.html
index b15e38fd526..34dbfc431e4 100644
--- a/main/generated/torch.nn.utils.prune.random_structured.html
+++ b/main/generated/torch.nn.utils.prune.random_structured.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.nn.utils.prune.random_structured<a class="headerlink" href="#torch-nn-utils-prune-random-structured" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.utils.prune.random_structured">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.utils.prune.</span></span><span class="sig-name descname"><span class="pre">random_structured</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">amount</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dim</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/prune.html#random_structured"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/utils/prune.py#L934"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.prune.random_structured" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.utils.prune.</span></span><span class="sig-name descname"><span class="pre">random_structured</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">amount</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dim</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/prune.html#random_structured"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/utils/prune.py#L934"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.prune.random_structured" title="Permalink to this definition">¶</a></dt>
 <dd><p>Prune tensor by removing random channels along the specified dimension.</p>
 <p>Prunes tensor corresponding to parameter called <code class="docutils literal notranslate"><span class="pre">name</span></code> in <code class="docutils literal notranslate"><span class="pre">module</span></code>
 by removing the specified <code class="docutils literal notranslate"><span class="pre">amount</span></code> of (currently unpruned) channels
diff --git a/main/generated/torch.nn.utils.prune.random_unstructured.html b/main/generated/torch.nn.utils.prune.random_unstructured.html
index b9f0fb3be5e..5727a39dce9 100644
--- a/main/generated/torch.nn.utils.prune.random_unstructured.html
+++ b/main/generated/torch.nn.utils.prune.random_unstructured.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.nn.utils.prune.random_unstructured<a class="headerlink" href="#torch-nn-utils-prune-random-unstructured" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.utils.prune.random_unstructured">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.utils.prune.</span></span><span class="sig-name descname"><span class="pre">random_unstructured</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">amount</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/prune.html#random_unstructured"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/utils/prune.py#L853"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.prune.random_unstructured" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.utils.prune.</span></span><span class="sig-name descname"><span class="pre">random_unstructured</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">amount</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/prune.html#random_unstructured"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/utils/prune.py#L853"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.prune.random_unstructured" title="Permalink to this definition">¶</a></dt>
 <dd><p>Prune tensor by removing random (currently unpruned) units.</p>
 <p>Prunes tensor corresponding to parameter called <code class="docutils literal notranslate"><span class="pre">name</span></code> in <code class="docutils literal notranslate"><span class="pre">module</span></code>
 by removing the specified <code class="docutils literal notranslate"><span class="pre">amount</span></code> of (currently unpruned) units
diff --git a/main/generated/torch.nn.utils.prune.remove.html b/main/generated/torch.nn.utils.prune.remove.html
index b40fdb4370f..dbf88ac6931 100644
--- a/main/generated/torch.nn.utils.prune.remove.html
+++ b/main/generated/torch.nn.utils.prune.remove.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.nn.utils.prune.remove<a class="headerlink" href="#torch-nn-utils-prune-remove" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.utils.prune.remove">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.utils.prune.</span></span><span class="sig-name descname"><span class="pre">remove</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">name</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/prune.html#remove"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/utils/prune.py#L1174"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.prune.remove" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.utils.prune.</span></span><span class="sig-name descname"><span class="pre">remove</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">name</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/prune.html#remove"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/utils/prune.py#L1174"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.prune.remove" title="Permalink to this definition">¶</a></dt>
 <dd><p>Remove the pruning reparameterization from a module and the pruning method from the forward hook.</p>
 <p>The pruned parameter named <code class="docutils literal notranslate"><span class="pre">name</span></code> remains permanently pruned, and the parameter
 named <code class="docutils literal notranslate"><span class="pre">name+'_orig'</span></code> is removed from the parameter list. Similarly,
diff --git a/main/generated/torch.nn.utils.remove_spectral_norm.html b/main/generated/torch.nn.utils.remove_spectral_norm.html
index 7dbda302d1d..a0f25e6c336 100644
--- a/main/generated/torch.nn.utils.remove_spectral_norm.html
+++ b/main/generated/torch.nn.utils.remove_spectral_norm.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.nn.utils.remove_spectral_norm<a class="headerlink" href="#torch-nn-utils-remove-spectral-norm" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.utils.remove_spectral_norm">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.utils.</span></span><span class="sig-name descname"><span class="pre">remove_spectral_norm</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'weight'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/spectral_norm.html#remove_spectral_norm"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/utils/spectral_norm.py#L337"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.remove_spectral_norm" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.utils.</span></span><span class="sig-name descname"><span class="pre">remove_spectral_norm</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'weight'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/spectral_norm.html#remove_spectral_norm"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/utils/spectral_norm.py#L337"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.remove_spectral_norm" title="Permalink to this definition">¶</a></dt>
 <dd><p>Remove the spectral normalization reparameterization from a module.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.nn.utils.remove_weight_norm.html b/main/generated/torch.nn.utils.remove_weight_norm.html
index 6a23c9d22e1..73e7b570e9e 100644
--- a/main/generated/torch.nn.utils.remove_weight_norm.html
+++ b/main/generated/torch.nn.utils.remove_weight_norm.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.nn.utils.remove_weight_norm<a class="headerlink" href="#torch-nn-utils-remove-weight-norm" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.utils.remove_weight_norm">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.utils.</span></span><span class="sig-name descname"><span class="pre">remove_weight_norm</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'weight'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/weight_norm.html#remove_weight_norm"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/utils/weight_norm.py#L147"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.remove_weight_norm" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.utils.</span></span><span class="sig-name descname"><span class="pre">remove_weight_norm</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'weight'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/weight_norm.html#remove_weight_norm"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/utils/weight_norm.py#L147"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.remove_weight_norm" title="Permalink to this definition">¶</a></dt>
 <dd><p>Remove the weight normalization reparameterization from a module.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.nn.utils.rnn.PackedSequence.html b/main/generated/torch.nn.utils.rnn.PackedSequence.html
index 281f11a5b1b..a6c1d70fed7 100644
--- a/main/generated/torch.nn.utils.rnn.PackedSequence.html
+++ b/main/generated/torch.nn.utils.rnn.PackedSequence.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>PackedSequence<a class="headerlink" href="#packedsequence" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.utils.rnn.PackedSequence">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.utils.rnn.</span></span><span class="sig-name descname"><span class="pre">PackedSequence</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">data</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">batch_sizes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sorted_indices</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">unsorted_indices</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/rnn.html#PackedSequence"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/utils/rnn.py#L38"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.rnn.PackedSequence" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.utils.rnn.</span></span><span class="sig-name descname"><span class="pre">PackedSequence</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">data</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">batch_sizes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sorted_indices</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">unsorted_indices</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/rnn.html#PackedSequence"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/utils/rnn.py#L38"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.rnn.PackedSequence" title="Permalink to this definition">¶</a></dt>
 <dd><p>Holds the data and list of <a class="reference internal" href="#torch.nn.utils.rnn.PackedSequence.batch_sizes" title="torch.nn.utils.rnn.PackedSequence.batch_sizes"><code class="xref py py-attr docutils literal notranslate"><span class="pre">batch_sizes</span></code></a> of a packed sequence.</p>
 <p>All RNN modules accept packed sequences as inputs.</p>
 <div class="admonition note">
@@ -684,7 +684,7 @@ <h1>PackedSequence<a class="headerlink" href="#packedsequence" title="Permalink
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.utils.rnn.PackedSequence.is_pinned">
-<span class="sig-name descname"><span class="pre">is_pinned</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/rnn.html#PackedSequence.is_pinned"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/utils/rnn.py#L209"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.rnn.PackedSequence.is_pinned" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">is_pinned</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/rnn.html#PackedSequence.is_pinned"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/utils/rnn.py#L209"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.rnn.PackedSequence.is_pinned" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return true if <cite>self.data</cite> stored on in pinned memory.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -701,7 +701,7 @@ <h1>PackedSequence<a class="headerlink" href="#packedsequence" title="Permalink
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.utils.rnn.PackedSequence.to">
-<span class="sig-name descname"><span class="pre">to</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="../tensor_attributes.html#torch.dtype" title="torch.dtype"><span class="pre">dtype</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">non_blocking</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">...</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">...</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">Self</span></span></span><a class="reference internal" href="../_modules/torch/nn/utils/rnn.html#PackedSequence.to"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/utils/rnn.py#L130"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.rnn.PackedSequence.to" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">to</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="../tensor_attributes.html#torch.dtype" title="torch.dtype"><span class="pre">dtype</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">non_blocking</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">...</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">...</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">Self</span></span></span><a class="reference internal" href="../_modules/torch/nn/utils/rnn.html#PackedSequence.to"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/utils/rnn.py#L130"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.rnn.PackedSequence.to" title="Permalink to this definition">¶</a></dt>
 <dt class="sig sig-object py">
 <span class="sig-name descname"><span class="pre">to</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Optional" title="(in Python v3.13)"><span class="pre">Optional</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Union" title="(in Python v3.13)"><span class="pre">Union</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="../tensor_attributes.html#torch.device" title="torch.device"><span class="pre">device</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)"><span class="pre">int</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">...</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Optional" title="(in Python v3.13)"><span class="pre">Optional</span></a><span class="p"><span class="pre">[</span></span><a class="reference internal" href="../tensor_attributes.html#torch.dtype" title="torch.dtype"><span class="pre">dtype</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">...</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">non_blocking</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">...</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">copy</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)"><span class="pre">bool</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">...</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">Self</span></span></span></dt>
 <dt class="sig sig-object py">
diff --git a/main/generated/torch.nn.utils.rnn.pack_padded_sequence.html b/main/generated/torch.nn.utils.rnn.pack_padded_sequence.html
index 766c082e345..5f80d472907 100644
--- a/main/generated/torch.nn.utils.rnn.pack_padded_sequence.html
+++ b/main/generated/torch.nn.utils.rnn.pack_padded_sequence.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.nn.utils.rnn.pack_padded_sequence<a class="headerlink" href="#torch-nn-utils-rnn-pack-padded-sequence" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.utils.rnn.pack_padded_sequence">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.utils.rnn.</span></span><span class="sig-name descname"><span class="pre">pack_padded_sequence</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lengths</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">batch_first</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">enforce_sorted</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/rnn.html#pack_padded_sequence"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/utils/rnn.py#L270"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.rnn.pack_padded_sequence" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.utils.rnn.</span></span><span class="sig-name descname"><span class="pre">pack_padded_sequence</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lengths</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">batch_first</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">enforce_sorted</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/rnn.html#pack_padded_sequence"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/utils/rnn.py#L270"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.rnn.pack_padded_sequence" title="Permalink to this definition">¶</a></dt>
 <dd><p>Packs a Tensor containing padded sequences of variable length.</p>
 <p><code class="xref py py-attr docutils literal notranslate"><span class="pre">input</span></code> can be of size <code class="docutils literal notranslate"><span class="pre">T</span> <span class="pre">x</span> <span class="pre">B</span> <span class="pre">x</span> <span class="pre">*</span></code> (if <code class="xref py py-attr docutils literal notranslate"><span class="pre">batch_first</span></code> is <code class="docutils literal notranslate"><span class="pre">False</span></code>)
 or <code class="docutils literal notranslate"><span class="pre">B</span> <span class="pre">x</span> <span class="pre">T</span> <span class="pre">x</span> <span class="pre">*</span></code> (if <code class="xref py py-attr docutils literal notranslate"><span class="pre">batch_first</span></code> is <code class="docutils literal notranslate"><span class="pre">True</span></code>) where <code class="docutils literal notranslate"><span class="pre">T</span></code> is the length
diff --git a/main/generated/torch.nn.utils.rnn.pack_sequence.html b/main/generated/torch.nn.utils.rnn.pack_sequence.html
index 1ce9c6229b1..6198a2158ef 100644
--- a/main/generated/torch.nn.utils.rnn.pack_sequence.html
+++ b/main/generated/torch.nn.utils.rnn.pack_sequence.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.nn.utils.rnn.pack_sequence<a class="headerlink" href="#torch-nn-utils-rnn-pack-sequence" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.utils.rnn.pack_sequence">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.utils.rnn.</span></span><span class="sig-name descname"><span class="pre">pack_sequence</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sequences</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">enforce_sorted</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/rnn.html#pack_sequence"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/utils/rnn.py#L525"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.rnn.pack_sequence" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.utils.rnn.</span></span><span class="sig-name descname"><span class="pre">pack_sequence</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sequences</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">enforce_sorted</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/rnn.html#pack_sequence"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/utils/rnn.py#L525"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.rnn.pack_sequence" title="Permalink to this definition">¶</a></dt>
 <dd><p>Packs a list of variable length Tensors.</p>
 <p>Consecutive call of the next functions: <code class="docutils literal notranslate"><span class="pre">pad_sequence</span></code>, <code class="docutils literal notranslate"><span class="pre">pack_padded_sequence</span></code>.</p>
 <p><code class="docutils literal notranslate"><span class="pre">sequences</span></code> should be a list of Tensors of size <code class="docutils literal notranslate"><span class="pre">L</span> <span class="pre">x</span> <span class="pre">*</span></code>, where <cite>L</cite> is
diff --git a/main/generated/torch.nn.utils.rnn.pad_packed_sequence.html b/main/generated/torch.nn.utils.rnn.pad_packed_sequence.html
index 4c0ccaa9183..00c890edfbc 100644
--- a/main/generated/torch.nn.utils.rnn.pad_packed_sequence.html
+++ b/main/generated/torch.nn.utils.rnn.pad_packed_sequence.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.nn.utils.rnn.pad_packed_sequence<a class="headerlink" href="#torch-nn-utils-rnn-pad-packed-sequence" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.utils.rnn.pad_packed_sequence">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.utils.rnn.</span></span><span class="sig-name descname"><span class="pre">pad_packed_sequence</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sequence</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">batch_first</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding_value</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">total_length</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/rnn.html#pad_packed_sequence"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/utils/rnn.py#L335"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.rnn.pad_packed_sequence" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.utils.rnn.</span></span><span class="sig-name descname"><span class="pre">pad_packed_sequence</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sequence</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">batch_first</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding_value</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">total_length</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/rnn.html#pad_packed_sequence"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/utils/rnn.py#L335"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.rnn.pad_packed_sequence" title="Permalink to this definition">¶</a></dt>
 <dd><p>Pad a packed batch of variable length sequences.</p>
 <p>It is an inverse operation to <a class="reference internal" href="torch.nn.utils.rnn.pack_padded_sequence.html#torch.nn.utils.rnn.pack_padded_sequence" title="torch.nn.utils.rnn.pack_padded_sequence"><code class="xref py py-func docutils literal notranslate"><span class="pre">pack_padded_sequence()</span></code></a>.</p>
 <p>The returned Tensor’s data will be of size <code class="docutils literal notranslate"><span class="pre">T</span> <span class="pre">x</span> <span class="pre">B</span> <span class="pre">x</span> <span class="pre">*</span></code> (if <code class="xref py py-attr docutils literal notranslate"><span class="pre">batch_first</span></code> is <code class="docutils literal notranslate"><span class="pre">False</span></code>)
diff --git a/main/generated/torch.nn.utils.rnn.pad_sequence.html b/main/generated/torch.nn.utils.rnn.pad_sequence.html
index 780e3285bd5..b0dd10cf4f1 100644
--- a/main/generated/torch.nn.utils.rnn.pad_sequence.html
+++ b/main/generated/torch.nn.utils.rnn.pad_sequence.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.nn.utils.rnn.pad_sequence<a class="headerlink" href="#torch-nn-utils-rnn-pad-sequence" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.utils.rnn.pad_sequence">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.utils.rnn.</span></span><span class="sig-name descname"><span class="pre">pad_sequence</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sequences</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">batch_first</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding_value</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding_side</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'right'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/rnn.html#pad_sequence"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/utils/rnn.py#L411"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.rnn.pad_sequence" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.utils.rnn.</span></span><span class="sig-name descname"><span class="pre">pad_sequence</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sequences</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">batch_first</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding_value</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding_side</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'right'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/rnn.html#pad_sequence"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/utils/rnn.py#L411"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.rnn.pad_sequence" title="Permalink to this definition">¶</a></dt>
 <dd><p>Pad a list of variable length Tensors with <code class="xref py py-attr docutils literal notranslate"><span class="pre">padding_value</span></code>.</p>
 <p><code class="docutils literal notranslate"><span class="pre">pad_sequence</span></code> stacks a list of Tensors along a new dimension, and pads them
 to equal length. <code class="xref py py-attr docutils literal notranslate"><span class="pre">sequences</span></code> can be list of sequences with size <code class="docutils literal notranslate"><span class="pre">L</span> <span class="pre">x</span> <span class="pre">*</span></code>,
diff --git a/main/generated/torch.nn.utils.rnn.unpack_sequence.html b/main/generated/torch.nn.utils.rnn.unpack_sequence.html
index 148b9e9853d..da1398a7025 100644
--- a/main/generated/torch.nn.utils.rnn.unpack_sequence.html
+++ b/main/generated/torch.nn.utils.rnn.unpack_sequence.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.nn.utils.rnn.unpack_sequence<a class="headerlink" href="#torch-nn-utils-rnn-unpack-sequence" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.utils.rnn.unpack_sequence">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.utils.rnn.</span></span><span class="sig-name descname"><span class="pre">unpack_sequence</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">packed_sequences</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/rnn.html#unpack_sequence"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/utils/rnn.py#L564"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.rnn.unpack_sequence" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.utils.rnn.</span></span><span class="sig-name descname"><span class="pre">unpack_sequence</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">packed_sequences</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/rnn.html#unpack_sequence"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/utils/rnn.py#L564"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.rnn.unpack_sequence" title="Permalink to this definition">¶</a></dt>
 <dd><p>Unpack PackedSequence into a list of variable length Tensors.</p>
 <p><code class="docutils literal notranslate"><span class="pre">packed_sequences</span></code> should be a PackedSequence object.</p>
 <p class="rubric">Example</p>
diff --git a/main/generated/torch.nn.utils.rnn.unpad_sequence.html b/main/generated/torch.nn.utils.rnn.unpad_sequence.html
index 2186200a80d..61dddd55d6a 100644
--- a/main/generated/torch.nn.utils.rnn.unpad_sequence.html
+++ b/main/generated/torch.nn.utils.rnn.unpad_sequence.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.nn.utils.rnn.unpad_sequence<a class="headerlink" href="#torch-nn-utils-rnn-unpad-sequence" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.utils.rnn.unpad_sequence">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.utils.rnn.</span></span><span class="sig-name descname"><span class="pre">unpad_sequence</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">padded_sequences</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lengths</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">batch_first</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/rnn.html#unpad_sequence"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/utils/rnn.py#L476"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.rnn.unpad_sequence" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.utils.rnn.</span></span><span class="sig-name descname"><span class="pre">unpad_sequence</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">padded_sequences</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lengths</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">batch_first</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/rnn.html#unpad_sequence"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/utils/rnn.py#L476"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.rnn.unpad_sequence" title="Permalink to this definition">¶</a></dt>
 <dd><p>Unpad padded Tensor into a list of variable length Tensors.</p>
 <p><code class="docutils literal notranslate"><span class="pre">unpad_sequence</span></code> unstacks padded Tensor into a list of variable length Tensors.</p>
 <p class="rubric">Example</p>
diff --git a/main/generated/torch.nn.utils.skip_init.html b/main/generated/torch.nn.utils.skip_init.html
index 62ac76d3482..9b9e7f37b70 100644
--- a/main/generated/torch.nn.utils.skip_init.html
+++ b/main/generated/torch.nn.utils.skip_init.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.nn.utils.skip_init<a class="headerlink" href="#torch-nn-utils-skip-init" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.utils.skip_init">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.utils.</span></span><span class="sig-name descname"><span class="pre">skip_init</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module_cls</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/init.html#skip_init"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/utils/init.py#L7"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.skip_init" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.utils.</span></span><span class="sig-name descname"><span class="pre">skip_init</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module_cls</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/init.html#skip_init"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/utils/init.py#L7"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.skip_init" title="Permalink to this definition">¶</a></dt>
 <dd><p>Given a module class object and args / kwargs, instantiate the module without initializing parameters / buffers.</p>
 <p>This can be useful if initialization is slow or if custom initialization will
 be performed, making the default initialization unnecessary. There are some caveats to this, due to
diff --git a/main/generated/torch.nn.utils.spectral_norm.html b/main/generated/torch.nn.utils.spectral_norm.html
index 36706f7d5b3..3ace5a16d54 100644
--- a/main/generated/torch.nn.utils.spectral_norm.html
+++ b/main/generated/torch.nn.utils.spectral_norm.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.nn.utils.spectral_norm<a class="headerlink" href="#torch-nn-utils-spectral-norm" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.utils.spectral_norm">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.utils.</span></span><span class="sig-name descname"><span class="pre">spectral_norm</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'weight'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_power_iterations</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-12</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/spectral_norm.html#spectral_norm"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/utils/spectral_norm.py#L265"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.spectral_norm" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.utils.</span></span><span class="sig-name descname"><span class="pre">spectral_norm</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'weight'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_power_iterations</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-12</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/spectral_norm.html#spectral_norm"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/utils/spectral_norm.py#L265"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.spectral_norm" title="Permalink to this definition">¶</a></dt>
 <dd><p>Apply spectral normalization to a parameter in the given module.</p>
 <div class="math">
 <span class="katex-display"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML" display="block"><semantics><mrow><msub><mi mathvariant="bold">W</mi><mrow><mi>S</mi><mi>N</mi></mrow></msub><mo>=</mo><mfrac><mi mathvariant="bold">W</mi><mrow><mi>σ</mi><mo stretchy="false">(</mo><mi mathvariant="bold">W</mi><mo stretchy="false">)</mo></mrow></mfrac><mo separator="true">,</mo><mi>σ</mi><mo stretchy="false">(</mo><mi mathvariant="bold">W</mi><mo stretchy="false">)</mo><mo>=</mo><munder><mrow><mi>max</mi><mo>⁡</mo></mrow><mrow><mi mathvariant="bold">h</mi><mo>:</mo><mi mathvariant="bold">h</mi><mo mathvariant="normal">≠</mo><mn>0</mn></mrow></munder><mfrac><mrow><mi mathvariant="normal">∥</mi><mi mathvariant="bold">W</mi><mi mathvariant="bold">h</mi><msub><mi mathvariant="normal">∥</mi><mn>2</mn></msub></mrow><mrow><mi mathvariant="normal">∥</mi><mi mathvariant="bold">h</mi><msub><mi mathvariant="normal">∥</mi><mn>2</mn></msub></mrow></mfrac></mrow><annotation encoding="application/x-tex">\mathbf{W}_{SN} = \dfrac{\mathbf{W}}{\sigma(\mathbf{W})},
diff --git a/main/generated/torch.nn.utils.stateless.functional_call.html b/main/generated/torch.nn.utils.stateless.functional_call.html
index c80265999b6..c9072f4c843 100644
--- a/main/generated/torch.nn.utils.stateless.functional_call.html
+++ b/main/generated/torch.nn.utils.stateless.functional_call.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.nn.utils.stateless.functional_call<a class="headerlink" href="#torch-nn-utils-stateless-functional-call" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.utils.stateless.functional_call">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.utils.stateless.</span></span><span class="sig-name descname"><span class="pre">functional_call</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">parameters_and_buffers</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kwargs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tie_weights</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">strict</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/stateless.html#functional_call"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/utils/stateless.py#L162"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.stateless.functional_call" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.utils.stateless.</span></span><span class="sig-name descname"><span class="pre">functional_call</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">parameters_and_buffers</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kwargs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tie_weights</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">strict</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/stateless.html#functional_call"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/utils/stateless.py#L162"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.stateless.functional_call" title="Permalink to this definition">¶</a></dt>
 <dd><p>Perform a functional call on the module by replacing the module parameters and buffers with the provided ones.</p>
 <div class="admonition warning">
 <p class="admonition-title">Warning</p>
diff --git a/main/generated/torch.nn.utils.vector_to_parameters.html b/main/generated/torch.nn.utils.vector_to_parameters.html
index 48d9588521b..256da266a5a 100644
--- a/main/generated/torch.nn.utils.vector_to_parameters.html
+++ b/main/generated/torch.nn.utils.vector_to_parameters.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.nn.utils.vector_to_parameters<a class="headerlink" href="#torch-nn-utils-vector-to-parameters" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.utils.vector_to_parameters">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.utils.</span></span><span class="sig-name descname"><span class="pre">vector_to_parameters</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">vec</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">parameters</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/convert_parameters.html#vector_to_parameters"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/utils/convert_parameters.py#L29"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.vector_to_parameters" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.utils.</span></span><span class="sig-name descname"><span class="pre">vector_to_parameters</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">vec</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">parameters</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/convert_parameters.html#vector_to_parameters"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/utils/convert_parameters.py#L29"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.vector_to_parameters" title="Permalink to this definition">¶</a></dt>
 <dd><p>Copy slices of a vector into an iterable of parameters.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.nn.utils.weight_norm.html b/main/generated/torch.nn.utils.weight_norm.html
index b8b2882e105..2e60e5ee809 100644
--- a/main/generated/torch.nn.utils.weight_norm.html
+++ b/main/generated/torch.nn.utils.weight_norm.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.nn.utils.weight_norm<a class="headerlink" href="#torch-nn-utils-weight-norm" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.utils.weight_norm">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.utils.</span></span><span class="sig-name descname"><span class="pre">weight_norm</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'weight'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/weight_norm.html#weight_norm"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/utils/weight_norm.py#L83"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.weight_norm" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.utils.</span></span><span class="sig-name descname"><span class="pre">weight_norm</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'weight'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/nn/utils/weight_norm.html#weight_norm"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/utils/weight_norm.py#L83"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.utils.weight_norm" title="Permalink to this definition">¶</a></dt>
 <dd><p>Apply weight normalization to a parameter in the given module.</p>
 <div class="math">
 <span class="katex-display"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML" display="block"><semantics><mrow><mi mathvariant="bold">w</mi><mo>=</mo><mi>g</mi><mfrac><mi mathvariant="bold">v</mi><mrow><mi mathvariant="normal">∥</mi><mi mathvariant="bold">v</mi><mi mathvariant="normal">∥</mi></mrow></mfrac></mrow><annotation encoding="application/x-tex">\mathbf{w} = g \dfrac{\mathbf{v}}{\|\mathbf{v}\|}
diff --git a/main/generated/torch.no_grad.html b/main/generated/torch.no_grad.html
index 8111352c46b..67e3af718c2 100644
--- a/main/generated/torch.no_grad.html
+++ b/main/generated/torch.no_grad.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>no_grad<a class="headerlink" href="#no-grad" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.no_grad">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">no_grad</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">orig_func</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd/grad_mode.html#no_grad"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/autograd/grad_mode.py#L21"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.no_grad" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">no_grad</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">orig_func</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/autograd/grad_mode.html#no_grad"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/autograd/grad_mode.py#L21"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.no_grad" title="Permalink to this definition">¶</a></dt>
 <dd><p>Context-manager that disables gradient calculation.</p>
 <p>Disabling gradient calculation is useful for inference, when you are sure
 that you will not call <a class="reference internal" href="torch.Tensor.backward.html#torch.Tensor.backward" title="torch.Tensor.backward"><code class="xref py py-meth docutils literal notranslate"><span class="pre">Tensor.backward()</span></code></a>. It will reduce memory
diff --git a/main/generated/torch.nonzero.html b/main/generated/torch.nonzero.html
index 936a33b6637..f6a494d3c6b 100644
--- a/main/generated/torch.nonzero.html
+++ b/main/generated/torch.nonzero.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.norm.html b/main/generated/torch.norm.html
index f039f0434aa..81d51e9a4d0 100644
--- a/main/generated/torch.norm.html
+++ b/main/generated/torch.norm.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.norm<a class="headerlink" href="#torch-norm" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.norm">
-<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">norm</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">p</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'fro'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">keepdim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/functional.html#norm"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/functional.py#L1677"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.norm" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">norm</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">p</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'fro'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">keepdim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/functional.html#norm"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/functional.py#L1677"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.norm" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns the matrix norm or vector norm of a given tensor.</p>
 <div class="admonition warning">
 <p class="admonition-title">Warning</p>
diff --git a/main/generated/torch.normal.html b/main/generated/torch.normal.html
index e6d83432fcc..23329f18e73 100644
--- a/main/generated/torch.normal.html
+++ b/main/generated/torch.normal.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.not_equal.html b/main/generated/torch.not_equal.html
index 650f72e0060..67e05bd8147 100644
--- a/main/generated/torch.not_equal.html
+++ b/main/generated/torch.not_equal.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.numel.html b/main/generated/torch.numel.html
index 2982f124100..a54bbc69975 100644
--- a/main/generated/torch.numel.html
+++ b/main/generated/torch.numel.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.ones.html b/main/generated/torch.ones.html
index a3eb1cb3139..8d7833fbb46 100644
--- a/main/generated/torch.ones.html
+++ b/main/generated/torch.ones.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.ones_like.html b/main/generated/torch.ones_like.html
index 7c6b7dd1a0d..9223753b4a2 100644
--- a/main/generated/torch.ones_like.html
+++ b/main/generated/torch.ones_like.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.onnx.JitScalarType.html b/main/generated/torch.onnx.JitScalarType.html
index e1c9218c7e1..8f82668c8b8 100644
--- a/main/generated/torch.onnx.JitScalarType.html
+++ b/main/generated/torch.onnx.JitScalarType.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -632,7 +632,7 @@ <h1>JitScalarType<a class="headerlink" href="#jitscalartype" title="Permalink to
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.onnx.JitScalarType.dtype">
-<span class="sig-name descname"><span class="pre">dtype</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/onnx/_type_utils.html#JitScalarType.dtype"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/onnx/_type_utils.py#L253"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.onnx.JitScalarType.dtype" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">dtype</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/onnx/_type_utils.html#JitScalarType.dtype"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/onnx/_type_utils.py#L253"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.onnx.JitScalarType.dtype" title="Permalink to this definition">¶</a></dt>
 <dd><p>Convert a JitScalarType to a torch dtype.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -643,7 +643,7 @@ <h1>JitScalarType<a class="headerlink" href="#jitscalartype" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.onnx.JitScalarType.from_dtype">
-<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_dtype</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dtype</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/onnx/_type_utils.html#JitScalarType.from_dtype"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/onnx/_type_utils.py#L136"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.onnx.JitScalarType.from_dtype" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_dtype</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dtype</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/onnx/_type_utils.html#JitScalarType.from_dtype"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/onnx/_type_utils.py#L136"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.onnx.JitScalarType.from_dtype" title="Permalink to this definition">¶</a></dt>
 <dd><p>Convert a torch dtype to JitScalarType.</p>
 <dl class="simple">
 <dt>Note: DO NOT USE this API when <cite>dtype</cite> comes from a <cite>torch._C.Value.type()</cite> calls.</dt><dd><p>A “RuntimeError: INTERNAL ASSERT FAILED at “../aten/src/ATen/core/jit_type_base.h” can
@@ -669,7 +669,7 @@ <h1>JitScalarType<a class="headerlink" href="#jitscalartype" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.onnx.JitScalarType.from_onnx_type">
-<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_onnx_type</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">onnx_type</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/onnx/_type_utils.html#JitScalarType.from_onnx_type"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/onnx/_type_utils.py#L158"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.onnx.JitScalarType.from_onnx_type" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_onnx_type</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">onnx_type</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/onnx/_type_utils.html#JitScalarType.from_onnx_type"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/onnx/_type_utils.py#L158"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.onnx.JitScalarType.from_onnx_type" title="Permalink to this definition">¶</a></dt>
 <dd><p>Convert a ONNX data type to JitScalarType.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -689,7 +689,7 @@ <h1>JitScalarType<a class="headerlink" href="#jitscalartype" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.onnx.JitScalarType.from_value">
-<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_value</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">default</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/onnx/_type_utils.html#JitScalarType.from_value"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/onnx/_type_utils.py#L177"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.onnx.JitScalarType.from_value" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_value</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">default</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/onnx/_type_utils.html#JitScalarType.from_value"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/onnx/_type_utils.py#L177"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.onnx.JitScalarType.from_value" title="Permalink to this definition">¶</a></dt>
 <dd><p>Create a JitScalarType from an value’s scalar type.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -715,7 +715,7 @@ <h1>JitScalarType<a class="headerlink" href="#jitscalartype" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.onnx.JitScalarType.onnx_compatible">
-<span class="sig-name descname"><span class="pre">onnx_compatible</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/onnx/_type_utils.html#JitScalarType.onnx_compatible"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/onnx/_type_utils.py#L265"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.onnx.JitScalarType.onnx_compatible" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">onnx_compatible</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/onnx/_type_utils.html#JitScalarType.onnx_compatible"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/onnx/_type_utils.py#L265"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.onnx.JitScalarType.onnx_compatible" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return whether this JitScalarType is compatible with ONNX.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -726,7 +726,7 @@ <h1>JitScalarType<a class="headerlink" href="#jitscalartype" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.onnx.JitScalarType.onnx_type">
-<span class="sig-name descname"><span class="pre">onnx_type</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/onnx/_type_utils.html#JitScalarType.onnx_type"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/onnx/_type_utils.py#L257"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.onnx.JitScalarType.onnx_type" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">onnx_type</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/onnx/_type_utils.html#JitScalarType.onnx_type"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/onnx/_type_utils.py#L257"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.onnx.JitScalarType.onnx_type" title="Permalink to this definition">¶</a></dt>
 <dd><p>Convert a JitScalarType to an ONNX data type.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -737,7 +737,7 @@ <h1>JitScalarType<a class="headerlink" href="#jitscalartype" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.onnx.JitScalarType.scalar_name">
-<span class="sig-name descname"><span class="pre">scalar_name</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/onnx/_type_utils.html#JitScalarType.scalar_name"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/onnx/_type_utils.py#L245"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.onnx.JitScalarType.scalar_name" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">scalar_name</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/onnx/_type_utils.html#JitScalarType.scalar_name"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/onnx/_type_utils.py#L245"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.onnx.JitScalarType.scalar_name" title="Permalink to this definition">¶</a></dt>
 <dd><p>Convert a JitScalarType to a JIT scalar type name.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -748,7 +748,7 @@ <h1>JitScalarType<a class="headerlink" href="#jitscalartype" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.onnx.JitScalarType.torch_name">
-<span class="sig-name descname"><span class="pre">torch_name</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/onnx/_type_utils.html#JitScalarType.torch_name"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/onnx/_type_utils.py#L249"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.onnx.JitScalarType.torch_name" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">torch_name</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/onnx/_type_utils.html#JitScalarType.torch_name"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/onnx/_type_utils.py#L249"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.onnx.JitScalarType.torch_name" title="Permalink to this definition">¶</a></dt>
 <dd><p>Convert a JitScalarType to a torch type name.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
diff --git a/main/generated/torch.onnx.verification.GraphInfo.html b/main/generated/torch.onnx.verification.GraphInfo.html
index de7866094f9..c37223c1bab 100644
--- a/main/generated/torch.onnx.verification.GraphInfo.html
+++ b/main/generated/torch.onnx.verification.GraphInfo.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,13 +614,11 @@
 <h1>GraphInfo<a class="headerlink" href="#graphinfo" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.onnx.verification.GraphInfo">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.onnx.verification.</span></span><span class="sig-name descname"><span class="pre">GraphInfo</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">graph</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">params_dict</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">export_options=&lt;factory&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">id=''</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_EXCLUDED_NODE_KINDS=frozenset({'aten::ScalarImplicit'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">'prim::Constant'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">'prim::ListConstruct'})</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/onnx/verification.html#GraphInfo"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/onnx/verification.py#L1151"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.onnx.verification.GraphInfo" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.onnx.verification.</span></span><span class="sig-name descname"><span class="pre">GraphInfo</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">graph</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">params_dict</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">export_options=&lt;factory&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">id=''</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_EXCLUDED_NODE_KINDS=frozenset({'aten::ScalarImplicit'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">'prim::Constant'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">'prim::ListConstruct'})</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/onnx/verification.html#GraphInfo"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/onnx/verification.py#L1162"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.onnx.verification.GraphInfo" title="Permalink to this definition">¶</a></dt>
 <dd><p>GraphInfo contains validation information of a TorchScript graph and its converted ONNX graph.</p>
-<dl class="field-list simple">
-</dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.onnx.verification.GraphInfo.all_mismatch_leaf_graph_info">
-<span class="sig-name descname"><span class="pre">all_mismatch_leaf_graph_info</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/onnx/verification.html#GraphInfo.all_mismatch_leaf_graph_info"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/onnx/verification.py#L1254"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.onnx.verification.GraphInfo.all_mismatch_leaf_graph_info" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">all_mismatch_leaf_graph_info</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/onnx/verification.html#GraphInfo.all_mismatch_leaf_graph_info"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/onnx/verification.py#L1270"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.onnx.verification.GraphInfo.all_mismatch_leaf_graph_info" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return a list of all leaf <cite>GraphInfo</cite> objects that have mismatch.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -631,13 +629,13 @@ <h1>GraphInfo<a class="headerlink" href="#graphinfo" title="Permalink to this he
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.onnx.verification.GraphInfo.clear">
-<span class="sig-name descname"><span class="pre">clear</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/onnx/verification.html#GraphInfo.clear"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/onnx/verification.py#L1172"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.onnx.verification.GraphInfo.clear" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">clear</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/onnx/verification.html#GraphInfo.clear"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/onnx/verification.py#L1188"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.onnx.verification.GraphInfo.clear" title="Permalink to this definition">¶</a></dt>
 <dd><p>Clear states and results of previous verification.</p>
 </dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.onnx.verification.GraphInfo.essential_node_count">
-<span class="sig-name descname"><span class="pre">essential_node_count</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/onnx/verification.html#GraphInfo.essential_node_count"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/onnx/verification.py#L1240"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.onnx.verification.GraphInfo.essential_node_count" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">essential_node_count</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/onnx/verification.html#GraphInfo.essential_node_count"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/onnx/verification.py#L1256"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.onnx.verification.GraphInfo.essential_node_count" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the number of nodes in the subgraph excluding those in <cite>_EXCLUDED_NODE_KINDS</cite>.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -648,7 +646,7 @@ <h1>GraphInfo<a class="headerlink" href="#graphinfo" title="Permalink to this he
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.onnx.verification.GraphInfo.essential_node_kinds">
-<span class="sig-name descname"><span class="pre">essential_node_kinds</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/onnx/verification.html#GraphInfo.essential_node_kinds"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/onnx/verification.py#L1246"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.onnx.verification.GraphInfo.essential_node_kinds" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">essential_node_kinds</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/onnx/verification.html#GraphInfo.essential_node_kinds"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/onnx/verification.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.onnx.verification.GraphInfo.essential_node_kinds" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the set of node kinds in the subgraph excluding those in <cite>_EXCLUDED_NODE_KINDS</cite>.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -659,7 +657,7 @@ <h1>GraphInfo<a class="headerlink" href="#graphinfo" title="Permalink to this he
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.onnx.verification.GraphInfo.export_repro">
-<span class="sig-name descname"><span class="pre">export_repro</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">repro_dir</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/onnx/verification.html#GraphInfo.export_repro"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/onnx/verification.py#L1288"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.onnx.verification.GraphInfo.export_repro" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">export_repro</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">repro_dir</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/onnx/verification.html#GraphInfo.export_repro"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/onnx/verification.py#L1304"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.onnx.verification.GraphInfo.export_repro" title="Permalink to this definition">¶</a></dt>
 <dd><p>Export the subgraph to ONNX along with the input/output data for repro.</p>
 <p>The repro directory will contain the following files:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span>dir
@@ -691,7 +689,7 @@ <h1>GraphInfo<a class="headerlink" href="#graphinfo" title="Permalink to this he
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.onnx.verification.GraphInfo.find_mismatch">
-<span class="sig-name descname"><span class="pre">find_mismatch</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">options</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/onnx/verification.html#GraphInfo.find_mismatch"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/onnx/verification.py#L1576"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.onnx.verification.GraphInfo.find_mismatch" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">find_mismatch</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">options</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/onnx/verification.html#GraphInfo.find_mismatch"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/onnx/verification.py#L1592"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.onnx.verification.GraphInfo.find_mismatch" title="Permalink to this definition">¶</a></dt>
 <dd><p>Find all mismatches between the TorchScript IR graph and the exported onnx model.</p>
 <p>Binary searches the model graph to find the minimal subgraph that exhibits the
 mismatch. A <cite>GraphInfo</cite> object is created for each subgraph, recording the test
@@ -705,7 +703,7 @@ <h1>GraphInfo<a class="headerlink" href="#graphinfo" title="Permalink to this he
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.onnx.verification.GraphInfo.find_partition">
-<span class="sig-name descname"><span class="pre">find_partition</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">id</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/onnx/verification.html#GraphInfo.find_partition"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/onnx/verification.py#L1276"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.onnx.verification.GraphInfo.find_partition" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">find_partition</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">id</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/onnx/verification.html#GraphInfo.find_partition"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/onnx/verification.py#L1292"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.onnx.verification.GraphInfo.find_partition" title="Permalink to this definition">¶</a></dt>
 <dd><p>Find the <cite>GraphInfo</cite> object with the given id.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -716,7 +714,7 @@ <h1>GraphInfo<a class="headerlink" href="#graphinfo" title="Permalink to this he
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.onnx.verification.GraphInfo.has_mismatch">
-<span class="sig-name descname"><span class="pre">has_mismatch</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/onnx/verification.html#GraphInfo.has_mismatch"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/onnx/verification.py#L1236"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.onnx.verification.GraphInfo.has_mismatch" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">has_mismatch</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/onnx/verification.html#GraphInfo.has_mismatch"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/onnx/verification.py#L1252"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.onnx.verification.GraphInfo.has_mismatch" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return True if the subgraph has output mismatch between torch and ONNX.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -727,7 +725,7 @@ <h1>GraphInfo<a class="headerlink" href="#graphinfo" title="Permalink to this he
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.onnx.verification.GraphInfo.pretty_print_mismatch">
-<span class="sig-name descname"><span class="pre">pretty_print_mismatch</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">graph</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/onnx/verification.html#GraphInfo.pretty_print_mismatch"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/onnx/verification.py#L1214"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.onnx.verification.GraphInfo.pretty_print_mismatch" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">pretty_print_mismatch</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">graph</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/onnx/verification.html#GraphInfo.pretty_print_mismatch"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/onnx/verification.py#L1230"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.onnx.verification.GraphInfo.pretty_print_mismatch" title="Permalink to this definition">¶</a></dt>
 <dd><p>Pretty print details of the mismatch between torch and ONNX.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -738,7 +736,7 @@ <h1>GraphInfo<a class="headerlink" href="#graphinfo" title="Permalink to this he
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.onnx.verification.GraphInfo.pretty_print_tree">
-<span class="sig-name descname"><span class="pre">pretty_print_tree</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/onnx/verification.html#GraphInfo.pretty_print_tree"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/onnx/verification.py#L1180"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.onnx.verification.GraphInfo.pretty_print_tree" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">pretty_print_tree</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/onnx/verification.html#GraphInfo.pretty_print_tree"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/onnx/verification.py#L1196"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.onnx.verification.GraphInfo.pretty_print_tree" title="Permalink to this definition">¶</a></dt>
 <dd><p>Pretty print <cite>GraphInfo</cite> tree.</p>
 <p>Each node represents a subgraph, showing the number of nodes in the subgraph and
 a check mark if the subgraph has output mismatch between torch and ONNX.</p>
@@ -770,7 +768,7 @@ <h1>GraphInfo<a class="headerlink" href="#graphinfo" title="Permalink to this he
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.onnx.verification.GraphInfo.verify_export">
-<span class="sig-name descname"><span class="pre">verify_export</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">options</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/onnx/verification.html#GraphInfo.verify_export"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/onnx/verification.py#L1547"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.onnx.verification.GraphInfo.verify_export" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">verify_export</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">options</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/onnx/verification.html#GraphInfo.verify_export"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/onnx/verification.py#L1563"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.onnx.verification.GraphInfo.verify_export" title="Permalink to this definition">¶</a></dt>
 <dd><p>Verify the export from TorchScript IR graph to ONNX.</p>
 <p>Export the TorchScript IR graph to ONNX, with the inputs, parameters and export
 options recorded in this object. Then verify the exported ONNX graph against
diff --git a/main/generated/torch.onnx.verification.VerificationOptions.html b/main/generated/torch.onnx.verification.VerificationOptions.html
index d5818af3141..fa2f6313d71 100644
--- a/main/generated/torch.onnx.verification.VerificationOptions.html
+++ b/main/generated/torch.onnx.verification.VerificationOptions.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -614,7 +614,7 @@
 <h1>VerificationOptions<a class="headerlink" href="#verificationoptions" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.onnx.verification.VerificationOptions">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.onnx.verification.</span></span><span class="sig-name descname"><span class="pre">VerificationOptions</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">flatten</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ignore_none</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">check_shape</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">check_dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">backend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">OnnxBackend.ONNX_RUNTIME_CPU</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">rtol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">atol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-07</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">remained_onnx_input_idx</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">acceptable_error_percentage</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/onnx/verification.html#VerificationOptions"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/onnx/verification.py#L53"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.onnx.verification.VerificationOptions" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.onnx.verification.</span></span><span class="sig-name descname"><span class="pre">VerificationOptions</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">flatten</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ignore_none</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">check_shape</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">check_dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">backend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">OnnxBackend.ONNX_RUNTIME_CPU</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">rtol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">atol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-07</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">remained_onnx_input_idx</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">acceptable_error_percentage</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/onnx/verification.html#VerificationOptions"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/onnx/verification.py#L54"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.onnx.verification.VerificationOptions" title="Permalink to this definition">¶</a></dt>
 <dd><p>Options for ONNX export verification.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Variables</dt>
diff --git a/main/generated/torch.optim.ASGD.html b/main/generated/torch.optim.ASGD.html
index c05162ce9ca..ea8bac586c4 100644
--- a/main/generated/torch.optim.ASGD.html
+++ b/main/generated/torch.optim.ASGD.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>ASGD<a class="headerlink" href="#asgd" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.optim.ASGD">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.optim.</span></span><span class="sig-name descname"><span class="pre">ASGD</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">params</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lr</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.01</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lambd</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.75</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">t0</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1000000.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weight_decay</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">foreach</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">maximize</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">differentiable</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">capturable</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/asgd.html#ASGD"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/asgd.py#L28"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.ASGD" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.optim.</span></span><span class="sig-name descname"><span class="pre">ASGD</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">params</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lr</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.01</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lambd</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.75</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">t0</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1000000.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weight_decay</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">foreach</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">maximize</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">differentiable</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">capturable</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/asgd.html#ASGD"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/asgd.py#L28"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.ASGD" title="Permalink to this definition">¶</a></dt>
 <dd><p>Implements Averaged Stochastic Gradient Descent.</p>
 <p>It has been proposed in <a class="reference external" href="https://dl.acm.org/citation.cfm?id=131098">Acceleration of stochastic approximation by
 averaging</a>.</p>
@@ -650,7 +650,7 @@ <h1>ASGD<a class="headerlink" href="#asgd" title="Permalink to this heading">¶<
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.ASGD.add_param_group">
-<span class="sig-name descname"><span class="pre">add_param_group</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">param_group</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L1003"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.ASGD.add_param_group" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">add_param_group</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">param_group</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/optimizer.py#L1003"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.ASGD.add_param_group" title="Permalink to this definition">¶</a></dt>
 <dd><p>Add a param group to the <a class="reference internal" href="../optim.html#torch.optim.Optimizer" title="torch.optim.Optimizer"><code class="xref py py-class docutils literal notranslate"><span class="pre">Optimizer</span></code></a> s <cite>param_groups</cite>.</p>
 <p>This can be useful when fine tuning a pre-trained network as frozen layers can be made
 trainable and added to the <a class="reference internal" href="../optim.html#torch.optim.Optimizer" title="torch.optim.Optimizer"><code class="xref py py-class docutils literal notranslate"><span class="pre">Optimizer</span></code></a> as training progresses.</p>
@@ -664,7 +664,7 @@ <h1>ASGD<a class="headerlink" href="#asgd" title="Permalink to this heading">¶<
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.ASGD.load_state_dict">
-<span class="sig-name descname"><span class="pre">load_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state_dict</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L832"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.ASGD.load_state_dict" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">load_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state_dict</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/optimizer.py#L832"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.ASGD.load_state_dict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Load the optimizer state.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -688,7 +688,7 @@ <h1>ASGD<a class="headerlink" href="#asgd" title="Permalink to this heading">¶<
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.ASGD.register_load_state_dict_post_hook">
-<span class="sig-name descname"><span class="pre">register_load_state_dict_post_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L798"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.ASGD.register_load_state_dict_post_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_load_state_dict_post_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/optimizer.py#L798"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.ASGD.register_load_state_dict_post_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register a load_state_dict post-hook which will be called after
 <a class="reference internal" href="torch.optim.Optimizer.load_state_dict.html#torch.optim.Optimizer.load_state_dict" title="torch.optim.Optimizer.load_state_dict"><code class="xref py py-meth docutils literal notranslate"><span class="pre">load_state_dict()</span></code></a> is called. It should have the
 following signature:</p>
@@ -722,7 +722,7 @@ <h1>ASGD<a class="headerlink" href="#asgd" title="Permalink to this heading">¶<
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.ASGD.register_load_state_dict_pre_hook">
-<span class="sig-name descname"><span class="pre">register_load_state_dict_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L759"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.ASGD.register_load_state_dict_pre_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_load_state_dict_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/optimizer.py#L759"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.ASGD.register_load_state_dict_pre_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register a load_state_dict pre-hook which will be called before
 <a class="reference internal" href="torch.optim.Optimizer.load_state_dict.html#torch.optim.Optimizer.load_state_dict" title="torch.optim.Optimizer.load_state_dict"><code class="xref py py-meth docutils literal notranslate"><span class="pre">load_state_dict()</span></code></a> is called. It should have the
 following signature:</p>
@@ -759,7 +759,7 @@ <h1>ASGD<a class="headerlink" href="#asgd" title="Permalink to this heading">¶<
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.ASGD.register_state_dict_post_hook">
-<span class="sig-name descname"><span class="pre">register_state_dict_post_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L601"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.ASGD.register_state_dict_post_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_state_dict_post_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/optimizer.py#L601"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.ASGD.register_state_dict_post_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register a state dict post-hook which will be called after <a class="reference internal" href="torch.optim.Optimizer.state_dict.html#torch.optim.Optimizer.state_dict" title="torch.optim.Optimizer.state_dict"><code class="xref py py-meth docutils literal notranslate"><span class="pre">state_dict()</span></code></a> is called.</p>
 <p>It should have the following signature:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">hook</span><span class="p">(</span><span class="n">optimizer</span><span class="p">,</span> <span class="n">state_dict</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">state_dict</span> <span class="ow">or</span> <span class="kc">None</span>
@@ -791,7 +791,7 @@ <h1>ASGD<a class="headerlink" href="#asgd" title="Permalink to this heading">¶<
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.ASGD.register_state_dict_pre_hook">
-<span class="sig-name descname"><span class="pre">register_state_dict_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L569"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.ASGD.register_state_dict_pre_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_state_dict_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/optimizer.py#L569"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.ASGD.register_state_dict_pre_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register a state dict pre-hook which will be called before <a class="reference internal" href="torch.optim.Optimizer.state_dict.html#torch.optim.Optimizer.state_dict" title="torch.optim.Optimizer.state_dict"><code class="xref py py-meth docutils literal notranslate"><span class="pre">state_dict()</span></code></a> is called.</p>
 <p>It should have the following signature:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">hook</span><span class="p">(</span><span class="n">optimizer</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span>
@@ -823,7 +823,7 @@ <h1>ASGD<a class="headerlink" href="#asgd" title="Permalink to this heading">¶<
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.ASGD.register_step_post_hook">
-<span class="sig-name descname"><span class="pre">register_step_post_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L548"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.ASGD.register_step_post_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_step_post_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/optimizer.py#L548"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.ASGD.register_step_post_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register an optimizer step post hook which will be called after optimizer step.</p>
 <p>It should have the following signature:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">hook</span><span class="p">(</span><span class="n">optimizer</span><span class="p">,</span> <span class="n">args</span><span class="p">,</span> <span class="n">kwargs</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span>
@@ -846,7 +846,7 @@ <h1>ASGD<a class="headerlink" href="#asgd" title="Permalink to this heading">¶<
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.ASGD.register_step_pre_hook">
-<span class="sig-name descname"><span class="pre">register_step_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L525"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.ASGD.register_step_pre_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_step_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/optimizer.py#L525"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.ASGD.register_step_pre_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register an optimizer step pre hook which will be called before optimizer step.</p>
 <p>It should have the following signature:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">hook</span><span class="p">(</span><span class="n">optimizer</span><span class="p">,</span> <span class="n">args</span><span class="p">,</span> <span class="n">kwargs</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span> <span class="ow">or</span> <span class="n">modified</span> <span class="n">args</span> <span class="ow">and</span> <span class="n">kwargs</span>
@@ -871,7 +871,7 @@ <h1>ASGD<a class="headerlink" href="#asgd" title="Permalink to this heading">¶<
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.ASGD.state_dict">
-<span class="sig-name descname"><span class="pre">state_dict</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L635"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.ASGD.state_dict" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">state_dict</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/optimizer.py#L635"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.ASGD.state_dict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the state of the optimizer as a <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.13)"><code class="xref py py-class docutils literal notranslate"><span class="pre">dict</span></code></a>.</p>
 <p>It contains two entries:</p>
 <ul class="simple">
@@ -934,7 +934,7 @@ <h1>ASGD<a class="headerlink" href="#asgd" title="Permalink to this heading">¶<
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.ASGD.step">
-<span class="sig-name descname"><span class="pre">step</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">closure</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/asgd.html#ASGD.step"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/asgd.py#L122"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.ASGD.step" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">step</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">closure</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/asgd.html#ASGD.step"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/asgd.py#L122"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.ASGD.step" title="Permalink to this definition">¶</a></dt>
 <dd><p>Perform a single optimization step.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -946,7 +946,7 @@ <h1>ASGD<a class="headerlink" href="#asgd" title="Permalink to this heading">¶<
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.ASGD.zero_grad">
-<span class="sig-name descname"><span class="pre">zero_grad</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">set_to_none</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L931"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.ASGD.zero_grad" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">zero_grad</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">set_to_none</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/optimizer.py#L931"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.ASGD.zero_grad" title="Permalink to this definition">¶</a></dt>
 <dd><p>Reset the gradients of all optimized <a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.Tensor</span></code></a> s.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.optim.Adadelta.html b/main/generated/torch.optim.Adadelta.html
index 1e6f4eff0ea..68022109f5e 100644
--- a/main/generated/torch.optim.Adadelta.html
+++ b/main/generated/torch.optim.Adadelta.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>Adadelta<a class="headerlink" href="#adadelta" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.optim.Adadelta">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.optim.</span></span><span class="sig-name descname"><span class="pre">Adadelta</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">params</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lr</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">rho</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.9</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-06</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weight_decay</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">foreach</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">capturable</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">maximize</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">differentiable</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/adadelta.html#Adadelta"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/adadelta.py#L27"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adadelta" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.optim.</span></span><span class="sig-name descname"><span class="pre">Adadelta</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">params</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lr</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">rho</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.9</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-06</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weight_decay</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">foreach</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">capturable</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">maximize</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">differentiable</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/adadelta.html#Adadelta"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/adadelta.py#L27"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adadelta" title="Permalink to this definition">¶</a></dt>
 <dd><p>Implements Adadelta algorithm.</p>
 <div class="math">
 <span class="katex-display"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML" display="block"><semantics><mtable rowspacing="0.25em" columnalign="right left" columnspacing="0em"><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mpadded height="0em" voffset="0em"><mspace mathbackground="black" width="31.298em" height="0.04em"></mspace></mpadded></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mtext mathvariant="bold">input</mtext><mo>:</mo><mi>γ</mi><mtext> (lr)</mtext><mo separator="true">,</mo><mtext> </mtext><msub><mi>θ</mi><mn>0</mn></msub><mtext> (params)</mtext><mo separator="true">,</mo><mtext> </mtext><mi>f</mi><mo stretchy="false">(</mo><mi>θ</mi><mo stretchy="false">)</mo><mtext> (objective)</mtext><mo separator="true">,</mo><mtext> </mtext><mi>ρ</mi><mtext> (decay)</mtext><mo separator="true">,</mo><mtext> </mtext><mi>λ</mi><mtext> (weight decay)</mtext></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mtext mathvariant="bold">initialize</mtext><mo>:</mo><msub><mi>v</mi><mn>0</mn></msub><mo>←</mo><mn>0</mn><mtext> </mtext><mtext> (square avg)</mtext><mo separator="true">,</mo><mtext> </mtext><msub><mi>u</mi><mn>0</mn></msub><mo>←</mo><mn>0</mn><mtext> </mtext><mtext> (accumulate variables)</mtext></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mpadded height="0em" voffset="0em"><mspace mathbackground="black" width="31.298em" height="0.04em"></mspace></mpadded></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mtext mathvariant="bold">for</mtext><mtext> </mtext><mi>t</mi><mo>=</mo><mn>1</mn><mtext> </mtext><mtext mathvariant="bold">to</mtext><mtext> </mtext><mo>…</mo><mtext> </mtext><mtext mathvariant="bold">do</mtext></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="1.4226em"/><msub><mi>g</mi><mi>t</mi></msub><mo>←</mo><msub><mi mathvariant="normal">∇</mi><mi>θ</mi></msub><msub><mi>f</mi><mi>t</mi></msub><mo stretchy="false">(</mo><msub><mi>θ</mi><mrow><mi>t</mi><mo>−</mo><mn>1</mn></mrow></msub><mo stretchy="false">)</mo></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="1.4226em"/><mi>i</mi><mi>f</mi><mtext> </mtext><mi>λ</mi><mo mathvariant="normal">≠</mo><mn>0</mn></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="2.8453em"/><msub><mi>g</mi><mi>t</mi></msub><mo>←</mo><msub><mi>g</mi><mi>t</mi></msub><mo>+</mo><mi>λ</mi><msub><mi>θ</mi><mrow><mi>t</mi><mo>−</mo><mn>1</mn></mrow></msub></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="1.4226em"/><msub><mi>v</mi><mi>t</mi></msub><mo>←</mo><msub><mi>v</mi><mrow><mi>t</mi><mo>−</mo><mn>1</mn></mrow></msub><mi>ρ</mi><mo>+</mo><msubsup><mi>g</mi><mi>t</mi><mn>2</mn></msubsup><mo stretchy="false">(</mo><mn>1</mn><mo>−</mo><mi>ρ</mi><mo stretchy="false">)</mo></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="1.4226em"/><mi mathvariant="normal">Δ</mi><msub><mi>x</mi><mi>t</mi></msub><mo>←</mo><mfrac><msqrt><mrow><msub><mi>u</mi><mrow><mi>t</mi><mo>−</mo><mn>1</mn></mrow></msub><mo>+</mo><mi>ϵ</mi></mrow></msqrt><msqrt><mrow><msub><mi>v</mi><mi>t</mi></msub><mo>+</mo><mi>ϵ</mi></mrow></msqrt></mfrac><msub><mi>g</mi><mi>t</mi></msub><mspace width="5.9751em"/></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="1.4226em"/><msub><mi>u</mi><mi>t</mi></msub><mo>←</mo><msub><mi>u</mi><mrow><mi>t</mi><mo>−</mo><mn>1</mn></mrow></msub><mi>ρ</mi><mo>+</mo><mi mathvariant="normal">Δ</mi><msubsup><mi>x</mi><mi>t</mi><mn>2</mn></msubsup><mo stretchy="false">(</mo><mn>1</mn><mo>−</mo><mi>ρ</mi><mo stretchy="false">)</mo></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="1.4226em"/><msub><mi>θ</mi><mi>t</mi></msub><mo>←</mo><msub><mi>θ</mi><mrow><mi>t</mi><mo>−</mo><mn>1</mn></mrow></msub><mo>−</mo><mi>γ</mi><mi mathvariant="normal">Δ</mi><msub><mi>x</mi><mi>t</mi></msub></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mpadded height="0em" voffset="0em"><mspace mathbackground="black" width="31.298em" height="0.04em"></mspace></mpadded></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mrow><mrow><mi mathvariant="bold">r</mi><mi mathvariant="bold">e</mi><mi mathvariant="bold">t</mi><mi mathvariant="bold">u</mi><mi mathvariant="bold">r</mi><mi mathvariant="bold">n</mi></mrow><mtext> </mtext><msub><mi mathvariant="bold">θ</mi><mi mathvariant="bold">t</mi></msub></mrow></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mpadded height="0em" voffset="0em"><mspace mathbackground="black" width="31.298em" height="0.04em"></mspace></mpadded></mrow></mstyle></mtd></mtr></mtable><annotation encoding="application/x-tex">\begin{aligned}
@@ -699,7 +699,7 @@ <h1>Adadelta<a class="headerlink" href="#adadelta" title="Permalink to this head
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.Adadelta.add_param_group">
-<span class="sig-name descname"><span class="pre">add_param_group</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">param_group</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L1003"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adadelta.add_param_group" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">add_param_group</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">param_group</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/optimizer.py#L1003"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adadelta.add_param_group" title="Permalink to this definition">¶</a></dt>
 <dd><p>Add a param group to the <a class="reference internal" href="../optim.html#torch.optim.Optimizer" title="torch.optim.Optimizer"><code class="xref py py-class docutils literal notranslate"><span class="pre">Optimizer</span></code></a> s <cite>param_groups</cite>.</p>
 <p>This can be useful when fine tuning a pre-trained network as frozen layers can be made
 trainable and added to the <a class="reference internal" href="../optim.html#torch.optim.Optimizer" title="torch.optim.Optimizer"><code class="xref py py-class docutils literal notranslate"><span class="pre">Optimizer</span></code></a> as training progresses.</p>
@@ -713,7 +713,7 @@ <h1>Adadelta<a class="headerlink" href="#adadelta" title="Permalink to this head
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.Adadelta.load_state_dict">
-<span class="sig-name descname"><span class="pre">load_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state_dict</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L832"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adadelta.load_state_dict" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">load_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state_dict</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/optimizer.py#L832"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adadelta.load_state_dict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Load the optimizer state.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -737,7 +737,7 @@ <h1>Adadelta<a class="headerlink" href="#adadelta" title="Permalink to this head
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.Adadelta.register_load_state_dict_post_hook">
-<span class="sig-name descname"><span class="pre">register_load_state_dict_post_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L798"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adadelta.register_load_state_dict_post_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_load_state_dict_post_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/optimizer.py#L798"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adadelta.register_load_state_dict_post_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register a load_state_dict post-hook which will be called after
 <a class="reference internal" href="torch.optim.Optimizer.load_state_dict.html#torch.optim.Optimizer.load_state_dict" title="torch.optim.Optimizer.load_state_dict"><code class="xref py py-meth docutils literal notranslate"><span class="pre">load_state_dict()</span></code></a> is called. It should have the
 following signature:</p>
@@ -771,7 +771,7 @@ <h1>Adadelta<a class="headerlink" href="#adadelta" title="Permalink to this head
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.Adadelta.register_load_state_dict_pre_hook">
-<span class="sig-name descname"><span class="pre">register_load_state_dict_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L759"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adadelta.register_load_state_dict_pre_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_load_state_dict_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/optimizer.py#L759"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adadelta.register_load_state_dict_pre_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register a load_state_dict pre-hook which will be called before
 <a class="reference internal" href="torch.optim.Optimizer.load_state_dict.html#torch.optim.Optimizer.load_state_dict" title="torch.optim.Optimizer.load_state_dict"><code class="xref py py-meth docutils literal notranslate"><span class="pre">load_state_dict()</span></code></a> is called. It should have the
 following signature:</p>
@@ -808,7 +808,7 @@ <h1>Adadelta<a class="headerlink" href="#adadelta" title="Permalink to this head
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.Adadelta.register_state_dict_post_hook">
-<span class="sig-name descname"><span class="pre">register_state_dict_post_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L601"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adadelta.register_state_dict_post_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_state_dict_post_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/optimizer.py#L601"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adadelta.register_state_dict_post_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register a state dict post-hook which will be called after <a class="reference internal" href="torch.optim.Optimizer.state_dict.html#torch.optim.Optimizer.state_dict" title="torch.optim.Optimizer.state_dict"><code class="xref py py-meth docutils literal notranslate"><span class="pre">state_dict()</span></code></a> is called.</p>
 <p>It should have the following signature:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">hook</span><span class="p">(</span><span class="n">optimizer</span><span class="p">,</span> <span class="n">state_dict</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">state_dict</span> <span class="ow">or</span> <span class="kc">None</span>
@@ -840,7 +840,7 @@ <h1>Adadelta<a class="headerlink" href="#adadelta" title="Permalink to this head
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.Adadelta.register_state_dict_pre_hook">
-<span class="sig-name descname"><span class="pre">register_state_dict_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L569"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adadelta.register_state_dict_pre_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_state_dict_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/optimizer.py#L569"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adadelta.register_state_dict_pre_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register a state dict pre-hook which will be called before <a class="reference internal" href="torch.optim.Optimizer.state_dict.html#torch.optim.Optimizer.state_dict" title="torch.optim.Optimizer.state_dict"><code class="xref py py-meth docutils literal notranslate"><span class="pre">state_dict()</span></code></a> is called.</p>
 <p>It should have the following signature:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">hook</span><span class="p">(</span><span class="n">optimizer</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span>
@@ -872,7 +872,7 @@ <h1>Adadelta<a class="headerlink" href="#adadelta" title="Permalink to this head
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.Adadelta.register_step_post_hook">
-<span class="sig-name descname"><span class="pre">register_step_post_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L548"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adadelta.register_step_post_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_step_post_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/optimizer.py#L548"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adadelta.register_step_post_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register an optimizer step post hook which will be called after optimizer step.</p>
 <p>It should have the following signature:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">hook</span><span class="p">(</span><span class="n">optimizer</span><span class="p">,</span> <span class="n">args</span><span class="p">,</span> <span class="n">kwargs</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span>
@@ -895,7 +895,7 @@ <h1>Adadelta<a class="headerlink" href="#adadelta" title="Permalink to this head
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.Adadelta.register_step_pre_hook">
-<span class="sig-name descname"><span class="pre">register_step_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L525"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adadelta.register_step_pre_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_step_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/optimizer.py#L525"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adadelta.register_step_pre_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register an optimizer step pre hook which will be called before optimizer step.</p>
 <p>It should have the following signature:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">hook</span><span class="p">(</span><span class="n">optimizer</span><span class="p">,</span> <span class="n">args</span><span class="p">,</span> <span class="n">kwargs</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span> <span class="ow">or</span> <span class="n">modified</span> <span class="n">args</span> <span class="ow">and</span> <span class="n">kwargs</span>
@@ -920,7 +920,7 @@ <h1>Adadelta<a class="headerlink" href="#adadelta" title="Permalink to this head
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.Adadelta.state_dict">
-<span class="sig-name descname"><span class="pre">state_dict</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L635"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adadelta.state_dict" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">state_dict</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/optimizer.py#L635"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adadelta.state_dict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the state of the optimizer as a <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.13)"><code class="xref py py-class docutils literal notranslate"><span class="pre">dict</span></code></a>.</p>
 <p>It contains two entries:</p>
 <ul class="simple">
@@ -983,7 +983,7 @@ <h1>Adadelta<a class="headerlink" href="#adadelta" title="Permalink to this head
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.Adadelta.step">
-<span class="sig-name descname"><span class="pre">step</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">closure</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/adadelta.html#Adadelta.step"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/adadelta.py#L126"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adadelta.step" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">step</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">closure</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/adadelta.html#Adadelta.step"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/adadelta.py#L126"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adadelta.step" title="Permalink to this definition">¶</a></dt>
 <dd><p>Perform a single optimization step.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -995,7 +995,7 @@ <h1>Adadelta<a class="headerlink" href="#adadelta" title="Permalink to this head
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.Adadelta.zero_grad">
-<span class="sig-name descname"><span class="pre">zero_grad</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">set_to_none</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L931"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adadelta.zero_grad" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">zero_grad</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">set_to_none</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/optimizer.py#L931"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adadelta.zero_grad" title="Permalink to this definition">¶</a></dt>
 <dd><p>Reset the gradients of all optimized <a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.Tensor</span></code></a> s.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.optim.Adafactor.html b/main/generated/torch.optim.Adafactor.html
index a5dab0df1b7..2ac0ac94e0a 100644
--- a/main/generated/torch.optim.Adafactor.html
+++ b/main/generated/torch.optim.Adafactor.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -839,7 +839,7 @@ <h1>Adafactor<a class="headerlink" href="#adafactor" title="Permalink to this he
 M1001 80h400000v40h-400000z"/></svg></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.1683em;"><span></span></span></span></span></span></span></span><span style="top:-3.3017em;"><span class="pstrut" style="height:3.0717em;"></span><span class="frac-line" style="border-bottom-width:0.04em;"></span></span><span style="top:-3.7487em;"><span class="pstrut" style="height:3.0717em;"></span><span class="mord"><span class="mord"><span class="mord mathnormal">G</span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.2806em;"><span style="top:-2.55em;margin-left:0em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mathnormal mtight">t</span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.15em;"><span></span></span></span></span></span></span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:1.13em;"><span></span></span></span></span></span><span class="mclose nulldelimiter"></span></span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:4.0363em;"><span></span></span></span></span></span></span></span></span></span></span></span></div></div>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.Adafactor.add_param_group">
-<span class="sig-name descname"><span class="pre">add_param_group</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">param_group</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L1003"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adafactor.add_param_group" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">add_param_group</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">param_group</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/optimizer.py#L1003"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adafactor.add_param_group" title="Permalink to this definition">¶</a></dt>
 <dd><p>Add a param group to the <a class="reference internal" href="../optim.html#torch.optim.Optimizer" title="torch.optim.Optimizer"><code class="xref py py-class docutils literal notranslate"><span class="pre">Optimizer</span></code></a> s <cite>param_groups</cite>.</p>
 <p>This can be useful when fine tuning a pre-trained network as frozen layers can be made
 trainable and added to the <a class="reference internal" href="../optim.html#torch.optim.Optimizer" title="torch.optim.Optimizer"><code class="xref py py-class docutils literal notranslate"><span class="pre">Optimizer</span></code></a> as training progresses.</p>
@@ -853,7 +853,7 @@ <h1>Adafactor<a class="headerlink" href="#adafactor" title="Permalink to this he
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.Adafactor.load_state_dict">
-<span class="sig-name descname"><span class="pre">load_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state_dict</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L832"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adafactor.load_state_dict" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">load_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state_dict</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/optimizer.py#L832"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adafactor.load_state_dict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Load the optimizer state.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -877,7 +877,7 @@ <h1>Adafactor<a class="headerlink" href="#adafactor" title="Permalink to this he
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.Adafactor.register_load_state_dict_post_hook">
-<span class="sig-name descname"><span class="pre">register_load_state_dict_post_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L798"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adafactor.register_load_state_dict_post_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_load_state_dict_post_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/optimizer.py#L798"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adafactor.register_load_state_dict_post_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register a load_state_dict post-hook which will be called after
 <a class="reference internal" href="torch.optim.Optimizer.load_state_dict.html#torch.optim.Optimizer.load_state_dict" title="torch.optim.Optimizer.load_state_dict"><code class="xref py py-meth docutils literal notranslate"><span class="pre">load_state_dict()</span></code></a> is called. It should have the
 following signature:</p>
@@ -911,7 +911,7 @@ <h1>Adafactor<a class="headerlink" href="#adafactor" title="Permalink to this he
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.Adafactor.register_load_state_dict_pre_hook">
-<span class="sig-name descname"><span class="pre">register_load_state_dict_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L759"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adafactor.register_load_state_dict_pre_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_load_state_dict_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/optimizer.py#L759"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adafactor.register_load_state_dict_pre_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register a load_state_dict pre-hook which will be called before
 <a class="reference internal" href="torch.optim.Optimizer.load_state_dict.html#torch.optim.Optimizer.load_state_dict" title="torch.optim.Optimizer.load_state_dict"><code class="xref py py-meth docutils literal notranslate"><span class="pre">load_state_dict()</span></code></a> is called. It should have the
 following signature:</p>
@@ -948,7 +948,7 @@ <h1>Adafactor<a class="headerlink" href="#adafactor" title="Permalink to this he
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.Adafactor.register_state_dict_post_hook">
-<span class="sig-name descname"><span class="pre">register_state_dict_post_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L601"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adafactor.register_state_dict_post_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_state_dict_post_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/optimizer.py#L601"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adafactor.register_state_dict_post_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register a state dict post-hook which will be called after <a class="reference internal" href="torch.optim.Optimizer.state_dict.html#torch.optim.Optimizer.state_dict" title="torch.optim.Optimizer.state_dict"><code class="xref py py-meth docutils literal notranslate"><span class="pre">state_dict()</span></code></a> is called.</p>
 <p>It should have the following signature:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">hook</span><span class="p">(</span><span class="n">optimizer</span><span class="p">,</span> <span class="n">state_dict</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">state_dict</span> <span class="ow">or</span> <span class="kc">None</span>
@@ -980,7 +980,7 @@ <h1>Adafactor<a class="headerlink" href="#adafactor" title="Permalink to this he
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.Adafactor.register_state_dict_pre_hook">
-<span class="sig-name descname"><span class="pre">register_state_dict_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L569"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adafactor.register_state_dict_pre_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_state_dict_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/optimizer.py#L569"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adafactor.register_state_dict_pre_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register a state dict pre-hook which will be called before <a class="reference internal" href="torch.optim.Optimizer.state_dict.html#torch.optim.Optimizer.state_dict" title="torch.optim.Optimizer.state_dict"><code class="xref py py-meth docutils literal notranslate"><span class="pre">state_dict()</span></code></a> is called.</p>
 <p>It should have the following signature:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">hook</span><span class="p">(</span><span class="n">optimizer</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span>
@@ -1012,7 +1012,7 @@ <h1>Adafactor<a class="headerlink" href="#adafactor" title="Permalink to this he
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.Adafactor.register_step_post_hook">
-<span class="sig-name descname"><span class="pre">register_step_post_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L548"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adafactor.register_step_post_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_step_post_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/optimizer.py#L548"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adafactor.register_step_post_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register an optimizer step post hook which will be called after optimizer step.</p>
 <p>It should have the following signature:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">hook</span><span class="p">(</span><span class="n">optimizer</span><span class="p">,</span> <span class="n">args</span><span class="p">,</span> <span class="n">kwargs</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span>
@@ -1035,7 +1035,7 @@ <h1>Adafactor<a class="headerlink" href="#adafactor" title="Permalink to this he
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.Adafactor.register_step_pre_hook">
-<span class="sig-name descname"><span class="pre">register_step_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L525"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adafactor.register_step_pre_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_step_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/optimizer.py#L525"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adafactor.register_step_pre_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register an optimizer step pre hook which will be called before optimizer step.</p>
 <p>It should have the following signature:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">hook</span><span class="p">(</span><span class="n">optimizer</span><span class="p">,</span> <span class="n">args</span><span class="p">,</span> <span class="n">kwargs</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span> <span class="ow">or</span> <span class="n">modified</span> <span class="n">args</span> <span class="ow">and</span> <span class="n">kwargs</span>
@@ -1060,7 +1060,7 @@ <h1>Adafactor<a class="headerlink" href="#adafactor" title="Permalink to this he
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.Adafactor.state_dict">
-<span class="sig-name descname"><span class="pre">state_dict</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L635"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adafactor.state_dict" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">state_dict</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/optimizer.py#L635"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adafactor.state_dict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the state of the optimizer as a <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.13)"><code class="xref py py-class docutils literal notranslate"><span class="pre">dict</span></code></a>.</p>
 <p>It contains two entries:</p>
 <ul class="simple">
@@ -1123,7 +1123,7 @@ <h1>Adafactor<a class="headerlink" href="#adafactor" title="Permalink to this he
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.Adafactor.step">
-<span class="sig-name descname"><span class="pre">step</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">closure</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/_adafactor.html#Adafactor.step"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/_adafactor.py#L120"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adafactor.step" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">step</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">closure</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/_adafactor.html#Adafactor.step"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/_adafactor.py#L120"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adafactor.step" title="Permalink to this definition">¶</a></dt>
 <dd><p>Perform a single optimization step.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -1135,7 +1135,7 @@ <h1>Adafactor<a class="headerlink" href="#adafactor" title="Permalink to this he
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.Adafactor.zero_grad">
-<span class="sig-name descname"><span class="pre">zero_grad</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">set_to_none</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L931"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adafactor.zero_grad" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">zero_grad</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">set_to_none</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/optimizer.py#L931"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adafactor.zero_grad" title="Permalink to this definition">¶</a></dt>
 <dd><p>Reset the gradients of all optimized <a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.Tensor</span></code></a> s.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.optim.Adagrad.html b/main/generated/torch.optim.Adagrad.html
index 9bb14a18b8b..c80a892457a 100644
--- a/main/generated/torch.optim.Adagrad.html
+++ b/main/generated/torch.optim.Adagrad.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>Adagrad<a class="headerlink" href="#adagrad" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.optim.Adagrad">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.optim.</span></span><span class="sig-name descname"><span class="pre">Adagrad</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">params</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lr</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.01</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lr_decay</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weight_decay</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">initial_accumulator_value</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">foreach</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">maximize</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">differentiable</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fused</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/adagrad.html#Adagrad"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/adagrad.py#L26"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adagrad" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.optim.</span></span><span class="sig-name descname"><span class="pre">Adagrad</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">params</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lr</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.01</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lr_decay</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weight_decay</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">initial_accumulator_value</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">foreach</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">maximize</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">differentiable</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fused</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/adagrad.html#Adagrad"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/adagrad.py#L26"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adagrad" title="Permalink to this definition">¶</a></dt>
 <dd><p>Implements Adagrad algorithm.</p>
 <div class="math">
 <span class="katex-display"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML" display="block"><semantics><mtable rowspacing="0.25em" columnalign="right left" columnspacing="0em"><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mpadded height="0em" voffset="0em"><mspace mathbackground="black" width="31.298em" height="0.04em"></mspace></mpadded></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mtext mathvariant="bold">input</mtext><mo>:</mo><mi>γ</mi><mtext> (lr)</mtext><mo separator="true">,</mo><mtext> </mtext><msub><mi>θ</mi><mn>0</mn></msub><mtext> (params)</mtext><mo separator="true">,</mo><mtext> </mtext><mi>f</mi><mo stretchy="false">(</mo><mi>θ</mi><mo stretchy="false">)</mo><mtext> (objective)</mtext><mo separator="true">,</mo><mtext> </mtext><mi>λ</mi><mtext> (weight decay)</mtext><mo separator="true">,</mo></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="3.4143em"/><mi>τ</mi><mtext> (initial accumulator value)</mtext><mo separator="true">,</mo><mtext> </mtext><mi>η</mi><mtext> (lr decay)</mtext></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mtext mathvariant="bold">initialize</mtext><mo>:</mo><mi>s</mi><mi>t</mi><mi>a</mi><mi>t</mi><mi>e</mi><mi mathvariant="normal">_</mi><mi>s</mi><mi>u</mi><msub><mi>m</mi><mn>0</mn></msub><mo>←</mo><mi>τ</mi></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mpadded height="0em" voffset="0em"><mspace mathbackground="black" width="31.298em" height="0.04em"></mspace></mpadded></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mtext mathvariant="bold">for</mtext><mtext> </mtext><mi>t</mi><mo>=</mo><mn>1</mn><mtext> </mtext><mtext mathvariant="bold">to</mtext><mtext> </mtext><mo>…</mo><mtext> </mtext><mtext mathvariant="bold">do</mtext></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="1.4226em"/><msub><mi>g</mi><mi>t</mi></msub><mo>←</mo><msub><mi mathvariant="normal">∇</mi><mi>θ</mi></msub><msub><mi>f</mi><mi>t</mi></msub><mo stretchy="false">(</mo><msub><mi>θ</mi><mrow><mi>t</mi><mo>−</mo><mn>1</mn></mrow></msub><mo stretchy="false">)</mo></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="1.4226em"/><mover accent="true"><mi>γ</mi><mo>~</mo></mover><mo>←</mo><mi>γ</mi><mi mathvariant="normal">/</mi><mo stretchy="false">(</mo><mn>1</mn><mo>+</mo><mo stretchy="false">(</mo><mi>t</mi><mo>−</mo><mn>1</mn><mo stretchy="false">)</mo><mi>η</mi><mo stretchy="false">)</mo></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="1.4226em"/><mtext mathvariant="bold">if</mtext><mtext> </mtext><mi>λ</mi><mo mathvariant="normal">≠</mo><mn>0</mn></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="2.8453em"/><msub><mi>g</mi><mi>t</mi></msub><mo>←</mo><msub><mi>g</mi><mi>t</mi></msub><mo>+</mo><mi>λ</mi><msub><mi>θ</mi><mrow><mi>t</mi><mo>−</mo><mn>1</mn></mrow></msub></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="1.4226em"/><mi>s</mi><mi>t</mi><mi>a</mi><mi>t</mi><mi>e</mi><mi mathvariant="normal">_</mi><mi>s</mi><mi>u</mi><msub><mi>m</mi><mi>t</mi></msub><mo>←</mo><mi>s</mi><mi>t</mi><mi>a</mi><mi>t</mi><mi>e</mi><mi mathvariant="normal">_</mi><mi>s</mi><mi>u</mi><msub><mi>m</mi><mrow><mi>t</mi><mo>−</mo><mn>1</mn></mrow></msub><mo>+</mo><msubsup><mi>g</mi><mi>t</mi><mn>2</mn></msubsup></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="1.4226em"/><msub><mi>θ</mi><mi>t</mi></msub><mo>←</mo><msub><mi>θ</mi><mrow><mi>t</mi><mo>−</mo><mn>1</mn></mrow></msub><mo>−</mo><mover accent="true"><mi>γ</mi><mo>~</mo></mover><mfrac><msub><mi>g</mi><mi>t</mi></msub><mrow><msqrt><mrow><mi>s</mi><mi>t</mi><mi>a</mi><mi>t</mi><mi>e</mi><mi mathvariant="normal">_</mi><mi>s</mi><mi>u</mi><msub><mi>m</mi><mi>t</mi></msub></mrow></msqrt><mo>+</mo><mi>ϵ</mi></mrow></mfrac></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mpadded height="0em" voffset="0em"><mspace mathbackground="black" width="31.298em" height="0.04em"></mspace></mpadded></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mrow><mrow><mi mathvariant="bold">r</mi><mi mathvariant="bold">e</mi><mi mathvariant="bold">t</mi><mi mathvariant="bold">u</mi><mi mathvariant="bold">r</mi><mi mathvariant="bold">n</mi></mrow><mtext> </mtext><msub><mi mathvariant="bold">θ</mi><mi mathvariant="bold">t</mi></msub></mrow></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mpadded height="0em" voffset="0em"><mspace mathbackground="black" width="31.298em" height="0.04em"></mspace></mpadded></mrow></mstyle></mtd></mtr></mtable><annotation encoding="application/x-tex">\begin{aligned}
@@ -683,7 +683,7 @@ <h1>Adagrad<a class="headerlink" href="#adagrad" title="Permalink to this headin
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.Adagrad.add_param_group">
-<span class="sig-name descname"><span class="pre">add_param_group</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">param_group</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L1003"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adagrad.add_param_group" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">add_param_group</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">param_group</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/optimizer.py#L1003"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adagrad.add_param_group" title="Permalink to this definition">¶</a></dt>
 <dd><p>Add a param group to the <a class="reference internal" href="../optim.html#torch.optim.Optimizer" title="torch.optim.Optimizer"><code class="xref py py-class docutils literal notranslate"><span class="pre">Optimizer</span></code></a> s <cite>param_groups</cite>.</p>
 <p>This can be useful when fine tuning a pre-trained network as frozen layers can be made
 trainable and added to the <a class="reference internal" href="../optim.html#torch.optim.Optimizer" title="torch.optim.Optimizer"><code class="xref py py-class docutils literal notranslate"><span class="pre">Optimizer</span></code></a> as training progresses.</p>
@@ -697,7 +697,7 @@ <h1>Adagrad<a class="headerlink" href="#adagrad" title="Permalink to this headin
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.Adagrad.load_state_dict">
-<span class="sig-name descname"><span class="pre">load_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state_dict</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L832"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adagrad.load_state_dict" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">load_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state_dict</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/optimizer.py#L832"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adagrad.load_state_dict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Load the optimizer state.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -721,7 +721,7 @@ <h1>Adagrad<a class="headerlink" href="#adagrad" title="Permalink to this headin
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.Adagrad.register_load_state_dict_post_hook">
-<span class="sig-name descname"><span class="pre">register_load_state_dict_post_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L798"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adagrad.register_load_state_dict_post_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_load_state_dict_post_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/optimizer.py#L798"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adagrad.register_load_state_dict_post_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register a load_state_dict post-hook which will be called after
 <a class="reference internal" href="torch.optim.Optimizer.load_state_dict.html#torch.optim.Optimizer.load_state_dict" title="torch.optim.Optimizer.load_state_dict"><code class="xref py py-meth docutils literal notranslate"><span class="pre">load_state_dict()</span></code></a> is called. It should have the
 following signature:</p>
@@ -755,7 +755,7 @@ <h1>Adagrad<a class="headerlink" href="#adagrad" title="Permalink to this headin
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.Adagrad.register_load_state_dict_pre_hook">
-<span class="sig-name descname"><span class="pre">register_load_state_dict_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L759"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adagrad.register_load_state_dict_pre_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_load_state_dict_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/optimizer.py#L759"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adagrad.register_load_state_dict_pre_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register a load_state_dict pre-hook which will be called before
 <a class="reference internal" href="torch.optim.Optimizer.load_state_dict.html#torch.optim.Optimizer.load_state_dict" title="torch.optim.Optimizer.load_state_dict"><code class="xref py py-meth docutils literal notranslate"><span class="pre">load_state_dict()</span></code></a> is called. It should have the
 following signature:</p>
@@ -792,7 +792,7 @@ <h1>Adagrad<a class="headerlink" href="#adagrad" title="Permalink to this headin
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.Adagrad.register_state_dict_post_hook">
-<span class="sig-name descname"><span class="pre">register_state_dict_post_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L601"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adagrad.register_state_dict_post_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_state_dict_post_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/optimizer.py#L601"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adagrad.register_state_dict_post_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register a state dict post-hook which will be called after <a class="reference internal" href="torch.optim.Optimizer.state_dict.html#torch.optim.Optimizer.state_dict" title="torch.optim.Optimizer.state_dict"><code class="xref py py-meth docutils literal notranslate"><span class="pre">state_dict()</span></code></a> is called.</p>
 <p>It should have the following signature:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">hook</span><span class="p">(</span><span class="n">optimizer</span><span class="p">,</span> <span class="n">state_dict</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">state_dict</span> <span class="ow">or</span> <span class="kc">None</span>
@@ -824,7 +824,7 @@ <h1>Adagrad<a class="headerlink" href="#adagrad" title="Permalink to this headin
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.Adagrad.register_state_dict_pre_hook">
-<span class="sig-name descname"><span class="pre">register_state_dict_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L569"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adagrad.register_state_dict_pre_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_state_dict_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/optimizer.py#L569"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adagrad.register_state_dict_pre_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register a state dict pre-hook which will be called before <a class="reference internal" href="torch.optim.Optimizer.state_dict.html#torch.optim.Optimizer.state_dict" title="torch.optim.Optimizer.state_dict"><code class="xref py py-meth docutils literal notranslate"><span class="pre">state_dict()</span></code></a> is called.</p>
 <p>It should have the following signature:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">hook</span><span class="p">(</span><span class="n">optimizer</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span>
@@ -856,7 +856,7 @@ <h1>Adagrad<a class="headerlink" href="#adagrad" title="Permalink to this headin
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.Adagrad.register_step_post_hook">
-<span class="sig-name descname"><span class="pre">register_step_post_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L548"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adagrad.register_step_post_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_step_post_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/optimizer.py#L548"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adagrad.register_step_post_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register an optimizer step post hook which will be called after optimizer step.</p>
 <p>It should have the following signature:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">hook</span><span class="p">(</span><span class="n">optimizer</span><span class="p">,</span> <span class="n">args</span><span class="p">,</span> <span class="n">kwargs</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span>
@@ -879,7 +879,7 @@ <h1>Adagrad<a class="headerlink" href="#adagrad" title="Permalink to this headin
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.Adagrad.register_step_pre_hook">
-<span class="sig-name descname"><span class="pre">register_step_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L525"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adagrad.register_step_pre_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_step_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/optimizer.py#L525"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adagrad.register_step_pre_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register an optimizer step pre hook which will be called before optimizer step.</p>
 <p>It should have the following signature:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">hook</span><span class="p">(</span><span class="n">optimizer</span><span class="p">,</span> <span class="n">args</span><span class="p">,</span> <span class="n">kwargs</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span> <span class="ow">or</span> <span class="n">modified</span> <span class="n">args</span> <span class="ow">and</span> <span class="n">kwargs</span>
@@ -904,7 +904,7 @@ <h1>Adagrad<a class="headerlink" href="#adagrad" title="Permalink to this headin
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.Adagrad.state_dict">
-<span class="sig-name descname"><span class="pre">state_dict</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L635"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adagrad.state_dict" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">state_dict</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/optimizer.py#L635"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adagrad.state_dict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the state of the optimizer as a <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.13)"><code class="xref py py-class docutils literal notranslate"><span class="pre">dict</span></code></a>.</p>
 <p>It contains two entries:</p>
 <ul class="simple">
@@ -967,7 +967,7 @@ <h1>Adagrad<a class="headerlink" href="#adagrad" title="Permalink to this headin
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.Adagrad.step">
-<span class="sig-name descname"><span class="pre">step</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">closure</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/adagrad.html#Adagrad.step"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/adagrad.py#L145"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adagrad.step" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">step</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">closure</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/adagrad.html#Adagrad.step"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/adagrad.py#L145"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adagrad.step" title="Permalink to this definition">¶</a></dt>
 <dd><p>Perform a single optimization step.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -979,7 +979,7 @@ <h1>Adagrad<a class="headerlink" href="#adagrad" title="Permalink to this headin
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.Adagrad.zero_grad">
-<span class="sig-name descname"><span class="pre">zero_grad</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">set_to_none</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L931"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adagrad.zero_grad" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">zero_grad</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">set_to_none</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/optimizer.py#L931"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adagrad.zero_grad" title="Permalink to this definition">¶</a></dt>
 <dd><p>Reset the gradients of all optimized <a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.Tensor</span></code></a> s.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.optim.Adam.html b/main/generated/torch.optim.Adam.html
index 4506dc47e1d..7c897b33594 100644
--- a/main/generated/torch.optim.Adam.html
+++ b/main/generated/torch.optim.Adam.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>Adam<a class="headerlink" href="#adam" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.optim.Adam">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.optim.</span></span><span class="sig-name descname"><span class="pre">Adam</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">params</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lr</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">betas</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">(0.9,</span> <span class="pre">0.999)</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-08</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weight_decay</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">amsgrad</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">foreach</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">maximize</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">capturable</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">differentiable</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fused</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">decoupled_weight_decay</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/adam.html#Adam"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/adam.py#L33"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adam" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.optim.</span></span><span class="sig-name descname"><span class="pre">Adam</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">params</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lr</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">betas</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">(0.9,</span> <span class="pre">0.999)</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-08</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weight_decay</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">amsgrad</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">foreach</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">maximize</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">capturable</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">differentiable</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fused</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">decoupled_weight_decay</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/adam.html#Adam"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/adam.py#L33"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adam" title="Permalink to this definition">¶</a></dt>
 <dd><p>Implements Adam algorithm.</p>
 <div class="math">
 <span class="katex-display"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML" display="block"><semantics><mtable rowspacing="0.25em" columnalign="right left" columnspacing="0em"><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mpadded height="0em" voffset="0em"><mspace mathbackground="black" width="31.298em" height="0.04em"></mspace></mpadded></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mtext mathvariant="bold">input</mtext><mo>:</mo><mi>γ</mi><mtext> (lr)</mtext><mo separator="true">,</mo><msub><mi>β</mi><mn>1</mn></msub><mo separator="true">,</mo><msub><mi>β</mi><mn>2</mn></msub><mtext> (betas)</mtext><mo separator="true">,</mo><msub><mi>θ</mi><mn>0</mn></msub><mtext> (params)</mtext><mo separator="true">,</mo><mi>f</mi><mo stretchy="false">(</mo><mi>θ</mi><mo stretchy="false">)</mo><mtext> (objective)</mtext></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="3.6989em"/><mi>λ</mi><mtext> (weight decay)</mtext><mo separator="true">,</mo><mtext> </mtext><mtext mathvariant="italic">amsgrad</mtext><mo separator="true">,</mo><mtext> </mtext><mtext mathvariant="italic">maximize</mtext><mo separator="true">,</mo><mtext> </mtext><mi>ϵ</mi><mtext> (epsilon)</mtext></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mtext mathvariant="bold">initialize</mtext><mo>:</mo><msub><mi>m</mi><mn>0</mn></msub><mo>←</mo><mn>0</mn><mtext> ( first moment)</mtext><mo separator="true">,</mo><msub><mi>v</mi><mn>0</mn></msub><mo>←</mo><mn>0</mn><mtext> (second moment)</mtext><mo separator="true">,</mo><mtext> </mtext><msubsup><mi>v</mi><mn>0</mn><mrow><mi>m</mi><mi>a</mi><mi>x</mi></mrow></msubsup><mo>←</mo><mn>0</mn></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mpadded height="0em" voffset="0em"><mspace mathbackground="black" width="31.298em" height="0.04em"></mspace></mpadded></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mtext mathvariant="bold">for</mtext><mtext> </mtext><mi>t</mi><mo>=</mo><mn>1</mn><mtext> </mtext><mtext mathvariant="bold">to</mtext><mtext> </mtext><mo>…</mo><mtext> </mtext><mtext mathvariant="bold">do</mtext></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="1.4226em"/><mtext mathvariant="bold">if</mtext><mtext> </mtext><mtext mathvariant="italic">maximize</mtext><mo>:</mo></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="2.8453em"/><msub><mi>g</mi><mi>t</mi></msub><mo>←</mo><mo>−</mo><msub><mi mathvariant="normal">∇</mi><mi>θ</mi></msub><msub><mi>f</mi><mi>t</mi></msub><mo stretchy="false">(</mo><msub><mi>θ</mi><mrow><mi>t</mi><mo>−</mo><mn>1</mn></mrow></msub><mo stretchy="false">)</mo></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="1.4226em"/><mtext mathvariant="bold">else</mtext></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="2.8453em"/><msub><mi>g</mi><mi>t</mi></msub><mo>←</mo><msub><mi mathvariant="normal">∇</mi><mi>θ</mi></msub><msub><mi>f</mi><mi>t</mi></msub><mo stretchy="false">(</mo><msub><mi>θ</mi><mrow><mi>t</mi><mo>−</mo><mn>1</mn></mrow></msub><mo stretchy="false">)</mo></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="1.4226em"/><mtext mathvariant="bold">if</mtext><mtext> </mtext><mi>λ</mi><mo mathvariant="normal">≠</mo><mn>0</mn></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="2.8453em"/><msub><mi>g</mi><mi>t</mi></msub><mo>←</mo><msub><mi>g</mi><mi>t</mi></msub><mo>+</mo><mi>λ</mi><msub><mi>θ</mi><mrow><mi>t</mi><mo>−</mo><mn>1</mn></mrow></msub></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="1.4226em"/><msub><mi>m</mi><mi>t</mi></msub><mo>←</mo><msub><mi>β</mi><mn>1</mn></msub><msub><mi>m</mi><mrow><mi>t</mi><mo>−</mo><mn>1</mn></mrow></msub><mo>+</mo><mo stretchy="false">(</mo><mn>1</mn><mo>−</mo><msub><mi>β</mi><mn>1</mn></msub><mo stretchy="false">)</mo><msub><mi>g</mi><mi>t</mi></msub></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="1.4226em"/><msub><mi>v</mi><mi>t</mi></msub><mo>←</mo><msub><mi>β</mi><mn>2</mn></msub><msub><mi>v</mi><mrow><mi>t</mi><mo>−</mo><mn>1</mn></mrow></msub><mo>+</mo><mo stretchy="false">(</mo><mn>1</mn><mo>−</mo><msub><mi>β</mi><mn>2</mn></msub><mo stretchy="false">)</mo><msubsup><mi>g</mi><mi>t</mi><mn>2</mn></msubsup></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="1.4226em"/><mover accent="true"><msub><mi>m</mi><mi>t</mi></msub><mo stretchy="true">^</mo></mover><mo>←</mo><msub><mi>m</mi><mi>t</mi></msub><mi mathvariant="normal">/</mi><mo fence="false" stretchy="true" minsize="1.2em" maxsize="1.2em">(</mo><mn>1</mn><mo>−</mo><msubsup><mi>β</mi><mn>1</mn><mi>t</mi></msubsup><mo fence="false" stretchy="true" minsize="1.2em" maxsize="1.2em">)</mo></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="1.4226em"/><mtext mathvariant="bold">if</mtext><mtext> </mtext><mi>a</mi><mi>m</mi><mi>s</mi><mi>g</mi><mi>r</mi><mi>a</mi><mi>d</mi></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="2.8453em"/><msubsup><mi>v</mi><mi>t</mi><mrow><mi>m</mi><mi>a</mi><mi>x</mi></mrow></msubsup><mo>←</mo><mrow><mi mathvariant="normal">m</mi><mi mathvariant="normal">a</mi><mi mathvariant="normal">x</mi></mrow><mo stretchy="false">(</mo><msubsup><mi>v</mi><mrow><mi>t</mi><mo>−</mo><mn>1</mn></mrow><mrow><mi>m</mi><mi>a</mi><mi>x</mi></mrow></msubsup><mo separator="true">,</mo><msub><mi>v</mi><mi>t</mi></msub><mo stretchy="false">)</mo></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="2.8453em"/><mover accent="true"><msub><mi>v</mi><mi>t</mi></msub><mo stretchy="true">^</mo></mover><mo>←</mo><msubsup><mi>v</mi><mi>t</mi><mrow><mi>m</mi><mi>a</mi><mi>x</mi></mrow></msubsup><mi mathvariant="normal">/</mi><mo fence="false" stretchy="true" minsize="1.2em" maxsize="1.2em">(</mo><mn>1</mn><mo>−</mo><msubsup><mi>β</mi><mn>2</mn><mi>t</mi></msubsup><mo fence="false" stretchy="true" minsize="1.2em" maxsize="1.2em">)</mo></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="1.4226em"/><mtext mathvariant="bold">else</mtext></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="2.8453em"/><mover accent="true"><msub><mi>v</mi><mi>t</mi></msub><mo stretchy="true">^</mo></mover><mo>←</mo><msub><mi>v</mi><mi>t</mi></msub><mi mathvariant="normal">/</mi><mo fence="false" stretchy="true" minsize="1.2em" maxsize="1.2em">(</mo><mn>1</mn><mo>−</mo><msubsup><mi>β</mi><mn>2</mn><mi>t</mi></msubsup><mo fence="false" stretchy="true" minsize="1.2em" maxsize="1.2em">)</mo></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="1.4226em"/><msub><mi>θ</mi><mi>t</mi></msub><mo>←</mo><msub><mi>θ</mi><mrow><mi>t</mi><mo>−</mo><mn>1</mn></mrow></msub><mo>−</mo><mi>γ</mi><mover accent="true"><msub><mi>m</mi><mi>t</mi></msub><mo stretchy="true">^</mo></mover><mi mathvariant="normal">/</mi><mo fence="false" stretchy="true" minsize="1.2em" maxsize="1.2em">(</mo><msqrt><mover accent="true"><msub><mi>v</mi><mi>t</mi></msub><mo stretchy="true">^</mo></mover></msqrt><mo>+</mo><mi>ϵ</mi><mo fence="false" stretchy="true" minsize="1.2em" maxsize="1.2em">)</mo></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mpadded height="0em" voffset="0em"><mspace mathbackground="black" width="31.298em" height="0.04em"></mspace></mpadded></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mrow><mrow><mi mathvariant="bold">r</mi><mi mathvariant="bold">e</mi><mi mathvariant="bold">t</mi><mi mathvariant="bold">u</mi><mi mathvariant="bold">r</mi><mi mathvariant="bold">n</mi></mrow><mtext> </mtext><msub><mi mathvariant="bold">θ</mi><mi mathvariant="bold">t</mi></msub></mrow></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mpadded height="0em" voffset="0em"><mspace mathbackground="black" width="31.298em" height="0.04em"></mspace></mpadded></mrow></mstyle></mtd></mtr></mtable><annotation encoding="application/x-tex">\begin{aligned}
@@ -722,7 +722,7 @@ <h1>Adam<a class="headerlink" href="#adam" title="Permalink to this heading">¶<
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.Adam.add_param_group">
-<span class="sig-name descname"><span class="pre">add_param_group</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">param_group</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L1003"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adam.add_param_group" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">add_param_group</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">param_group</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/optimizer.py#L1003"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adam.add_param_group" title="Permalink to this definition">¶</a></dt>
 <dd><p>Add a param group to the <a class="reference internal" href="../optim.html#torch.optim.Optimizer" title="torch.optim.Optimizer"><code class="xref py py-class docutils literal notranslate"><span class="pre">Optimizer</span></code></a> s <cite>param_groups</cite>.</p>
 <p>This can be useful when fine tuning a pre-trained network as frozen layers can be made
 trainable and added to the <a class="reference internal" href="../optim.html#torch.optim.Optimizer" title="torch.optim.Optimizer"><code class="xref py py-class docutils literal notranslate"><span class="pre">Optimizer</span></code></a> as training progresses.</p>
@@ -736,7 +736,7 @@ <h1>Adam<a class="headerlink" href="#adam" title="Permalink to this heading">¶<
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.Adam.load_state_dict">
-<span class="sig-name descname"><span class="pre">load_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state_dict</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L832"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adam.load_state_dict" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">load_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state_dict</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/optimizer.py#L832"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adam.load_state_dict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Load the optimizer state.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -760,7 +760,7 @@ <h1>Adam<a class="headerlink" href="#adam" title="Permalink to this heading">¶<
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.Adam.register_load_state_dict_post_hook">
-<span class="sig-name descname"><span class="pre">register_load_state_dict_post_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L798"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adam.register_load_state_dict_post_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_load_state_dict_post_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/optimizer.py#L798"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adam.register_load_state_dict_post_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register a load_state_dict post-hook which will be called after
 <a class="reference internal" href="torch.optim.Optimizer.load_state_dict.html#torch.optim.Optimizer.load_state_dict" title="torch.optim.Optimizer.load_state_dict"><code class="xref py py-meth docutils literal notranslate"><span class="pre">load_state_dict()</span></code></a> is called. It should have the
 following signature:</p>
@@ -794,7 +794,7 @@ <h1>Adam<a class="headerlink" href="#adam" title="Permalink to this heading">¶<
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.Adam.register_load_state_dict_pre_hook">
-<span class="sig-name descname"><span class="pre">register_load_state_dict_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L759"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adam.register_load_state_dict_pre_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_load_state_dict_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/optimizer.py#L759"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adam.register_load_state_dict_pre_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register a load_state_dict pre-hook which will be called before
 <a class="reference internal" href="torch.optim.Optimizer.load_state_dict.html#torch.optim.Optimizer.load_state_dict" title="torch.optim.Optimizer.load_state_dict"><code class="xref py py-meth docutils literal notranslate"><span class="pre">load_state_dict()</span></code></a> is called. It should have the
 following signature:</p>
@@ -831,7 +831,7 @@ <h1>Adam<a class="headerlink" href="#adam" title="Permalink to this heading">¶<
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.Adam.register_state_dict_post_hook">
-<span class="sig-name descname"><span class="pre">register_state_dict_post_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L601"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adam.register_state_dict_post_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_state_dict_post_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/optimizer.py#L601"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adam.register_state_dict_post_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register a state dict post-hook which will be called after <a class="reference internal" href="torch.optim.Optimizer.state_dict.html#torch.optim.Optimizer.state_dict" title="torch.optim.Optimizer.state_dict"><code class="xref py py-meth docutils literal notranslate"><span class="pre">state_dict()</span></code></a> is called.</p>
 <p>It should have the following signature:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">hook</span><span class="p">(</span><span class="n">optimizer</span><span class="p">,</span> <span class="n">state_dict</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">state_dict</span> <span class="ow">or</span> <span class="kc">None</span>
@@ -863,7 +863,7 @@ <h1>Adam<a class="headerlink" href="#adam" title="Permalink to this heading">¶<
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.Adam.register_state_dict_pre_hook">
-<span class="sig-name descname"><span class="pre">register_state_dict_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L569"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adam.register_state_dict_pre_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_state_dict_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/optimizer.py#L569"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adam.register_state_dict_pre_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register a state dict pre-hook which will be called before <a class="reference internal" href="torch.optim.Optimizer.state_dict.html#torch.optim.Optimizer.state_dict" title="torch.optim.Optimizer.state_dict"><code class="xref py py-meth docutils literal notranslate"><span class="pre">state_dict()</span></code></a> is called.</p>
 <p>It should have the following signature:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">hook</span><span class="p">(</span><span class="n">optimizer</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span>
@@ -895,7 +895,7 @@ <h1>Adam<a class="headerlink" href="#adam" title="Permalink to this heading">¶<
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.Adam.register_step_post_hook">
-<span class="sig-name descname"><span class="pre">register_step_post_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L548"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adam.register_step_post_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_step_post_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/optimizer.py#L548"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adam.register_step_post_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register an optimizer step post hook which will be called after optimizer step.</p>
 <p>It should have the following signature:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">hook</span><span class="p">(</span><span class="n">optimizer</span><span class="p">,</span> <span class="n">args</span><span class="p">,</span> <span class="n">kwargs</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span>
@@ -918,7 +918,7 @@ <h1>Adam<a class="headerlink" href="#adam" title="Permalink to this heading">¶<
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.Adam.register_step_pre_hook">
-<span class="sig-name descname"><span class="pre">register_step_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L525"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adam.register_step_pre_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_step_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/optimizer.py#L525"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adam.register_step_pre_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register an optimizer step pre hook which will be called before optimizer step.</p>
 <p>It should have the following signature:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">hook</span><span class="p">(</span><span class="n">optimizer</span><span class="p">,</span> <span class="n">args</span><span class="p">,</span> <span class="n">kwargs</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span> <span class="ow">or</span> <span class="n">modified</span> <span class="n">args</span> <span class="ow">and</span> <span class="n">kwargs</span>
@@ -943,7 +943,7 @@ <h1>Adam<a class="headerlink" href="#adam" title="Permalink to this heading">¶<
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.Adam.state_dict">
-<span class="sig-name descname"><span class="pre">state_dict</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L635"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adam.state_dict" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">state_dict</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/optimizer.py#L635"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adam.state_dict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the state of the optimizer as a <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.13)"><code class="xref py py-class docutils literal notranslate"><span class="pre">dict</span></code></a>.</p>
 <p>It contains two entries:</p>
 <ul class="simple">
@@ -1006,7 +1006,7 @@ <h1>Adam<a class="headerlink" href="#adam" title="Permalink to this heading">¶<
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.Adam.step">
-<span class="sig-name descname"><span class="pre">step</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">closure</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/adam.html#Adam.step"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/adam.py#L212"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adam.step" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">step</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">closure</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/adam.html#Adam.step"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/adam.py#L212"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adam.step" title="Permalink to this definition">¶</a></dt>
 <dd><p>Perform a single optimization step.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -1018,7 +1018,7 @@ <h1>Adam<a class="headerlink" href="#adam" title="Permalink to this heading">¶<
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.Adam.zero_grad">
-<span class="sig-name descname"><span class="pre">zero_grad</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">set_to_none</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L931"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adam.zero_grad" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">zero_grad</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">set_to_none</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/optimizer.py#L931"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adam.zero_grad" title="Permalink to this definition">¶</a></dt>
 <dd><p>Reset the gradients of all optimized <a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.Tensor</span></code></a> s.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.optim.AdamW.html b/main/generated/torch.optim.AdamW.html
index ef1d5d7aa38..959835c52be 100644
--- a/main/generated/torch.optim.AdamW.html
+++ b/main/generated/torch.optim.AdamW.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>AdamW<a class="headerlink" href="#adamw" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.optim.AdamW">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.optim.</span></span><span class="sig-name descname"><span class="pre">AdamW</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">params</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lr</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">betas</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">(0.9,</span> <span class="pre">0.999)</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-08</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weight_decay</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.01</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">amsgrad</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">maximize</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">foreach</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">capturable</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">differentiable</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fused</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/adamw.html#AdamW"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/adamw.py#L21"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.AdamW" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.optim.</span></span><span class="sig-name descname"><span class="pre">AdamW</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">params</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lr</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">betas</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">(0.9,</span> <span class="pre">0.999)</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-08</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weight_decay</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.01</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">amsgrad</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">maximize</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">foreach</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">capturable</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">differentiable</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fused</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/adamw.html#AdamW"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/adamw.py#L21"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.AdamW" title="Permalink to this definition">¶</a></dt>
 <dd><p>Implements AdamW algorithm, where weight decay does not accumulate in the momentum nor variance.</p>
 <div class="math">
 <span class="katex-display"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML" display="block"><semantics><mtable rowspacing="0.25em" columnalign="right left" columnspacing="0em"><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mpadded height="0em" voffset="0em"><mspace mathbackground="black" width="31.298em" height="0.04em"></mspace></mpadded></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mtext mathvariant="bold">input</mtext><mo>:</mo><mi>γ</mi><mtext>(lr)</mtext><mo separator="true">,</mo><mtext> </mtext><msub><mi>β</mi><mn>1</mn></msub><mo separator="true">,</mo><msub><mi>β</mi><mn>2</mn></msub><mtext>(betas)</mtext><mo separator="true">,</mo><mtext> </mtext><msub><mi>θ</mi><mn>0</mn></msub><mtext>(params)</mtext><mo separator="true">,</mo><mtext> </mtext><mi>f</mi><mo stretchy="false">(</mo><mi>θ</mi><mo stretchy="false">)</mo><mtext>(objective)</mtext><mo separator="true">,</mo><mtext> </mtext><mi>ϵ</mi><mtext> (epsilon)</mtext></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="3.6989em"/><mi>λ</mi><mtext>(weight decay)</mtext><mo separator="true">,</mo><mtext> </mtext><mtext mathvariant="italic">amsgrad</mtext><mo separator="true">,</mo><mtext> </mtext><mtext mathvariant="italic">maximize</mtext></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mtext mathvariant="bold">initialize</mtext><mo>:</mo><msub><mi>m</mi><mn>0</mn></msub><mo>←</mo><mn>0</mn><mtext> (first moment)</mtext><mo separator="true">,</mo><msub><mi>v</mi><mn>0</mn></msub><mo>←</mo><mn>0</mn><mtext> ( second moment)</mtext><mo separator="true">,</mo><mtext> </mtext><msubsup><mi>v</mi><mn>0</mn><mrow><mi>m</mi><mi>a</mi><mi>x</mi></mrow></msubsup><mo>←</mo><mn>0</mn></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mpadded height="0em" voffset="0em"><mspace mathbackground="black" width="31.298em" height="0.04em"></mspace></mpadded></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mtext mathvariant="bold">for</mtext><mtext> </mtext><mi>t</mi><mo>=</mo><mn>1</mn><mtext> </mtext><mtext mathvariant="bold">to</mtext><mtext> </mtext><mo>…</mo><mtext> </mtext><mtext mathvariant="bold">do</mtext></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="1.4226em"/><mtext mathvariant="bold">if</mtext><mtext> </mtext><mtext mathvariant="italic">maximize</mtext><mo>:</mo></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="2.8453em"/><msub><mi>g</mi><mi>t</mi></msub><mo>←</mo><mo>−</mo><msub><mi mathvariant="normal">∇</mi><mi>θ</mi></msub><msub><mi>f</mi><mi>t</mi></msub><mo stretchy="false">(</mo><msub><mi>θ</mi><mrow><mi>t</mi><mo>−</mo><mn>1</mn></mrow></msub><mo stretchy="false">)</mo></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="1.4226em"/><mtext mathvariant="bold">else</mtext></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="2.8453em"/><msub><mi>g</mi><mi>t</mi></msub><mo>←</mo><msub><mi mathvariant="normal">∇</mi><mi>θ</mi></msub><msub><mi>f</mi><mi>t</mi></msub><mo stretchy="false">(</mo><msub><mi>θ</mi><mrow><mi>t</mi><mo>−</mo><mn>1</mn></mrow></msub><mo stretchy="false">)</mo></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="1.4226em"/><msub><mi>θ</mi><mi>t</mi></msub><mo>←</mo><msub><mi>θ</mi><mrow><mi>t</mi><mo>−</mo><mn>1</mn></mrow></msub><mo>−</mo><mi>γ</mi><mi>λ</mi><msub><mi>θ</mi><mrow><mi>t</mi><mo>−</mo><mn>1</mn></mrow></msub></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="1.4226em"/><msub><mi>m</mi><mi>t</mi></msub><mo>←</mo><msub><mi>β</mi><mn>1</mn></msub><msub><mi>m</mi><mrow><mi>t</mi><mo>−</mo><mn>1</mn></mrow></msub><mo>+</mo><mo stretchy="false">(</mo><mn>1</mn><mo>−</mo><msub><mi>β</mi><mn>1</mn></msub><mo stretchy="false">)</mo><msub><mi>g</mi><mi>t</mi></msub></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="1.4226em"/><msub><mi>v</mi><mi>t</mi></msub><mo>←</mo><msub><mi>β</mi><mn>2</mn></msub><msub><mi>v</mi><mrow><mi>t</mi><mo>−</mo><mn>1</mn></mrow></msub><mo>+</mo><mo stretchy="false">(</mo><mn>1</mn><mo>−</mo><msub><mi>β</mi><mn>2</mn></msub><mo stretchy="false">)</mo><msubsup><mi>g</mi><mi>t</mi><mn>2</mn></msubsup></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="1.4226em"/><mover accent="true"><msub><mi>m</mi><mi>t</mi></msub><mo stretchy="true">^</mo></mover><mo>←</mo><msub><mi>m</mi><mi>t</mi></msub><mi mathvariant="normal">/</mi><mo fence="false" stretchy="true" minsize="1.2em" maxsize="1.2em">(</mo><mn>1</mn><mo>−</mo><msubsup><mi>β</mi><mn>1</mn><mi>t</mi></msubsup><mo fence="false" stretchy="true" minsize="1.2em" maxsize="1.2em">)</mo></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="1.4226em"/><mtext mathvariant="bold">if</mtext><mtext> </mtext><mi>a</mi><mi>m</mi><mi>s</mi><mi>g</mi><mi>r</mi><mi>a</mi><mi>d</mi></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="2.8453em"/><msubsup><mi>v</mi><mi>t</mi><mrow><mi>m</mi><mi>a</mi><mi>x</mi></mrow></msubsup><mo>←</mo><mrow><mi mathvariant="normal">m</mi><mi mathvariant="normal">a</mi><mi mathvariant="normal">x</mi></mrow><mo stretchy="false">(</mo><msubsup><mi>v</mi><mrow><mi>t</mi><mo>−</mo><mn>1</mn></mrow><mrow><mi>m</mi><mi>a</mi><mi>x</mi></mrow></msubsup><mo separator="true">,</mo><msub><mi>v</mi><mi>t</mi></msub><mo stretchy="false">)</mo></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="2.8453em"/><mover accent="true"><msub><mi>v</mi><mi>t</mi></msub><mo stretchy="true">^</mo></mover><mo>←</mo><msubsup><mi>v</mi><mi>t</mi><mrow><mi>m</mi><mi>a</mi><mi>x</mi></mrow></msubsup><mi mathvariant="normal">/</mi><mo fence="false" stretchy="true" minsize="1.2em" maxsize="1.2em">(</mo><mn>1</mn><mo>−</mo><msubsup><mi>β</mi><mn>2</mn><mi>t</mi></msubsup><mo fence="false" stretchy="true" minsize="1.2em" maxsize="1.2em">)</mo></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="1.4226em"/><mtext mathvariant="bold">else</mtext></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="2.8453em"/><mover accent="true"><msub><mi>v</mi><mi>t</mi></msub><mo stretchy="true">^</mo></mover><mo>←</mo><msub><mi>v</mi><mi>t</mi></msub><mi mathvariant="normal">/</mi><mo fence="false" stretchy="true" minsize="1.2em" maxsize="1.2em">(</mo><mn>1</mn><mo>−</mo><msubsup><mi>β</mi><mn>2</mn><mi>t</mi></msubsup><mo fence="false" stretchy="true" minsize="1.2em" maxsize="1.2em">)</mo></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="1.4226em"/><msub><mi>θ</mi><mi>t</mi></msub><mo>←</mo><msub><mi>θ</mi><mi>t</mi></msub><mo>−</mo><mi>γ</mi><mover accent="true"><msub><mi>m</mi><mi>t</mi></msub><mo stretchy="true">^</mo></mover><mi mathvariant="normal">/</mi><mo fence="false" stretchy="true" minsize="1.2em" maxsize="1.2em">(</mo><msqrt><mover accent="true"><msub><mi>v</mi><mi>t</mi></msub><mo stretchy="true">^</mo></mover></msqrt><mo>+</mo><mi>ϵ</mi><mo fence="false" stretchy="true" minsize="1.2em" maxsize="1.2em">)</mo></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mpadded height="0em" voffset="0em"><mspace mathbackground="black" width="31.298em" height="0.04em"></mspace></mpadded></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mrow><mrow><mi mathvariant="bold">r</mi><mi mathvariant="bold">e</mi><mi mathvariant="bold">t</mi><mi mathvariant="bold">u</mi><mi mathvariant="bold">r</mi><mi mathvariant="bold">n</mi></mrow><mtext> </mtext><msub><mi mathvariant="bold">θ</mi><mi mathvariant="bold">t</mi></msub></mrow></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mpadded height="0em" voffset="0em"><mspace mathbackground="black" width="31.298em" height="0.04em"></mspace></mpadded></mrow></mstyle></mtd></mtr></mtable><annotation encoding="application/x-tex">\begin{aligned}
@@ -719,7 +719,7 @@ <h1>AdamW<a class="headerlink" href="#adamw" title="Permalink to this heading">
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.AdamW.add_param_group">
-<span class="sig-name descname"><span class="pre">add_param_group</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">param_group</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L1003"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.AdamW.add_param_group" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">add_param_group</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">param_group</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/optimizer.py#L1003"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.AdamW.add_param_group" title="Permalink to this definition">¶</a></dt>
 <dd><p>Add a param group to the <a class="reference internal" href="../optim.html#torch.optim.Optimizer" title="torch.optim.Optimizer"><code class="xref py py-class docutils literal notranslate"><span class="pre">Optimizer</span></code></a> s <cite>param_groups</cite>.</p>
 <p>This can be useful when fine tuning a pre-trained network as frozen layers can be made
 trainable and added to the <a class="reference internal" href="../optim.html#torch.optim.Optimizer" title="torch.optim.Optimizer"><code class="xref py py-class docutils literal notranslate"><span class="pre">Optimizer</span></code></a> as training progresses.</p>
@@ -733,7 +733,7 @@ <h1>AdamW<a class="headerlink" href="#adamw" title="Permalink to this heading">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.AdamW.load_state_dict">
-<span class="sig-name descname"><span class="pre">load_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state_dict</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L832"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.AdamW.load_state_dict" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">load_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state_dict</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/optimizer.py#L832"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.AdamW.load_state_dict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Load the optimizer state.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -757,7 +757,7 @@ <h1>AdamW<a class="headerlink" href="#adamw" title="Permalink to this heading">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.AdamW.register_load_state_dict_post_hook">
-<span class="sig-name descname"><span class="pre">register_load_state_dict_post_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L798"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.AdamW.register_load_state_dict_post_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_load_state_dict_post_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/optimizer.py#L798"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.AdamW.register_load_state_dict_post_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register a load_state_dict post-hook which will be called after
 <a class="reference internal" href="torch.optim.Optimizer.load_state_dict.html#torch.optim.Optimizer.load_state_dict" title="torch.optim.Optimizer.load_state_dict"><code class="xref py py-meth docutils literal notranslate"><span class="pre">load_state_dict()</span></code></a> is called. It should have the
 following signature:</p>
@@ -791,7 +791,7 @@ <h1>AdamW<a class="headerlink" href="#adamw" title="Permalink to this heading">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.AdamW.register_load_state_dict_pre_hook">
-<span class="sig-name descname"><span class="pre">register_load_state_dict_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L759"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.AdamW.register_load_state_dict_pre_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_load_state_dict_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/optimizer.py#L759"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.AdamW.register_load_state_dict_pre_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register a load_state_dict pre-hook which will be called before
 <a class="reference internal" href="torch.optim.Optimizer.load_state_dict.html#torch.optim.Optimizer.load_state_dict" title="torch.optim.Optimizer.load_state_dict"><code class="xref py py-meth docutils literal notranslate"><span class="pre">load_state_dict()</span></code></a> is called. It should have the
 following signature:</p>
@@ -828,7 +828,7 @@ <h1>AdamW<a class="headerlink" href="#adamw" title="Permalink to this heading">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.AdamW.register_state_dict_post_hook">
-<span class="sig-name descname"><span class="pre">register_state_dict_post_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L601"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.AdamW.register_state_dict_post_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_state_dict_post_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/optimizer.py#L601"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.AdamW.register_state_dict_post_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register a state dict post-hook which will be called after <a class="reference internal" href="torch.optim.Optimizer.state_dict.html#torch.optim.Optimizer.state_dict" title="torch.optim.Optimizer.state_dict"><code class="xref py py-meth docutils literal notranslate"><span class="pre">state_dict()</span></code></a> is called.</p>
 <p>It should have the following signature:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">hook</span><span class="p">(</span><span class="n">optimizer</span><span class="p">,</span> <span class="n">state_dict</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">state_dict</span> <span class="ow">or</span> <span class="kc">None</span>
@@ -860,7 +860,7 @@ <h1>AdamW<a class="headerlink" href="#adamw" title="Permalink to this heading">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.AdamW.register_state_dict_pre_hook">
-<span class="sig-name descname"><span class="pre">register_state_dict_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L569"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.AdamW.register_state_dict_pre_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_state_dict_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/optimizer.py#L569"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.AdamW.register_state_dict_pre_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register a state dict pre-hook which will be called before <a class="reference internal" href="torch.optim.Optimizer.state_dict.html#torch.optim.Optimizer.state_dict" title="torch.optim.Optimizer.state_dict"><code class="xref py py-meth docutils literal notranslate"><span class="pre">state_dict()</span></code></a> is called.</p>
 <p>It should have the following signature:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">hook</span><span class="p">(</span><span class="n">optimizer</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span>
@@ -892,7 +892,7 @@ <h1>AdamW<a class="headerlink" href="#adamw" title="Permalink to this heading">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.AdamW.register_step_post_hook">
-<span class="sig-name descname"><span class="pre">register_step_post_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L548"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.AdamW.register_step_post_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_step_post_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/optimizer.py#L548"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.AdamW.register_step_post_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register an optimizer step post hook which will be called after optimizer step.</p>
 <p>It should have the following signature:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">hook</span><span class="p">(</span><span class="n">optimizer</span><span class="p">,</span> <span class="n">args</span><span class="p">,</span> <span class="n">kwargs</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span>
@@ -915,7 +915,7 @@ <h1>AdamW<a class="headerlink" href="#adamw" title="Permalink to this heading">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.AdamW.register_step_pre_hook">
-<span class="sig-name descname"><span class="pre">register_step_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L525"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.AdamW.register_step_pre_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_step_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/optimizer.py#L525"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.AdamW.register_step_pre_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register an optimizer step pre hook which will be called before optimizer step.</p>
 <p>It should have the following signature:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">hook</span><span class="p">(</span><span class="n">optimizer</span><span class="p">,</span> <span class="n">args</span><span class="p">,</span> <span class="n">kwargs</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span> <span class="ow">or</span> <span class="n">modified</span> <span class="n">args</span> <span class="ow">and</span> <span class="n">kwargs</span>
@@ -940,7 +940,7 @@ <h1>AdamW<a class="headerlink" href="#adamw" title="Permalink to this heading">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.AdamW.state_dict">
-<span class="sig-name descname"><span class="pre">state_dict</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L635"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.AdamW.state_dict" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">state_dict</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/optimizer.py#L635"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.AdamW.state_dict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the state of the optimizer as a <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.13)"><code class="xref py py-class docutils literal notranslate"><span class="pre">dict</span></code></a>.</p>
 <p>It contains two entries:</p>
 <ul class="simple">
@@ -1003,7 +1003,7 @@ <h1>AdamW<a class="headerlink" href="#adamw" title="Permalink to this heading">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.AdamW.step">
-<span class="sig-name descname"><span class="pre">step</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">closure</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/adam.py#L212"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.AdamW.step" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">step</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">closure</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/adam.py#L212"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.AdamW.step" title="Permalink to this definition">¶</a></dt>
 <dd><p>Perform a single optimization step.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -1015,7 +1015,7 @@ <h1>AdamW<a class="headerlink" href="#adamw" title="Permalink to this heading">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.AdamW.zero_grad">
-<span class="sig-name descname"><span class="pre">zero_grad</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">set_to_none</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L931"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.AdamW.zero_grad" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">zero_grad</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">set_to_none</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/optimizer.py#L931"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.AdamW.zero_grad" title="Permalink to this definition">¶</a></dt>
 <dd><p>Reset the gradients of all optimized <a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.Tensor</span></code></a> s.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.optim.Adamax.html b/main/generated/torch.optim.Adamax.html
index c74bccebf0e..853e7e7d0b0 100644
--- a/main/generated/torch.optim.Adamax.html
+++ b/main/generated/torch.optim.Adamax.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>Adamax<a class="headerlink" href="#adamax" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.optim.Adamax">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.optim.</span></span><span class="sig-name descname"><span class="pre">Adamax</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">params</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lr</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.002</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">betas</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">(0.9,</span> <span class="pre">0.999)</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-08</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weight_decay</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">foreach</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">maximize</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">differentiable</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">capturable</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/adamax.html#Adamax"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/adamax.py#L28"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adamax" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.optim.</span></span><span class="sig-name descname"><span class="pre">Adamax</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">params</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lr</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.002</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">betas</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">(0.9,</span> <span class="pre">0.999)</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-08</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weight_decay</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">foreach</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">maximize</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">differentiable</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">capturable</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/adamax.html#Adamax"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/adamax.py#L28"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adamax" title="Permalink to this definition">¶</a></dt>
 <dd><p>Implements Adamax algorithm (a variant of Adam based on infinity norm).</p>
 <div class="math">
 <span class="katex-display"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML" display="block"><semantics><mtable rowspacing="0.25em" columnalign="right left" columnspacing="0em"><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mpadded height="0em" voffset="0em"><mspace mathbackground="black" width="31.298em" height="0.04em"></mspace></mpadded></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mtext mathvariant="bold">input</mtext><mo>:</mo><mi>γ</mi><mtext> (lr)</mtext><mo separator="true">,</mo><msub><mi>β</mi><mn>1</mn></msub><mo separator="true">,</mo><msub><mi>β</mi><mn>2</mn></msub><mtext> (betas)</mtext><mo separator="true">,</mo><msub><mi>θ</mi><mn>0</mn></msub><mtext> (params)</mtext><mo separator="true">,</mo><mi>f</mi><mo stretchy="false">(</mo><mi>θ</mi><mo stretchy="false">)</mo><mtext> (objective)</mtext><mo separator="true">,</mo><mtext> </mtext><mi>λ</mi><mtext> (weight decay)</mtext><mo separator="true">,</mo></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="3.6989em"/><mi>ϵ</mi><mtext> (epsilon)</mtext></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mtext mathvariant="bold">initialize</mtext><mo>:</mo><msub><mi>m</mi><mn>0</mn></msub><mo>←</mo><mn>0</mn><mtext> ( first moment)</mtext><mo separator="true">,</mo><msub><mi>u</mi><mn>0</mn></msub><mo>←</mo><mn>0</mn><mtext> ( infinity norm)</mtext></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mpadded height="0em" voffset="0em"><mspace mathbackground="black" width="31.298em" height="0.04em"></mspace></mpadded></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mtext mathvariant="bold">for</mtext><mtext> </mtext><mi>t</mi><mo>=</mo><mn>1</mn><mtext> </mtext><mtext mathvariant="bold">to</mtext><mtext> </mtext><mo>…</mo><mtext> </mtext><mtext mathvariant="bold">do</mtext></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="1.4226em"/><msub><mi>g</mi><mi>t</mi></msub><mo>←</mo><msub><mi mathvariant="normal">∇</mi><mi>θ</mi></msub><msub><mi>f</mi><mi>t</mi></msub><mo stretchy="false">(</mo><msub><mi>θ</mi><mrow><mi>t</mi><mo>−</mo><mn>1</mn></mrow></msub><mo stretchy="false">)</mo></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="1.4226em"/><mi>i</mi><mi>f</mi><mtext> </mtext><mi>λ</mi><mo mathvariant="normal">≠</mo><mn>0</mn></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="2.8453em"/><msub><mi>g</mi><mi>t</mi></msub><mo>←</mo><msub><mi>g</mi><mi>t</mi></msub><mo>+</mo><mi>λ</mi><msub><mi>θ</mi><mrow><mi>t</mi><mo>−</mo><mn>1</mn></mrow></msub></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="1.4226em"/><msub><mi>m</mi><mi>t</mi></msub><mo>←</mo><msub><mi>β</mi><mn>1</mn></msub><msub><mi>m</mi><mrow><mi>t</mi><mo>−</mo><mn>1</mn></mrow></msub><mo>+</mo><mo stretchy="false">(</mo><mn>1</mn><mo>−</mo><msub><mi>β</mi><mn>1</mn></msub><mo stretchy="false">)</mo><msub><mi>g</mi><mi>t</mi></msub></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="1.4226em"/><msub><mi>u</mi><mi>t</mi></msub><mo>←</mo><mrow><mi mathvariant="normal">m</mi><mi mathvariant="normal">a</mi><mi mathvariant="normal">x</mi></mrow><mo stretchy="false">(</mo><msub><mi>β</mi><mn>2</mn></msub><msub><mi>u</mi><mrow><mi>t</mi><mo>−</mo><mn>1</mn></mrow></msub><mo separator="true">,</mo><mi mathvariant="normal">∣</mi><msub><mi>g</mi><mi>t</mi></msub><mi mathvariant="normal">∣</mi><mo>+</mo><mi>ϵ</mi><mo stretchy="false">)</mo></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="1.4226em"/><msub><mi>θ</mi><mi>t</mi></msub><mo>←</mo><msub><mi>θ</mi><mrow><mi>t</mi><mo>−</mo><mn>1</mn></mrow></msub><mo>−</mo><mfrac><mrow><mi>γ</mi><msub><mi>m</mi><mi>t</mi></msub></mrow><mrow><mo stretchy="false">(</mo><mn>1</mn><mo>−</mo><msubsup><mi>β</mi><mn>1</mn><mi>t</mi></msubsup><mo stretchy="false">)</mo><msub><mi>u</mi><mi>t</mi></msub></mrow></mfrac></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mpadded height="0em" voffset="0em"><mspace mathbackground="black" width="31.298em" height="0.04em"></mspace></mpadded></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mrow><mrow><mi mathvariant="bold">r</mi><mi mathvariant="bold">e</mi><mi mathvariant="bold">t</mi><mi mathvariant="bold">u</mi><mi mathvariant="bold">r</mi><mi mathvariant="bold">n</mi></mrow><mtext> </mtext><msub><mi mathvariant="bold">θ</mi><mi mathvariant="bold">t</mi></msub></mrow></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mpadded height="0em" voffset="0em"><mspace mathbackground="black" width="31.298em" height="0.04em"></mspace></mpadded></mrow></mstyle></mtd></mtr></mtable><annotation encoding="application/x-tex">\begin{aligned}
@@ -672,7 +672,7 @@ <h1>Adamax<a class="headerlink" href="#adamax" title="Permalink to this heading"
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.Adamax.add_param_group">
-<span class="sig-name descname"><span class="pre">add_param_group</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">param_group</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L1003"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adamax.add_param_group" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">add_param_group</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">param_group</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/optimizer.py#L1003"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adamax.add_param_group" title="Permalink to this definition">¶</a></dt>
 <dd><p>Add a param group to the <a class="reference internal" href="../optim.html#torch.optim.Optimizer" title="torch.optim.Optimizer"><code class="xref py py-class docutils literal notranslate"><span class="pre">Optimizer</span></code></a> s <cite>param_groups</cite>.</p>
 <p>This can be useful when fine tuning a pre-trained network as frozen layers can be made
 trainable and added to the <a class="reference internal" href="../optim.html#torch.optim.Optimizer" title="torch.optim.Optimizer"><code class="xref py py-class docutils literal notranslate"><span class="pre">Optimizer</span></code></a> as training progresses.</p>
@@ -686,7 +686,7 @@ <h1>Adamax<a class="headerlink" href="#adamax" title="Permalink to this heading"
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.Adamax.load_state_dict">
-<span class="sig-name descname"><span class="pre">load_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state_dict</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L832"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adamax.load_state_dict" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">load_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state_dict</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/optimizer.py#L832"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adamax.load_state_dict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Load the optimizer state.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -710,7 +710,7 @@ <h1>Adamax<a class="headerlink" href="#adamax" title="Permalink to this heading"
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.Adamax.register_load_state_dict_post_hook">
-<span class="sig-name descname"><span class="pre">register_load_state_dict_post_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L798"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adamax.register_load_state_dict_post_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_load_state_dict_post_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/optimizer.py#L798"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adamax.register_load_state_dict_post_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register a load_state_dict post-hook which will be called after
 <a class="reference internal" href="torch.optim.Optimizer.load_state_dict.html#torch.optim.Optimizer.load_state_dict" title="torch.optim.Optimizer.load_state_dict"><code class="xref py py-meth docutils literal notranslate"><span class="pre">load_state_dict()</span></code></a> is called. It should have the
 following signature:</p>
@@ -744,7 +744,7 @@ <h1>Adamax<a class="headerlink" href="#adamax" title="Permalink to this heading"
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.Adamax.register_load_state_dict_pre_hook">
-<span class="sig-name descname"><span class="pre">register_load_state_dict_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L759"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adamax.register_load_state_dict_pre_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_load_state_dict_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/optimizer.py#L759"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adamax.register_load_state_dict_pre_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register a load_state_dict pre-hook which will be called before
 <a class="reference internal" href="torch.optim.Optimizer.load_state_dict.html#torch.optim.Optimizer.load_state_dict" title="torch.optim.Optimizer.load_state_dict"><code class="xref py py-meth docutils literal notranslate"><span class="pre">load_state_dict()</span></code></a> is called. It should have the
 following signature:</p>
@@ -781,7 +781,7 @@ <h1>Adamax<a class="headerlink" href="#adamax" title="Permalink to this heading"
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.Adamax.register_state_dict_post_hook">
-<span class="sig-name descname"><span class="pre">register_state_dict_post_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L601"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adamax.register_state_dict_post_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_state_dict_post_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/optimizer.py#L601"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adamax.register_state_dict_post_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register a state dict post-hook which will be called after <a class="reference internal" href="torch.optim.Optimizer.state_dict.html#torch.optim.Optimizer.state_dict" title="torch.optim.Optimizer.state_dict"><code class="xref py py-meth docutils literal notranslate"><span class="pre">state_dict()</span></code></a> is called.</p>
 <p>It should have the following signature:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">hook</span><span class="p">(</span><span class="n">optimizer</span><span class="p">,</span> <span class="n">state_dict</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">state_dict</span> <span class="ow">or</span> <span class="kc">None</span>
@@ -813,7 +813,7 @@ <h1>Adamax<a class="headerlink" href="#adamax" title="Permalink to this heading"
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.Adamax.register_state_dict_pre_hook">
-<span class="sig-name descname"><span class="pre">register_state_dict_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L569"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adamax.register_state_dict_pre_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_state_dict_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/optimizer.py#L569"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adamax.register_state_dict_pre_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register a state dict pre-hook which will be called before <a class="reference internal" href="torch.optim.Optimizer.state_dict.html#torch.optim.Optimizer.state_dict" title="torch.optim.Optimizer.state_dict"><code class="xref py py-meth docutils literal notranslate"><span class="pre">state_dict()</span></code></a> is called.</p>
 <p>It should have the following signature:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">hook</span><span class="p">(</span><span class="n">optimizer</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span>
@@ -845,7 +845,7 @@ <h1>Adamax<a class="headerlink" href="#adamax" title="Permalink to this heading"
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.Adamax.register_step_post_hook">
-<span class="sig-name descname"><span class="pre">register_step_post_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L548"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adamax.register_step_post_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_step_post_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/optimizer.py#L548"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adamax.register_step_post_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register an optimizer step post hook which will be called after optimizer step.</p>
 <p>It should have the following signature:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">hook</span><span class="p">(</span><span class="n">optimizer</span><span class="p">,</span> <span class="n">args</span><span class="p">,</span> <span class="n">kwargs</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span>
@@ -868,7 +868,7 @@ <h1>Adamax<a class="headerlink" href="#adamax" title="Permalink to this heading"
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.Adamax.register_step_pre_hook">
-<span class="sig-name descname"><span class="pre">register_step_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L525"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adamax.register_step_pre_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_step_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/optimizer.py#L525"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adamax.register_step_pre_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register an optimizer step pre hook which will be called before optimizer step.</p>
 <p>It should have the following signature:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">hook</span><span class="p">(</span><span class="n">optimizer</span><span class="p">,</span> <span class="n">args</span><span class="p">,</span> <span class="n">kwargs</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span> <span class="ow">or</span> <span class="n">modified</span> <span class="n">args</span> <span class="ow">and</span> <span class="n">kwargs</span>
@@ -893,7 +893,7 @@ <h1>Adamax<a class="headerlink" href="#adamax" title="Permalink to this heading"
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.Adamax.state_dict">
-<span class="sig-name descname"><span class="pre">state_dict</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L635"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adamax.state_dict" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">state_dict</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/optimizer.py#L635"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adamax.state_dict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the state of the optimizer as a <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.13)"><code class="xref py py-class docutils literal notranslate"><span class="pre">dict</span></code></a>.</p>
 <p>It contains two entries:</p>
 <ul class="simple">
@@ -956,7 +956,7 @@ <h1>Adamax<a class="headerlink" href="#adamax" title="Permalink to this heading"
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.Adamax.step">
-<span class="sig-name descname"><span class="pre">step</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">closure</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/adamax.html#Adamax.step"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/adamax.py#L121"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adamax.step" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">step</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">closure</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/adamax.html#Adamax.step"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/adamax.py#L121"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adamax.step" title="Permalink to this definition">¶</a></dt>
 <dd><p>Performs a single optimization step.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -968,7 +968,7 @@ <h1>Adamax<a class="headerlink" href="#adamax" title="Permalink to this heading"
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.Adamax.zero_grad">
-<span class="sig-name descname"><span class="pre">zero_grad</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">set_to_none</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L931"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adamax.zero_grad" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">zero_grad</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">set_to_none</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/optimizer.py#L931"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Adamax.zero_grad" title="Permalink to this definition">¶</a></dt>
 <dd><p>Reset the gradients of all optimized <a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.Tensor</span></code></a> s.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.optim.LBFGS.html b/main/generated/torch.optim.LBFGS.html
index b8ad7057509..382486caaf7 100644
--- a/main/generated/torch.optim.LBFGS.html
+++ b/main/generated/torch.optim.LBFGS.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>LBFGS<a class="headerlink" href="#lbfgs" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.optim.LBFGS">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.optim.</span></span><span class="sig-name descname"><span class="pre">LBFGS</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">params</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lr</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">20</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_eval</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tolerance_grad</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-07</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tolerance_change</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-09</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">history_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">line_search_fn</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/lbfgs.html#LBFGS"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lbfgs.py#L183"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.LBFGS" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.optim.</span></span><span class="sig-name descname"><span class="pre">LBFGS</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">params</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lr</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">20</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_eval</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tolerance_grad</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-07</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tolerance_change</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-09</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">history_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">line_search_fn</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/lbfgs.html#LBFGS"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/lbfgs.py#L183"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.LBFGS" title="Permalink to this definition">¶</a></dt>
 <dd><p>Implements L-BFGS algorithm.</p>
 <p>Heavily inspired by <a class="reference external" href="https://www.cs.ubc.ca/~schmidtm/Software/minFunc.html">minFunc</a>.</p>
 <div class="admonition warning">
@@ -651,7 +651,7 @@ <h1>LBFGS<a class="headerlink" href="#lbfgs" title="Permalink to this heading">
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.LBFGS.add_param_group">
-<span class="sig-name descname"><span class="pre">add_param_group</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">param_group</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L1003"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.LBFGS.add_param_group" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">add_param_group</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">param_group</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/optimizer.py#L1003"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.LBFGS.add_param_group" title="Permalink to this definition">¶</a></dt>
 <dd><p>Add a param group to the <a class="reference internal" href="../optim.html#torch.optim.Optimizer" title="torch.optim.Optimizer"><code class="xref py py-class docutils literal notranslate"><span class="pre">Optimizer</span></code></a> s <cite>param_groups</cite>.</p>
 <p>This can be useful when fine tuning a pre-trained network as frozen layers can be made
 trainable and added to the <a class="reference internal" href="../optim.html#torch.optim.Optimizer" title="torch.optim.Optimizer"><code class="xref py py-class docutils literal notranslate"><span class="pre">Optimizer</span></code></a> as training progresses.</p>
@@ -665,7 +665,7 @@ <h1>LBFGS<a class="headerlink" href="#lbfgs" title="Permalink to this heading">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.LBFGS.load_state_dict">
-<span class="sig-name descname"><span class="pre">load_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state_dict</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L832"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.LBFGS.load_state_dict" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">load_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state_dict</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/optimizer.py#L832"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.LBFGS.load_state_dict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Load the optimizer state.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -689,7 +689,7 @@ <h1>LBFGS<a class="headerlink" href="#lbfgs" title="Permalink to this heading">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.LBFGS.register_load_state_dict_post_hook">
-<span class="sig-name descname"><span class="pre">register_load_state_dict_post_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L798"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.LBFGS.register_load_state_dict_post_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_load_state_dict_post_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/optimizer.py#L798"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.LBFGS.register_load_state_dict_post_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register a load_state_dict post-hook which will be called after
 <a class="reference internal" href="torch.optim.Optimizer.load_state_dict.html#torch.optim.Optimizer.load_state_dict" title="torch.optim.Optimizer.load_state_dict"><code class="xref py py-meth docutils literal notranslate"><span class="pre">load_state_dict()</span></code></a> is called. It should have the
 following signature:</p>
@@ -723,7 +723,7 @@ <h1>LBFGS<a class="headerlink" href="#lbfgs" title="Permalink to this heading">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.LBFGS.register_load_state_dict_pre_hook">
-<span class="sig-name descname"><span class="pre">register_load_state_dict_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L759"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.LBFGS.register_load_state_dict_pre_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_load_state_dict_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/optimizer.py#L759"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.LBFGS.register_load_state_dict_pre_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register a load_state_dict pre-hook which will be called before
 <a class="reference internal" href="torch.optim.Optimizer.load_state_dict.html#torch.optim.Optimizer.load_state_dict" title="torch.optim.Optimizer.load_state_dict"><code class="xref py py-meth docutils literal notranslate"><span class="pre">load_state_dict()</span></code></a> is called. It should have the
 following signature:</p>
@@ -760,7 +760,7 @@ <h1>LBFGS<a class="headerlink" href="#lbfgs" title="Permalink to this heading">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.LBFGS.register_state_dict_post_hook">
-<span class="sig-name descname"><span class="pre">register_state_dict_post_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L601"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.LBFGS.register_state_dict_post_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_state_dict_post_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/optimizer.py#L601"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.LBFGS.register_state_dict_post_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register a state dict post-hook which will be called after <a class="reference internal" href="torch.optim.Optimizer.state_dict.html#torch.optim.Optimizer.state_dict" title="torch.optim.Optimizer.state_dict"><code class="xref py py-meth docutils literal notranslate"><span class="pre">state_dict()</span></code></a> is called.</p>
 <p>It should have the following signature:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">hook</span><span class="p">(</span><span class="n">optimizer</span><span class="p">,</span> <span class="n">state_dict</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">state_dict</span> <span class="ow">or</span> <span class="kc">None</span>
@@ -792,7 +792,7 @@ <h1>LBFGS<a class="headerlink" href="#lbfgs" title="Permalink to this heading">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.LBFGS.register_state_dict_pre_hook">
-<span class="sig-name descname"><span class="pre">register_state_dict_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L569"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.LBFGS.register_state_dict_pre_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_state_dict_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/optimizer.py#L569"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.LBFGS.register_state_dict_pre_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register a state dict pre-hook which will be called before <a class="reference internal" href="torch.optim.Optimizer.state_dict.html#torch.optim.Optimizer.state_dict" title="torch.optim.Optimizer.state_dict"><code class="xref py py-meth docutils literal notranslate"><span class="pre">state_dict()</span></code></a> is called.</p>
 <p>It should have the following signature:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">hook</span><span class="p">(</span><span class="n">optimizer</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span>
@@ -824,7 +824,7 @@ <h1>LBFGS<a class="headerlink" href="#lbfgs" title="Permalink to this heading">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.LBFGS.register_step_post_hook">
-<span class="sig-name descname"><span class="pre">register_step_post_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L548"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.LBFGS.register_step_post_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_step_post_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/optimizer.py#L548"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.LBFGS.register_step_post_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register an optimizer step post hook which will be called after optimizer step.</p>
 <p>It should have the following signature:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">hook</span><span class="p">(</span><span class="n">optimizer</span><span class="p">,</span> <span class="n">args</span><span class="p">,</span> <span class="n">kwargs</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span>
@@ -847,7 +847,7 @@ <h1>LBFGS<a class="headerlink" href="#lbfgs" title="Permalink to this heading">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.LBFGS.register_step_pre_hook">
-<span class="sig-name descname"><span class="pre">register_step_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L525"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.LBFGS.register_step_pre_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_step_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/optimizer.py#L525"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.LBFGS.register_step_pre_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register an optimizer step pre hook which will be called before optimizer step.</p>
 <p>It should have the following signature:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">hook</span><span class="p">(</span><span class="n">optimizer</span><span class="p">,</span> <span class="n">args</span><span class="p">,</span> <span class="n">kwargs</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span> <span class="ow">or</span> <span class="n">modified</span> <span class="n">args</span> <span class="ow">and</span> <span class="n">kwargs</span>
@@ -872,7 +872,7 @@ <h1>LBFGS<a class="headerlink" href="#lbfgs" title="Permalink to this heading">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.LBFGS.state_dict">
-<span class="sig-name descname"><span class="pre">state_dict</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L635"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.LBFGS.state_dict" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">state_dict</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/optimizer.py#L635"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.LBFGS.state_dict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the state of the optimizer as a <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.13)"><code class="xref py py-class docutils literal notranslate"><span class="pre">dict</span></code></a>.</p>
 <p>It contains two entries:</p>
 <ul class="simple">
@@ -935,7 +935,7 @@ <h1>LBFGS<a class="headerlink" href="#lbfgs" title="Permalink to this heading">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.LBFGS.step">
-<span class="sig-name descname"><span class="pre">step</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">closure</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/lbfgs.html#LBFGS.step"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lbfgs.py#L301"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.LBFGS.step" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">step</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">closure</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/lbfgs.html#LBFGS.step"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/lbfgs.py#L301"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.LBFGS.step" title="Permalink to this definition">¶</a></dt>
 <dd><p>Perform a single optimization step.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -947,7 +947,7 @@ <h1>LBFGS<a class="headerlink" href="#lbfgs" title="Permalink to this heading">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.LBFGS.zero_grad">
-<span class="sig-name descname"><span class="pre">zero_grad</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">set_to_none</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L931"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.LBFGS.zero_grad" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">zero_grad</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">set_to_none</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/optimizer.py#L931"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.LBFGS.zero_grad" title="Permalink to this definition">¶</a></dt>
 <dd><p>Reset the gradients of all optimized <a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.Tensor</span></code></a> s.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.optim.NAdam.html b/main/generated/torch.optim.NAdam.html
index 72092d12039..617ae9839ee 100644
--- a/main/generated/torch.optim.NAdam.html
+++ b/main/generated/torch.optim.NAdam.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>NAdam<a class="headerlink" href="#nadam" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.optim.NAdam">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.optim.</span></span><span class="sig-name descname"><span class="pre">NAdam</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">params</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lr</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.002</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">betas</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">(0.9,</span> <span class="pre">0.999)</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-08</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weight_decay</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">momentum_decay</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.004</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">decoupled_weight_decay</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">foreach</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">maximize</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">capturable</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">differentiable</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/nadam.html#NAdam"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/nadam.py#L30"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.NAdam" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.optim.</span></span><span class="sig-name descname"><span class="pre">NAdam</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">params</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lr</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.002</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">betas</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">(0.9,</span> <span class="pre">0.999)</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-08</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weight_decay</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">momentum_decay</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.004</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">decoupled_weight_decay</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">foreach</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">maximize</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">capturable</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">differentiable</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/nadam.html#NAdam"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/nadam.py#L30"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.NAdam" title="Permalink to this definition">¶</a></dt>
 <dd><p>Implements NAdam algorithm.</p>
 <div class="math">
 <span class="katex-display"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML" display="block"><semantics><mtable rowspacing="0.25em" columnalign="right left" columnspacing="0em"><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mpadded height="0em" voffset="0em"><mspace mathbackground="black" width="31.298em" height="0.04em"></mspace></mpadded></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mtext mathvariant="bold">input</mtext><mo>:</mo><msub><mi>γ</mi><mi>t</mi></msub><mtext> (lr)</mtext><mo separator="true">,</mo><mtext> </mtext><msub><mi>β</mi><mn>1</mn></msub><mo separator="true">,</mo><msub><mi>β</mi><mn>2</mn></msub><mtext> (betas)</mtext><mo separator="true">,</mo><mtext> </mtext><msub><mi>θ</mi><mn>0</mn></msub><mtext> (params)</mtext><mo separator="true">,</mo><mtext> </mtext><mi>f</mi><mo stretchy="false">(</mo><mi>θ</mi><mo stretchy="false">)</mo><mtext> (objective)</mtext></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="3.6989em"/><mtext> </mtext><mi>λ</mi><mtext> (weight decay)</mtext><mo separator="true">,</mo><mtext> </mtext><mi>ψ</mi><mtext> (momentum decay)</mtext></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="3.6989em"/><mtext> </mtext><mtext mathvariant="italic">decoupled_weight_decay</mtext><mo separator="true">,</mo><mtext> </mtext><mtext mathvariant="italic">maximize</mtext></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mtext mathvariant="bold">initialize</mtext><mo>:</mo><msub><mi>m</mi><mn>0</mn></msub><mo>←</mo><mn>0</mn><mtext> ( first moment)</mtext><mo separator="true">,</mo><msub><mi>v</mi><mn>0</mn></msub><mo>←</mo><mn>0</mn><mtext> ( second moment)</mtext></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mpadded height="0em" voffset="0em"><mspace mathbackground="black" width="31.298em" height="0.04em"></mspace></mpadded></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mtext mathvariant="bold">for</mtext><mtext> </mtext><mi>t</mi><mo>=</mo><mn>1</mn><mtext> </mtext><mtext mathvariant="bold">to</mtext><mtext> </mtext><mo>…</mo><mtext> </mtext><mtext mathvariant="bold">do</mtext></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="1.4226em"/><mtext mathvariant="bold">if</mtext><mtext> </mtext><mtext mathvariant="italic">maximize</mtext><mo>:</mo></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="2.8453em"/><msub><mi>g</mi><mi>t</mi></msub><mo>←</mo><mo>−</mo><msub><mi mathvariant="normal">∇</mi><mi>θ</mi></msub><msub><mi>f</mi><mi>t</mi></msub><mo stretchy="false">(</mo><msub><mi>θ</mi><mrow><mi>t</mi><mo>−</mo><mn>1</mn></mrow></msub><mo stretchy="false">)</mo></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="1.4226em"/><mtext mathvariant="bold">else</mtext></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="2.8453em"/><msub><mi>g</mi><mi>t</mi></msub><mo>←</mo><msub><mi mathvariant="normal">∇</mi><mi>θ</mi></msub><msub><mi>f</mi><mi>t</mi></msub><mo stretchy="false">(</mo><msub><mi>θ</mi><mrow><mi>t</mi><mo>−</mo><mn>1</mn></mrow></msub><mo stretchy="false">)</mo></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="1.4226em"/><msub><mi>θ</mi><mi>t</mi></msub><mo>←</mo><msub><mi>θ</mi><mrow><mi>t</mi><mo>−</mo><mn>1</mn></mrow></msub></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="1.4226em"/><mtext mathvariant="bold">if</mtext><mtext> </mtext><mi>λ</mi><mo mathvariant="normal">≠</mo><mn>0</mn></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="2.8453em"/><mtext mathvariant="bold">if</mtext><mtext> </mtext><mtext mathvariant="italic">decoupled_weight_decay</mtext></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="4.2679em"/><msub><mi>θ</mi><mi>t</mi></msub><mo>←</mo><msub><mi>θ</mi><mrow><mi>t</mi><mo>−</mo><mn>1</mn></mrow></msub><mo>−</mo><mi>γ</mi><mi>λ</mi><msub><mi>θ</mi><mrow><mi>t</mi><mo>−</mo><mn>1</mn></mrow></msub></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="2.8453em"/><mtext mathvariant="bold">else</mtext></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="4.2679em"/><msub><mi>g</mi><mi>t</mi></msub><mo>←</mo><msub><mi>g</mi><mi>t</mi></msub><mo>+</mo><mi>λ</mi><msub><mi>θ</mi><mrow><mi>t</mi><mo>−</mo><mn>1</mn></mrow></msub></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="1.4226em"/><msub><mi>μ</mi><mi>t</mi></msub><mo>←</mo><msub><mi>β</mi><mn>1</mn></msub><mo fence="false" stretchy="true" minsize="1.2em" maxsize="1.2em">(</mo><mn>1</mn><mo>−</mo><mfrac><mn>1</mn><mn>2</mn></mfrac><msup><mn>0.96</mn><mrow><mi>t</mi><mi>ψ</mi></mrow></msup><mo fence="false" stretchy="true" minsize="1.2em" maxsize="1.2em">)</mo></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="1.4226em"/><msub><mi>μ</mi><mrow><mi>t</mi><mo>+</mo><mn>1</mn></mrow></msub><mo>←</mo><msub><mi>β</mi><mn>1</mn></msub><mo fence="false" stretchy="true" minsize="1.2em" maxsize="1.2em">(</mo><mn>1</mn><mo>−</mo><mfrac><mn>1</mn><mn>2</mn></mfrac><msup><mn>0.96</mn><mrow><mo stretchy="false">(</mo><mi>t</mi><mo>+</mo><mn>1</mn><mo stretchy="false">)</mo><mi>ψ</mi></mrow></msup><mo fence="false" stretchy="true" minsize="1.2em" maxsize="1.2em">)</mo></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="1.4226em"/><msub><mi>m</mi><mi>t</mi></msub><mo>←</mo><msub><mi>β</mi><mn>1</mn></msub><msub><mi>m</mi><mrow><mi>t</mi><mo>−</mo><mn>1</mn></mrow></msub><mo>+</mo><mo stretchy="false">(</mo><mn>1</mn><mo>−</mo><msub><mi>β</mi><mn>1</mn></msub><mo stretchy="false">)</mo><msub><mi>g</mi><mi>t</mi></msub></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="1.4226em"/><msub><mi>v</mi><mi>t</mi></msub><mo>←</mo><msub><mi>β</mi><mn>2</mn></msub><msub><mi>v</mi><mrow><mi>t</mi><mo>−</mo><mn>1</mn></mrow></msub><mo>+</mo><mo stretchy="false">(</mo><mn>1</mn><mo>−</mo><msub><mi>β</mi><mn>2</mn></msub><mo stretchy="false">)</mo><msubsup><mi>g</mi><mi>t</mi><mn>2</mn></msubsup></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="1.4226em"/><mover accent="true"><msub><mi>m</mi><mi>t</mi></msub><mo stretchy="true">^</mo></mover><mo>←</mo><msub><mi>μ</mi><mrow><mi>t</mi><mo>+</mo><mn>1</mn></mrow></msub><msub><mi>m</mi><mi>t</mi></msub><mi mathvariant="normal">/</mi><mo stretchy="false">(</mo><mn>1</mn><mo>−</mo><munderover><mo>∏</mo><mrow><mi>i</mi><mo>=</mo><mn>1</mn></mrow><mrow><mi>t</mi><mo>+</mo><mn>1</mn></mrow></munderover><msub><mi>μ</mi><mi>i</mi></msub><mo stretchy="false">)</mo></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="3.1298em"/><mo>+</mo><mo stretchy="false">(</mo><mn>1</mn><mo>−</mo><msub><mi>μ</mi><mi>t</mi></msub><mo stretchy="false">)</mo><msub><mi>g</mi><mi>t</mi></msub><mi mathvariant="normal">/</mi><mo stretchy="false">(</mo><mn>1</mn><mo>−</mo><munderover><mo>∏</mo><mrow><mi>i</mi><mo>=</mo><mn>1</mn></mrow><mi>t</mi></munderover><msub><mi>μ</mi><mi>i</mi></msub><mo stretchy="false">)</mo></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="1.4226em"/><mover accent="true"><msub><mi>v</mi><mi>t</mi></msub><mo stretchy="true">^</mo></mover><mo>←</mo><msub><mi>v</mi><mi>t</mi></msub><mi mathvariant="normal">/</mi><mo fence="false" stretchy="true" minsize="1.2em" maxsize="1.2em">(</mo><mn>1</mn><mo>−</mo><msubsup><mi>β</mi><mn>2</mn><mi>t</mi></msubsup><mo fence="false" stretchy="true" minsize="1.2em" maxsize="1.2em">)</mo></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="1.4226em"/><msub><mi>θ</mi><mi>t</mi></msub><mo>←</mo><msub><mi>θ</mi><mi>t</mi></msub><mo>−</mo><mi>γ</mi><mover accent="true"><msub><mi>m</mi><mi>t</mi></msub><mo stretchy="true">^</mo></mover><mi mathvariant="normal">/</mi><mo fence="false" stretchy="true" minsize="1.2em" maxsize="1.2em">(</mo><msqrt><mover accent="true"><msub><mi>v</mi><mi>t</mi></msub><mo stretchy="true">^</mo></mover></msqrt><mo>+</mo><mi>ϵ</mi><mo fence="false" stretchy="true" minsize="1.2em" maxsize="1.2em">)</mo></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mpadded height="0em" voffset="0em"><mspace mathbackground="black" width="31.298em" height="0.04em"></mspace></mpadded></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mrow><mrow><mi mathvariant="bold">r</mi><mi mathvariant="bold">e</mi><mi mathvariant="bold">t</mi><mi mathvariant="bold">u</mi><mi mathvariant="bold">r</mi><mi mathvariant="bold">n</mi></mrow><mtext> </mtext><msub><mi mathvariant="bold">θ</mi><mi mathvariant="bold">t</mi></msub></mrow></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mpadded height="0em" voffset="0em"><mspace mathbackground="black" width="31.298em" height="0.04em"></mspace></mpadded></mrow></mstyle></mtd></mtr></mtable><annotation encoding="application/x-tex">\begin{aligned}
@@ -703,7 +703,7 @@ <h1>NAdam<a class="headerlink" href="#nadam" title="Permalink to this heading">
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.NAdam.add_param_group">
-<span class="sig-name descname"><span class="pre">add_param_group</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">param_group</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L1003"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.NAdam.add_param_group" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">add_param_group</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">param_group</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/optimizer.py#L1003"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.NAdam.add_param_group" title="Permalink to this definition">¶</a></dt>
 <dd><p>Add a param group to the <a class="reference internal" href="../optim.html#torch.optim.Optimizer" title="torch.optim.Optimizer"><code class="xref py py-class docutils literal notranslate"><span class="pre">Optimizer</span></code></a> s <cite>param_groups</cite>.</p>
 <p>This can be useful when fine tuning a pre-trained network as frozen layers can be made
 trainable and added to the <a class="reference internal" href="../optim.html#torch.optim.Optimizer" title="torch.optim.Optimizer"><code class="xref py py-class docutils literal notranslate"><span class="pre">Optimizer</span></code></a> as training progresses.</p>
@@ -717,7 +717,7 @@ <h1>NAdam<a class="headerlink" href="#nadam" title="Permalink to this heading">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.NAdam.load_state_dict">
-<span class="sig-name descname"><span class="pre">load_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state_dict</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L832"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.NAdam.load_state_dict" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">load_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state_dict</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/optimizer.py#L832"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.NAdam.load_state_dict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Load the optimizer state.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -741,7 +741,7 @@ <h1>NAdam<a class="headerlink" href="#nadam" title="Permalink to this heading">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.NAdam.register_load_state_dict_post_hook">
-<span class="sig-name descname"><span class="pre">register_load_state_dict_post_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L798"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.NAdam.register_load_state_dict_post_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_load_state_dict_post_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/optimizer.py#L798"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.NAdam.register_load_state_dict_post_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register a load_state_dict post-hook which will be called after
 <a class="reference internal" href="torch.optim.Optimizer.load_state_dict.html#torch.optim.Optimizer.load_state_dict" title="torch.optim.Optimizer.load_state_dict"><code class="xref py py-meth docutils literal notranslate"><span class="pre">load_state_dict()</span></code></a> is called. It should have the
 following signature:</p>
@@ -775,7 +775,7 @@ <h1>NAdam<a class="headerlink" href="#nadam" title="Permalink to this heading">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.NAdam.register_load_state_dict_pre_hook">
-<span class="sig-name descname"><span class="pre">register_load_state_dict_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L759"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.NAdam.register_load_state_dict_pre_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_load_state_dict_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/optimizer.py#L759"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.NAdam.register_load_state_dict_pre_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register a load_state_dict pre-hook which will be called before
 <a class="reference internal" href="torch.optim.Optimizer.load_state_dict.html#torch.optim.Optimizer.load_state_dict" title="torch.optim.Optimizer.load_state_dict"><code class="xref py py-meth docutils literal notranslate"><span class="pre">load_state_dict()</span></code></a> is called. It should have the
 following signature:</p>
@@ -812,7 +812,7 @@ <h1>NAdam<a class="headerlink" href="#nadam" title="Permalink to this heading">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.NAdam.register_state_dict_post_hook">
-<span class="sig-name descname"><span class="pre">register_state_dict_post_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L601"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.NAdam.register_state_dict_post_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_state_dict_post_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/optimizer.py#L601"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.NAdam.register_state_dict_post_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register a state dict post-hook which will be called after <a class="reference internal" href="torch.optim.Optimizer.state_dict.html#torch.optim.Optimizer.state_dict" title="torch.optim.Optimizer.state_dict"><code class="xref py py-meth docutils literal notranslate"><span class="pre">state_dict()</span></code></a> is called.</p>
 <p>It should have the following signature:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">hook</span><span class="p">(</span><span class="n">optimizer</span><span class="p">,</span> <span class="n">state_dict</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">state_dict</span> <span class="ow">or</span> <span class="kc">None</span>
@@ -844,7 +844,7 @@ <h1>NAdam<a class="headerlink" href="#nadam" title="Permalink to this heading">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.NAdam.register_state_dict_pre_hook">
-<span class="sig-name descname"><span class="pre">register_state_dict_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L569"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.NAdam.register_state_dict_pre_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_state_dict_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/optimizer.py#L569"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.NAdam.register_state_dict_pre_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register a state dict pre-hook which will be called before <a class="reference internal" href="torch.optim.Optimizer.state_dict.html#torch.optim.Optimizer.state_dict" title="torch.optim.Optimizer.state_dict"><code class="xref py py-meth docutils literal notranslate"><span class="pre">state_dict()</span></code></a> is called.</p>
 <p>It should have the following signature:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">hook</span><span class="p">(</span><span class="n">optimizer</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span>
@@ -876,7 +876,7 @@ <h1>NAdam<a class="headerlink" href="#nadam" title="Permalink to this heading">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.NAdam.register_step_post_hook">
-<span class="sig-name descname"><span class="pre">register_step_post_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L548"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.NAdam.register_step_post_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_step_post_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/optimizer.py#L548"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.NAdam.register_step_post_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register an optimizer step post hook which will be called after optimizer step.</p>
 <p>It should have the following signature:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">hook</span><span class="p">(</span><span class="n">optimizer</span><span class="p">,</span> <span class="n">args</span><span class="p">,</span> <span class="n">kwargs</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span>
@@ -899,7 +899,7 @@ <h1>NAdam<a class="headerlink" href="#nadam" title="Permalink to this heading">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.NAdam.register_step_pre_hook">
-<span class="sig-name descname"><span class="pre">register_step_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L525"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.NAdam.register_step_pre_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_step_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/optimizer.py#L525"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.NAdam.register_step_pre_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register an optimizer step pre hook which will be called before optimizer step.</p>
 <p>It should have the following signature:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">hook</span><span class="p">(</span><span class="n">optimizer</span><span class="p">,</span> <span class="n">args</span><span class="p">,</span> <span class="n">kwargs</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span> <span class="ow">or</span> <span class="n">modified</span> <span class="n">args</span> <span class="ow">and</span> <span class="n">kwargs</span>
@@ -924,7 +924,7 @@ <h1>NAdam<a class="headerlink" href="#nadam" title="Permalink to this heading">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.NAdam.state_dict">
-<span class="sig-name descname"><span class="pre">state_dict</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L635"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.NAdam.state_dict" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">state_dict</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/optimizer.py#L635"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.NAdam.state_dict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the state of the optimizer as a <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.13)"><code class="xref py py-class docutils literal notranslate"><span class="pre">dict</span></code></a>.</p>
 <p>It contains two entries:</p>
 <ul class="simple">
@@ -987,7 +987,7 @@ <h1>NAdam<a class="headerlink" href="#nadam" title="Permalink to this heading">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.NAdam.step">
-<span class="sig-name descname"><span class="pre">step</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">closure</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/nadam.html#NAdam.step"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/nadam.py#L154"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.NAdam.step" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">step</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">closure</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/nadam.html#NAdam.step"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/nadam.py#L154"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.NAdam.step" title="Permalink to this definition">¶</a></dt>
 <dd><p>Perform a single optimization step.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -999,7 +999,7 @@ <h1>NAdam<a class="headerlink" href="#nadam" title="Permalink to this heading">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.NAdam.zero_grad">
-<span class="sig-name descname"><span class="pre">zero_grad</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">set_to_none</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L931"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.NAdam.zero_grad" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">zero_grad</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">set_to_none</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/optimizer.py#L931"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.NAdam.zero_grad" title="Permalink to this definition">¶</a></dt>
 <dd><p>Reset the gradients of all optimized <a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.Tensor</span></code></a> s.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.optim.Optimizer.add_param_group.html b/main/generated/torch.optim.Optimizer.add_param_group.html
index 7a287e53dc7..aed583c2287 100644
--- a/main/generated/torch.optim.Optimizer.add_param_group.html
+++ b/main/generated/torch.optim.Optimizer.add_param_group.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.optim.Optimizer.add_param_group<a class="headerlink" href="#torch-optim-optimizer-add-param-group" title="Permalink to this heading">¶</a></h1>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.Optimizer.add_param_group">
-<span class="sig-prename descclassname"><span class="pre">Optimizer.</span></span><span class="sig-name descname"><span class="pre">add_param_group</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">param_group</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/optimizer.html#Optimizer.add_param_group"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L1003"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Optimizer.add_param_group" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">Optimizer.</span></span><span class="sig-name descname"><span class="pre">add_param_group</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">param_group</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/optimizer.html#Optimizer.add_param_group"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/optimizer.py#L1003"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Optimizer.add_param_group" title="Permalink to this definition">¶</a></dt>
 <dd><p>Add a param group to the <a class="reference internal" href="../optim.html#torch.optim.Optimizer" title="torch.optim.Optimizer"><code class="xref py py-class docutils literal notranslate"><span class="pre">Optimizer</span></code></a> s <cite>param_groups</cite>.</p>
 <p>This can be useful when fine tuning a pre-trained network as frozen layers can be made
 trainable and added to the <a class="reference internal" href="../optim.html#torch.optim.Optimizer" title="torch.optim.Optimizer"><code class="xref py py-class docutils literal notranslate"><span class="pre">Optimizer</span></code></a> as training progresses.</p>
diff --git a/main/generated/torch.optim.Optimizer.load_state_dict.html b/main/generated/torch.optim.Optimizer.load_state_dict.html
index 366920e5d92..da34d48b9b6 100644
--- a/main/generated/torch.optim.Optimizer.load_state_dict.html
+++ b/main/generated/torch.optim.Optimizer.load_state_dict.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.optim.Optimizer.load_state_dict<a class="headerlink" href="#torch-optim-optimizer-load-state-dict" title="Permalink to this heading">¶</a></h1>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.Optimizer.load_state_dict">
-<span class="sig-prename descclassname"><span class="pre">Optimizer.</span></span><span class="sig-name descname"><span class="pre">load_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state_dict</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/optimizer.html#Optimizer.load_state_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L832"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Optimizer.load_state_dict" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">Optimizer.</span></span><span class="sig-name descname"><span class="pre">load_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state_dict</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/optimizer.html#Optimizer.load_state_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/optimizer.py#L832"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Optimizer.load_state_dict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Load the optimizer state.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.optim.Optimizer.register_load_state_dict_post_hook.html b/main/generated/torch.optim.Optimizer.register_load_state_dict_post_hook.html
index 41131aab9b2..f315ae41a17 100644
--- a/main/generated/torch.optim.Optimizer.register_load_state_dict_post_hook.html
+++ b/main/generated/torch.optim.Optimizer.register_load_state_dict_post_hook.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.optim.Optimizer.register_load_state_dict_post_hook<a class="headerlink" href="#torch-optim-optimizer-register-load-state-dict-post-hook" title="Permalink to this heading">¶</a></h1>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.Optimizer.register_load_state_dict_post_hook">
-<span class="sig-prename descclassname"><span class="pre">Optimizer.</span></span><span class="sig-name descname"><span class="pre">register_load_state_dict_post_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/optimizer.html#Optimizer.register_load_state_dict_post_hook"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L798"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Optimizer.register_load_state_dict_post_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">Optimizer.</span></span><span class="sig-name descname"><span class="pre">register_load_state_dict_post_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/optimizer.html#Optimizer.register_load_state_dict_post_hook"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/optimizer.py#L798"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Optimizer.register_load_state_dict_post_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register a load_state_dict post-hook which will be called after
 <a class="reference internal" href="torch.optim.Optimizer.load_state_dict.html#torch.optim.Optimizer.load_state_dict" title="torch.optim.Optimizer.load_state_dict"><code class="xref py py-meth docutils literal notranslate"><span class="pre">load_state_dict()</span></code></a> is called. It should have the
 following signature:</p>
diff --git a/main/generated/torch.optim.Optimizer.register_load_state_dict_pre_hook.html b/main/generated/torch.optim.Optimizer.register_load_state_dict_pre_hook.html
index ffe0ddc73ff..6c444b6c88e 100644
--- a/main/generated/torch.optim.Optimizer.register_load_state_dict_pre_hook.html
+++ b/main/generated/torch.optim.Optimizer.register_load_state_dict_pre_hook.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.optim.Optimizer.register_load_state_dict_pre_hook<a class="headerlink" href="#torch-optim-optimizer-register-load-state-dict-pre-hook" title="Permalink to this heading">¶</a></h1>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.Optimizer.register_load_state_dict_pre_hook">
-<span class="sig-prename descclassname"><span class="pre">Optimizer.</span></span><span class="sig-name descname"><span class="pre">register_load_state_dict_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/optimizer.html#Optimizer.register_load_state_dict_pre_hook"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L759"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Optimizer.register_load_state_dict_pre_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">Optimizer.</span></span><span class="sig-name descname"><span class="pre">register_load_state_dict_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/optimizer.html#Optimizer.register_load_state_dict_pre_hook"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/optimizer.py#L759"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Optimizer.register_load_state_dict_pre_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register a load_state_dict pre-hook which will be called before
 <a class="reference internal" href="torch.optim.Optimizer.load_state_dict.html#torch.optim.Optimizer.load_state_dict" title="torch.optim.Optimizer.load_state_dict"><code class="xref py py-meth docutils literal notranslate"><span class="pre">load_state_dict()</span></code></a> is called. It should have the
 following signature:</p>
diff --git a/main/generated/torch.optim.Optimizer.register_state_dict_post_hook.html b/main/generated/torch.optim.Optimizer.register_state_dict_post_hook.html
index 7c4cb0d2c55..7b7c23ddbd5 100644
--- a/main/generated/torch.optim.Optimizer.register_state_dict_post_hook.html
+++ b/main/generated/torch.optim.Optimizer.register_state_dict_post_hook.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.optim.Optimizer.register_state_dict_post_hook<a class="headerlink" href="#torch-optim-optimizer-register-state-dict-post-hook" title="Permalink to this heading">¶</a></h1>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.Optimizer.register_state_dict_post_hook">
-<span class="sig-prename descclassname"><span class="pre">Optimizer.</span></span><span class="sig-name descname"><span class="pre">register_state_dict_post_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/optimizer.html#Optimizer.register_state_dict_post_hook"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L601"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Optimizer.register_state_dict_post_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">Optimizer.</span></span><span class="sig-name descname"><span class="pre">register_state_dict_post_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/optimizer.html#Optimizer.register_state_dict_post_hook"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/optimizer.py#L601"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Optimizer.register_state_dict_post_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register a state dict post-hook which will be called after <a class="reference internal" href="torch.optim.Optimizer.state_dict.html#torch.optim.Optimizer.state_dict" title="torch.optim.Optimizer.state_dict"><code class="xref py py-meth docutils literal notranslate"><span class="pre">state_dict()</span></code></a> is called.</p>
 <p>It should have the following signature:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">hook</span><span class="p">(</span><span class="n">optimizer</span><span class="p">,</span> <span class="n">state_dict</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">state_dict</span> <span class="ow">or</span> <span class="kc">None</span>
diff --git a/main/generated/torch.optim.Optimizer.register_state_dict_pre_hook.html b/main/generated/torch.optim.Optimizer.register_state_dict_pre_hook.html
index 594a619895d..45b46e986fb 100644
--- a/main/generated/torch.optim.Optimizer.register_state_dict_pre_hook.html
+++ b/main/generated/torch.optim.Optimizer.register_state_dict_pre_hook.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.optim.Optimizer.register_state_dict_pre_hook<a class="headerlink" href="#torch-optim-optimizer-register-state-dict-pre-hook" title="Permalink to this heading">¶</a></h1>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.Optimizer.register_state_dict_pre_hook">
-<span class="sig-prename descclassname"><span class="pre">Optimizer.</span></span><span class="sig-name descname"><span class="pre">register_state_dict_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/optimizer.html#Optimizer.register_state_dict_pre_hook"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L569"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Optimizer.register_state_dict_pre_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">Optimizer.</span></span><span class="sig-name descname"><span class="pre">register_state_dict_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/optimizer.html#Optimizer.register_state_dict_pre_hook"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/optimizer.py#L569"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Optimizer.register_state_dict_pre_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register a state dict pre-hook which will be called before <a class="reference internal" href="torch.optim.Optimizer.state_dict.html#torch.optim.Optimizer.state_dict" title="torch.optim.Optimizer.state_dict"><code class="xref py py-meth docutils literal notranslate"><span class="pre">state_dict()</span></code></a> is called.</p>
 <p>It should have the following signature:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">hook</span><span class="p">(</span><span class="n">optimizer</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span>
diff --git a/main/generated/torch.optim.Optimizer.register_step_post_hook.html b/main/generated/torch.optim.Optimizer.register_step_post_hook.html
index baa3b4a21ba..3309f9cbebb 100644
--- a/main/generated/torch.optim.Optimizer.register_step_post_hook.html
+++ b/main/generated/torch.optim.Optimizer.register_step_post_hook.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.optim.Optimizer.register_step_post_hook<a class="headerlink" href="#torch-optim-optimizer-register-step-post-hook" title="Permalink to this heading">¶</a></h1>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.Optimizer.register_step_post_hook">
-<span class="sig-prename descclassname"><span class="pre">Optimizer.</span></span><span class="sig-name descname"><span class="pre">register_step_post_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/optimizer.html#Optimizer.register_step_post_hook"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L548"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Optimizer.register_step_post_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">Optimizer.</span></span><span class="sig-name descname"><span class="pre">register_step_post_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/optimizer.html#Optimizer.register_step_post_hook"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/optimizer.py#L548"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Optimizer.register_step_post_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register an optimizer step post hook which will be called after optimizer step.</p>
 <p>It should have the following signature:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">hook</span><span class="p">(</span><span class="n">optimizer</span><span class="p">,</span> <span class="n">args</span><span class="p">,</span> <span class="n">kwargs</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span>
diff --git a/main/generated/torch.optim.Optimizer.register_step_pre_hook.html b/main/generated/torch.optim.Optimizer.register_step_pre_hook.html
index ab64b906d26..ff14cbd33c3 100644
--- a/main/generated/torch.optim.Optimizer.register_step_pre_hook.html
+++ b/main/generated/torch.optim.Optimizer.register_step_pre_hook.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.optim.Optimizer.register_step_pre_hook<a class="headerlink" href="#torch-optim-optimizer-register-step-pre-hook" title="Permalink to this heading">¶</a></h1>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.Optimizer.register_step_pre_hook">
-<span class="sig-prename descclassname"><span class="pre">Optimizer.</span></span><span class="sig-name descname"><span class="pre">register_step_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/optimizer.html#Optimizer.register_step_pre_hook"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L525"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Optimizer.register_step_pre_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">Optimizer.</span></span><span class="sig-name descname"><span class="pre">register_step_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/optimizer.html#Optimizer.register_step_pre_hook"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/optimizer.py#L525"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Optimizer.register_step_pre_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register an optimizer step pre hook which will be called before optimizer step.</p>
 <p>It should have the following signature:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">hook</span><span class="p">(</span><span class="n">optimizer</span><span class="p">,</span> <span class="n">args</span><span class="p">,</span> <span class="n">kwargs</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span> <span class="ow">or</span> <span class="n">modified</span> <span class="n">args</span> <span class="ow">and</span> <span class="n">kwargs</span>
diff --git a/main/generated/torch.optim.Optimizer.state_dict.html b/main/generated/torch.optim.Optimizer.state_dict.html
index 5c641320bfe..36b63b9d5e5 100644
--- a/main/generated/torch.optim.Optimizer.state_dict.html
+++ b/main/generated/torch.optim.Optimizer.state_dict.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.optim.Optimizer.state_dict<a class="headerlink" href="#torch-optim-optimizer-state-dict" title="Permalink to this heading">¶</a></h1>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.Optimizer.state_dict">
-<span class="sig-prename descclassname"><span class="pre">Optimizer.</span></span><span class="sig-name descname"><span class="pre">state_dict</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/optimizer.html#Optimizer.state_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L635"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Optimizer.state_dict" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">Optimizer.</span></span><span class="sig-name descname"><span class="pre">state_dict</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/optimizer.html#Optimizer.state_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/optimizer.py#L635"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Optimizer.state_dict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the state of the optimizer as a <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.13)"><code class="xref py py-class docutils literal notranslate"><span class="pre">dict</span></code></a>.</p>
 <p>It contains two entries:</p>
 <ul class="simple">
diff --git a/main/generated/torch.optim.Optimizer.step.html b/main/generated/torch.optim.Optimizer.step.html
index 7a80d111d3f..85c7d075626 100644
--- a/main/generated/torch.optim.Optimizer.step.html
+++ b/main/generated/torch.optim.Optimizer.step.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.optim.Optimizer.step<a class="headerlink" href="#torch-optim-optimizer-step" title="Permalink to this heading">¶</a></h1>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.Optimizer.step">
-<span class="sig-prename descclassname"><span class="pre">Optimizer.</span></span><span class="sig-name descname"><span class="pre">step</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">closure</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a></span></span><a class="reference internal" href="../_modules/torch/optim/optimizer.html#Optimizer.step"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L994"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Optimizer.step" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">Optimizer.</span></span><span class="sig-name descname"><span class="pre">step</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">closure</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a></span></span><a class="reference internal" href="../_modules/torch/optim/optimizer.html#Optimizer.step"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/optimizer.py#L994"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Optimizer.step" title="Permalink to this definition">¶</a></dt>
 <dt class="sig sig-object py">
 <span class="sig-prename descclassname"><span class="pre">Optimizer.</span></span><span class="sig-name descname"><span class="pre">step</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">closure</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Callable" title="(in Python v3.13)"><span class="pre">Callable</span></a><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.13)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.13)"><span class="pre">float</span></a></span></span></dt>
 <dd><p>Perform a single optimization step to update parameter.</p>
diff --git a/main/generated/torch.optim.Optimizer.zero_grad.html b/main/generated/torch.optim.Optimizer.zero_grad.html
index 946443e36f5..cd1014997dc 100644
--- a/main/generated/torch.optim.Optimizer.zero_grad.html
+++ b/main/generated/torch.optim.Optimizer.zero_grad.html
@@ -300,7 +300,7 @@
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     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.optim.Optimizer.zero_grad<a class="headerlink" href="#torch-optim-optimizer-zero-grad" title="Permalink to this heading">¶</a></h1>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.Optimizer.zero_grad">
-<span class="sig-prename descclassname"><span class="pre">Optimizer.</span></span><span class="sig-name descname"><span class="pre">zero_grad</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">set_to_none</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/optimizer.html#Optimizer.zero_grad"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L931"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Optimizer.zero_grad" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">Optimizer.</span></span><span class="sig-name descname"><span class="pre">zero_grad</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">set_to_none</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/optimizer.html#Optimizer.zero_grad"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/optimizer.py#L931"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Optimizer.zero_grad" title="Permalink to this definition">¶</a></dt>
 <dd><p>Reset the gradients of all optimized <a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.Tensor</span></code></a> s.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.optim.RAdam.html b/main/generated/torch.optim.RAdam.html
index 088508e6340..b4f87a8286e 100644
--- a/main/generated/torch.optim.RAdam.html
+++ b/main/generated/torch.optim.RAdam.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>RAdam<a class="headerlink" href="#radam" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.optim.RAdam">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.optim.</span></span><span class="sig-name descname"><span class="pre">RAdam</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">params</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lr</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">betas</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">(0.9,</span> <span class="pre">0.999)</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-08</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weight_decay</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">decoupled_weight_decay</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">foreach</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">maximize</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">capturable</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">differentiable</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/radam.html#RAdam"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/radam.py#L29"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.RAdam" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.optim.</span></span><span class="sig-name descname"><span class="pre">RAdam</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">params</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lr</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">betas</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">(0.9,</span> <span class="pre">0.999)</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-08</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weight_decay</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">decoupled_weight_decay</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">foreach</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">maximize</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">capturable</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">differentiable</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/radam.html#RAdam"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/radam.py#L29"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.RAdam" title="Permalink to this definition">¶</a></dt>
 <dd><p>Implements RAdam algorithm.</p>
 <div class="math">
 <span class="katex-display"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML" display="block"><semantics><mtable rowspacing="0.25em" columnalign="right left" columnspacing="0em"><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mpadded height="0em" voffset="0em"><mspace mathbackground="black" width="31.298em" height="0.04em"></mspace></mpadded></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mtext mathvariant="bold">input</mtext><mo>:</mo><mi>γ</mi><mtext> (lr)</mtext><mo separator="true">,</mo><mtext> </mtext><msub><mi>β</mi><mn>1</mn></msub><mo separator="true">,</mo><msub><mi>β</mi><mn>2</mn></msub><mtext> (betas)</mtext><mo separator="true">,</mo><mtext> </mtext><msub><mi>θ</mi><mn>0</mn></msub><mtext> (params)</mtext><mo separator="true">,</mo><mtext> </mtext><mi>f</mi><mo stretchy="false">(</mo><mi>θ</mi><mo stretchy="false">)</mo><mtext> (objective)</mtext><mo separator="true">,</mo><mtext> </mtext><mi>λ</mi><mtext> (weightdecay)</mtext><mo separator="true">,</mo><mtext> </mtext><mtext mathvariant="italic">maximize</mtext></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="3.6989em"/><mi>ϵ</mi><mtext> (epsilon)</mtext><mo separator="true">,</mo><mtext mathvariant="italic">decoupled_weight_decay</mtext></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mtext mathvariant="bold">initialize</mtext><mo>:</mo><msub><mi>m</mi><mn>0</mn></msub><mo>←</mo><mn>0</mn><mtext> ( first moment)</mtext><mo separator="true">,</mo><msub><mi>v</mi><mn>0</mn></msub><mo>←</mo><mn>0</mn><mtext> ( second moment)</mtext><mo separator="true">,</mo></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="5.1215em"/><msub><mi>ρ</mi><mi mathvariant="normal">∞</mi></msub><mo>←</mo><mn>2</mn><mi mathvariant="normal">/</mi><mo stretchy="false">(</mo><mn>1</mn><mo>−</mo><msub><mi>β</mi><mn>2</mn></msub><mo stretchy="false">)</mo><mo>−</mo><mn>1</mn></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mpadded height="0em" voffset="0em"><mspace mathbackground="black" width="31.298em" height="0.04em"></mspace></mpadded></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mtext mathvariant="bold">for</mtext><mtext> </mtext><mi>t</mi><mo>=</mo><mn>1</mn><mtext> </mtext><mtext mathvariant="bold">to</mtext><mtext> </mtext><mo>…</mo><mtext> </mtext><mtext mathvariant="bold">do</mtext></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="1.7072em"/><mtext mathvariant="bold">if</mtext><mtext> </mtext><mtext mathvariant="italic">maximize</mtext><mo>:</mo></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="3.4143em"/><msub><mi>g</mi><mi>t</mi></msub><mo>←</mo><mo>−</mo><msub><mi mathvariant="normal">∇</mi><mi>θ</mi></msub><msub><mi>f</mi><mi>t</mi></msub><mo stretchy="false">(</mo><msub><mi>θ</mi><mrow><mi>t</mi><mo>−</mo><mn>1</mn></mrow></msub><mo stretchy="false">)</mo></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="1.7072em"/><mtext mathvariant="bold">else</mtext></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="3.4143em"/><msub><mi>g</mi><mi>t</mi></msub><mo>←</mo><msub><mi mathvariant="normal">∇</mi><mi>θ</mi></msub><msub><mi>f</mi><mi>t</mi></msub><mo stretchy="false">(</mo><msub><mi>θ</mi><mrow><mi>t</mi><mo>−</mo><mn>1</mn></mrow></msub><mo stretchy="false">)</mo></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="1.7072em"/><msub><mi>θ</mi><mi>t</mi></msub><mo>←</mo><msub><mi>θ</mi><mrow><mi>t</mi><mo>−</mo><mn>1</mn></mrow></msub></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="1.7072em"/><mtext mathvariant="bold">if</mtext><mtext> </mtext><mi>λ</mi><mo mathvariant="normal">≠</mo><mn>0</mn></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="3.4143em"/><mtext mathvariant="bold">if</mtext><mtext> </mtext><mtext mathvariant="italic">decoupled_weight_decay</mtext></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="5.1215em"/><msub><mi>θ</mi><mi>t</mi></msub><mo>←</mo><msub><mi>θ</mi><mi>t</mi></msub><mo>−</mo><mi>γ</mi><mi>λ</mi><msub><mi>θ</mi><mi>t</mi></msub></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="3.4143em"/><mtext mathvariant="bold">else</mtext></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="5.1215em"/><msub><mi>g</mi><mi>t</mi></msub><mo>←</mo><msub><mi>g</mi><mi>t</mi></msub><mo>+</mo><mi>λ</mi><msub><mi>θ</mi><mi>t</mi></msub></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="1.7072em"/><msub><mi>m</mi><mi>t</mi></msub><mo>←</mo><msub><mi>β</mi><mn>1</mn></msub><msub><mi>m</mi><mrow><mi>t</mi><mo>−</mo><mn>1</mn></mrow></msub><mo>+</mo><mo stretchy="false">(</mo><mn>1</mn><mo>−</mo><msub><mi>β</mi><mn>1</mn></msub><mo stretchy="false">)</mo><msub><mi>g</mi><mi>t</mi></msub></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="1.7072em"/><msub><mi>v</mi><mi>t</mi></msub><mo>←</mo><msub><mi>β</mi><mn>2</mn></msub><msub><mi>v</mi><mrow><mi>t</mi><mo>−</mo><mn>1</mn></mrow></msub><mo>+</mo><mo stretchy="false">(</mo><mn>1</mn><mo>−</mo><msub><mi>β</mi><mn>2</mn></msub><mo stretchy="false">)</mo><msubsup><mi>g</mi><mi>t</mi><mn>2</mn></msubsup></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="1.7072em"/><mover accent="true"><msub><mi>m</mi><mi>t</mi></msub><mo stretchy="true">^</mo></mover><mo>←</mo><msub><mi>m</mi><mi>t</mi></msub><mi mathvariant="normal">/</mi><mo fence="false" stretchy="true" minsize="1.2em" maxsize="1.2em">(</mo><mn>1</mn><mo>−</mo><msubsup><mi>β</mi><mn>1</mn><mi>t</mi></msubsup><mo fence="false" stretchy="true" minsize="1.2em" maxsize="1.2em">)</mo></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="1.7072em"/><msub><mi>ρ</mi><mi>t</mi></msub><mo>←</mo><msub><mi>ρ</mi><mi mathvariant="normal">∞</mi></msub><mo>−</mo><mn>2</mn><mi>t</mi><msubsup><mi>β</mi><mn>2</mn><mi>t</mi></msubsup><mi mathvariant="normal">/</mi><mo fence="false" stretchy="true" minsize="1.2em" maxsize="1.2em">(</mo><mn>1</mn><mo>−</mo><msubsup><mi>β</mi><mn>2</mn><mi>t</mi></msubsup><mo fence="false" stretchy="true" minsize="1.2em" maxsize="1.2em">)</mo></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="1.7072em"/><mtext mathvariant="bold">if</mtext><mtext> </mtext><msub><mi>ρ</mi><mi>t</mi></msub><mo>&gt;</mo><mn>5</mn></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="3.4143em"/><msub><mi>l</mi><mi>t</mi></msub><mo>←</mo><mfrac><msqrt><mrow><mo stretchy="false">(</mo><mn>1</mn><mo>−</mo><msubsup><mi>β</mi><mn>2</mn><mi>t</mi></msubsup><mo stretchy="false">)</mo></mrow></msqrt><mrow><msqrt><msub><mi>v</mi><mi>t</mi></msub></msqrt><mo>+</mo><mi>ϵ</mi></mrow></mfrac></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="3.4143em"/><msub><mi>r</mi><mi>t</mi></msub><mo>←</mo><msqrt><mfrac><mrow><mo stretchy="false">(</mo><msub><mi>ρ</mi><mi>t</mi></msub><mo>−</mo><mn>4</mn><mo stretchy="false">)</mo><mo stretchy="false">(</mo><msub><mi>ρ</mi><mi>t</mi></msub><mo>−</mo><mn>2</mn><mo stretchy="false">)</mo><msub><mi>ρ</mi><mi mathvariant="normal">∞</mi></msub></mrow><mrow><mo stretchy="false">(</mo><msub><mi>ρ</mi><mi mathvariant="normal">∞</mi></msub><mo>−</mo><mn>4</mn><mo stretchy="false">)</mo><mo stretchy="false">(</mo><msub><mi>ρ</mi><mi mathvariant="normal">∞</mi></msub><mo>−</mo><mn>2</mn><mo stretchy="false">)</mo><msub><mi>ρ</mi><mi>t</mi></msub></mrow></mfrac></msqrt></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="3.4143em"/><msub><mi>θ</mi><mi>t</mi></msub><mo>←</mo><msub><mi>θ</mi><mi>t</mi></msub><mo>−</mo><mi>γ</mi><mover accent="true"><msub><mi>m</mi><mi>t</mi></msub><mo stretchy="true">^</mo></mover><msub><mi>r</mi><mi>t</mi></msub><msub><mi>l</mi><mi>t</mi></msub></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="1.7072em"/><mtext mathvariant="bold">else</mtext></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="3.4143em"/><msub><mi>θ</mi><mi>t</mi></msub><mo>←</mo><msub><mi>θ</mi><mi>t</mi></msub><mo>−</mo><mi>γ</mi><mover accent="true"><msub><mi>m</mi><mi>t</mi></msub><mo stretchy="true">^</mo></mover></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mpadded height="0em" voffset="0em"><mspace mathbackground="black" width="31.298em" height="0.04em"></mspace></mpadded></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mrow><mrow><mi mathvariant="bold">r</mi><mi mathvariant="bold">e</mi><mi mathvariant="bold">t</mi><mi mathvariant="bold">u</mi><mi mathvariant="bold">r</mi><mi mathvariant="bold">n</mi></mrow><mtext> </mtext><msub><mi mathvariant="bold">θ</mi><mi mathvariant="bold">t</mi></msub></mrow></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mpadded height="0em" voffset="0em"><mspace mathbackground="black" width="31.298em" height="0.04em"></mspace></mpadded></mrow></mstyle></mtd></mtr></mtable><annotation encoding="application/x-tex">\begin{aligned}
@@ -729,7 +729,7 @@ <h1>RAdam<a class="headerlink" href="#radam" title="Permalink to this heading">
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.RAdam.add_param_group">
-<span class="sig-name descname"><span class="pre">add_param_group</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">param_group</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L1003"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.RAdam.add_param_group" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">add_param_group</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">param_group</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/optimizer.py#L1003"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.RAdam.add_param_group" title="Permalink to this definition">¶</a></dt>
 <dd><p>Add a param group to the <a class="reference internal" href="../optim.html#torch.optim.Optimizer" title="torch.optim.Optimizer"><code class="xref py py-class docutils literal notranslate"><span class="pre">Optimizer</span></code></a> s <cite>param_groups</cite>.</p>
 <p>This can be useful when fine tuning a pre-trained network as frozen layers can be made
 trainable and added to the <a class="reference internal" href="../optim.html#torch.optim.Optimizer" title="torch.optim.Optimizer"><code class="xref py py-class docutils literal notranslate"><span class="pre">Optimizer</span></code></a> as training progresses.</p>
@@ -743,7 +743,7 @@ <h1>RAdam<a class="headerlink" href="#radam" title="Permalink to this heading">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.RAdam.load_state_dict">
-<span class="sig-name descname"><span class="pre">load_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state_dict</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L832"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.RAdam.load_state_dict" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">load_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state_dict</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/optimizer.py#L832"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.RAdam.load_state_dict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Load the optimizer state.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -767,7 +767,7 @@ <h1>RAdam<a class="headerlink" href="#radam" title="Permalink to this heading">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.RAdam.register_load_state_dict_post_hook">
-<span class="sig-name descname"><span class="pre">register_load_state_dict_post_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L798"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.RAdam.register_load_state_dict_post_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_load_state_dict_post_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/optimizer.py#L798"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.RAdam.register_load_state_dict_post_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register a load_state_dict post-hook which will be called after
 <a class="reference internal" href="torch.optim.Optimizer.load_state_dict.html#torch.optim.Optimizer.load_state_dict" title="torch.optim.Optimizer.load_state_dict"><code class="xref py py-meth docutils literal notranslate"><span class="pre">load_state_dict()</span></code></a> is called. It should have the
 following signature:</p>
@@ -801,7 +801,7 @@ <h1>RAdam<a class="headerlink" href="#radam" title="Permalink to this heading">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.RAdam.register_load_state_dict_pre_hook">
-<span class="sig-name descname"><span class="pre">register_load_state_dict_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L759"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.RAdam.register_load_state_dict_pre_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_load_state_dict_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/optimizer.py#L759"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.RAdam.register_load_state_dict_pre_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register a load_state_dict pre-hook which will be called before
 <a class="reference internal" href="torch.optim.Optimizer.load_state_dict.html#torch.optim.Optimizer.load_state_dict" title="torch.optim.Optimizer.load_state_dict"><code class="xref py py-meth docutils literal notranslate"><span class="pre">load_state_dict()</span></code></a> is called. It should have the
 following signature:</p>
@@ -838,7 +838,7 @@ <h1>RAdam<a class="headerlink" href="#radam" title="Permalink to this heading">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.RAdam.register_state_dict_post_hook">
-<span class="sig-name descname"><span class="pre">register_state_dict_post_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L601"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.RAdam.register_state_dict_post_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_state_dict_post_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/optimizer.py#L601"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.RAdam.register_state_dict_post_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register a state dict post-hook which will be called after <a class="reference internal" href="torch.optim.Optimizer.state_dict.html#torch.optim.Optimizer.state_dict" title="torch.optim.Optimizer.state_dict"><code class="xref py py-meth docutils literal notranslate"><span class="pre">state_dict()</span></code></a> is called.</p>
 <p>It should have the following signature:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">hook</span><span class="p">(</span><span class="n">optimizer</span><span class="p">,</span> <span class="n">state_dict</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">state_dict</span> <span class="ow">or</span> <span class="kc">None</span>
@@ -870,7 +870,7 @@ <h1>RAdam<a class="headerlink" href="#radam" title="Permalink to this heading">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.RAdam.register_state_dict_pre_hook">
-<span class="sig-name descname"><span class="pre">register_state_dict_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L569"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.RAdam.register_state_dict_pre_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_state_dict_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/optimizer.py#L569"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.RAdam.register_state_dict_pre_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register a state dict pre-hook which will be called before <a class="reference internal" href="torch.optim.Optimizer.state_dict.html#torch.optim.Optimizer.state_dict" title="torch.optim.Optimizer.state_dict"><code class="xref py py-meth docutils literal notranslate"><span class="pre">state_dict()</span></code></a> is called.</p>
 <p>It should have the following signature:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">hook</span><span class="p">(</span><span class="n">optimizer</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span>
@@ -902,7 +902,7 @@ <h1>RAdam<a class="headerlink" href="#radam" title="Permalink to this heading">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.RAdam.register_step_post_hook">
-<span class="sig-name descname"><span class="pre">register_step_post_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L548"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.RAdam.register_step_post_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_step_post_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/optimizer.py#L548"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.RAdam.register_step_post_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register an optimizer step post hook which will be called after optimizer step.</p>
 <p>It should have the following signature:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">hook</span><span class="p">(</span><span class="n">optimizer</span><span class="p">,</span> <span class="n">args</span><span class="p">,</span> <span class="n">kwargs</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span>
@@ -925,7 +925,7 @@ <h1>RAdam<a class="headerlink" href="#radam" title="Permalink to this heading">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.RAdam.register_step_pre_hook">
-<span class="sig-name descname"><span class="pre">register_step_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L525"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.RAdam.register_step_pre_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_step_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/optimizer.py#L525"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.RAdam.register_step_pre_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register an optimizer step pre hook which will be called before optimizer step.</p>
 <p>It should have the following signature:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">hook</span><span class="p">(</span><span class="n">optimizer</span><span class="p">,</span> <span class="n">args</span><span class="p">,</span> <span class="n">kwargs</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span> <span class="ow">or</span> <span class="n">modified</span> <span class="n">args</span> <span class="ow">and</span> <span class="n">kwargs</span>
@@ -950,7 +950,7 @@ <h1>RAdam<a class="headerlink" href="#radam" title="Permalink to this heading">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.RAdam.state_dict">
-<span class="sig-name descname"><span class="pre">state_dict</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L635"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.RAdam.state_dict" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">state_dict</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/optimizer.py#L635"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.RAdam.state_dict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the state of the optimizer as a <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.13)"><code class="xref py py-class docutils literal notranslate"><span class="pre">dict</span></code></a>.</p>
 <p>It contains two entries:</p>
 <ul class="simple">
@@ -1013,7 +1013,7 @@ <h1>RAdam<a class="headerlink" href="#radam" title="Permalink to this heading">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.RAdam.step">
-<span class="sig-name descname"><span class="pre">step</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">closure</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/radam.html#RAdam.step"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/radam.py#L125"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.RAdam.step" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">step</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">closure</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/radam.html#RAdam.step"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/radam.py#L125"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.RAdam.step" title="Permalink to this definition">¶</a></dt>
 <dd><p>Perform a single optimization step.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -1025,7 +1025,7 @@ <h1>RAdam<a class="headerlink" href="#radam" title="Permalink to this heading">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.RAdam.zero_grad">
-<span class="sig-name descname"><span class="pre">zero_grad</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">set_to_none</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L931"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.RAdam.zero_grad" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">zero_grad</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">set_to_none</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/optimizer.py#L931"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.RAdam.zero_grad" title="Permalink to this definition">¶</a></dt>
 <dd><p>Reset the gradients of all optimized <a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.Tensor</span></code></a> s.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.optim.RMSprop.html b/main/generated/torch.optim.RMSprop.html
index 041f6887a8f..4da8c20bad6 100644
--- a/main/generated/torch.optim.RMSprop.html
+++ b/main/generated/torch.optim.RMSprop.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>RMSprop<a class="headerlink" href="#rmsprop" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.optim.RMSprop">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.optim.</span></span><span class="sig-name descname"><span class="pre">RMSprop</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">params</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lr</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.01</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.99</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-08</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weight_decay</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">momentum</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">centered</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">capturable</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">foreach</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">maximize</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">differentiable</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/rmsprop.html#RMSprop"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/rmsprop.py#L28"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.RMSprop" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.optim.</span></span><span class="sig-name descname"><span class="pre">RMSprop</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">params</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lr</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.01</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.99</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-08</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weight_decay</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">momentum</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">centered</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">capturable</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">foreach</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">maximize</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">differentiable</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/rmsprop.html#RMSprop"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/rmsprop.py#L28"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.RMSprop" title="Permalink to this definition">¶</a></dt>
 <dd><p>Implements RMSprop algorithm.</p>
 <div class="math">
 <span class="katex-display"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML" display="block"><semantics><mtable rowspacing="0.25em" columnalign="right left" columnspacing="0em"><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mpadded height="0em" voffset="0em"><mspace mathbackground="black" width="31.298em" height="0.04em"></mspace></mpadded></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mtext mathvariant="bold">input</mtext><mo>:</mo><mi>α</mi><mtext> (alpha)</mtext><mo separator="true">,</mo><mtext> </mtext><mi>γ</mi><mtext> (lr)</mtext><mo separator="true">,</mo><mtext> </mtext><msub><mi>θ</mi><mn>0</mn></msub><mtext> (params)</mtext><mo separator="true">,</mo><mtext> </mtext><mi>f</mi><mo stretchy="false">(</mo><mi>θ</mi><mo stretchy="false">)</mo><mtext> (objective)</mtext></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="3.6989em"/><mi>λ</mi><mtext> (weight decay)</mtext><mo separator="true">,</mo><mtext> </mtext><mi>μ</mi><mtext> (momentum)</mtext><mo separator="true">,</mo><mtext> </mtext><mi>c</mi><mi>e</mi><mi>n</mi><mi>t</mi><mi>e</mi><mi>r</mi><mi>e</mi><mi>d</mi><mo separator="true">,</mo><mtext> </mtext><mi>ϵ</mi><mtext> (epsilon)</mtext></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mtext mathvariant="bold">initialize</mtext><mo>:</mo><msub><mi>v</mi><mn>0</mn></msub><mo>←</mo><mn>0</mn><mtext> (square average)</mtext><mo separator="true">,</mo><mtext> </mtext><msub><mtext mathvariant="bold">b</mtext><mn>0</mn></msub><mo>←</mo><mn>0</mn><mtext> (buffer)</mtext><mo separator="true">,</mo><mtext> </mtext><msubsup><mi>g</mi><mn>0</mn><mrow><mi>a</mi><mi>v</mi><mi>e</mi></mrow></msubsup><mo>←</mo><mn>0</mn></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mpadded height="0em" voffset="0em"><mspace mathbackground="black" width="31.298em" height="0.04em"></mspace></mpadded></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mtext mathvariant="bold">for</mtext><mtext> </mtext><mi>t</mi><mo>=</mo><mn>1</mn><mtext> </mtext><mtext mathvariant="bold">to</mtext><mtext> </mtext><mo>…</mo><mtext> </mtext><mtext mathvariant="bold">do</mtext></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="1.4226em"/><msub><mi>g</mi><mi>t</mi></msub><mo>←</mo><msub><mi mathvariant="normal">∇</mi><mi>θ</mi></msub><msub><mi>f</mi><mi>t</mi></msub><mo stretchy="false">(</mo><msub><mi>θ</mi><mrow><mi>t</mi><mo>−</mo><mn>1</mn></mrow></msub><mo stretchy="false">)</mo></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="1.4226em"/><mi>i</mi><mi>f</mi><mtext> </mtext><mi>λ</mi><mo mathvariant="normal">≠</mo><mn>0</mn></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="2.8453em"/><msub><mi>g</mi><mi>t</mi></msub><mo>←</mo><msub><mi>g</mi><mi>t</mi></msub><mo>+</mo><mi>λ</mi><msub><mi>θ</mi><mrow><mi>t</mi><mo>−</mo><mn>1</mn></mrow></msub></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="1.4226em"/><msub><mi>v</mi><mi>t</mi></msub><mo>←</mo><mi>α</mi><msub><mi>v</mi><mrow><mi>t</mi><mo>−</mo><mn>1</mn></mrow></msub><mo>+</mo><mo stretchy="false">(</mo><mn>1</mn><mo>−</mo><mi>α</mi><mo stretchy="false">)</mo><msubsup><mi>g</mi><mi>t</mi><mn>2</mn></msubsup><mspace width="2.2762em"/></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="1.4226em"/><mover accent="true"><msub><mi>v</mi><mi>t</mi></msub><mo>~</mo></mover><mo>←</mo><msub><mi>v</mi><mi>t</mi></msub></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="1.4226em"/><mi>i</mi><mi>f</mi><mtext> </mtext><mi>c</mi><mi>e</mi><mi>n</mi><mi>t</mi><mi>e</mi><mi>r</mi><mi>e</mi><mi>d</mi></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="2.8453em"/><msubsup><mi>g</mi><mi>t</mi><mrow><mi>a</mi><mi>v</mi><mi>e</mi></mrow></msubsup><mo>←</mo><msubsup><mi>g</mi><mrow><mi>t</mi><mo>−</mo><mn>1</mn></mrow><mrow><mi>a</mi><mi>v</mi><mi>e</mi></mrow></msubsup><mi>α</mi><mo>+</mo><mo stretchy="false">(</mo><mn>1</mn><mo>−</mo><mi>α</mi><mo stretchy="false">)</mo><msub><mi>g</mi><mi>t</mi></msub></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="2.8453em"/><mover accent="true"><msub><mi>v</mi><mi>t</mi></msub><mo>~</mo></mover><mo>←</mo><mover accent="true"><msub><mi>v</mi><mi>t</mi></msub><mo>~</mo></mover><mo>−</mo><mo fence="false" stretchy="true" minsize="1.2em" maxsize="1.2em">(</mo><msubsup><mi>g</mi><mi>t</mi><mrow><mi>a</mi><mi>v</mi><mi>e</mi></mrow></msubsup><msup><mo fence="false" stretchy="true" minsize="1.2em" maxsize="1.2em">)</mo><mn>2</mn></msup></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="1.4226em"/><mi>i</mi><mi>f</mi><mtext> </mtext><mi>μ</mi><mo>&gt;</mo><mn>0</mn></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="2.8453em"/><msub><mtext mathvariant="bold">b</mtext><mi>t</mi></msub><mo>←</mo><mi>μ</mi><msub><mtext mathvariant="bold">b</mtext><mrow><mi>t</mi><mo>−</mo><mn>1</mn></mrow></msub><mo>+</mo><msub><mi>g</mi><mi>t</mi></msub><mi mathvariant="normal">/</mi><mo fence="false" stretchy="true" minsize="1.2em" maxsize="1.2em">(</mo><msqrt><mover accent="true"><msub><mi>v</mi><mi>t</mi></msub><mo>~</mo></mover></msqrt><mo>+</mo><mi>ϵ</mi><mo fence="false" stretchy="true" minsize="1.2em" maxsize="1.2em">)</mo></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="2.8453em"/><msub><mi>θ</mi><mi>t</mi></msub><mo>←</mo><msub><mi>θ</mi><mrow><mi>t</mi><mo>−</mo><mn>1</mn></mrow></msub><mo>−</mo><mi>γ</mi><msub><mtext mathvariant="bold">b</mtext><mi>t</mi></msub></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="1.4226em"/><mi>e</mi><mi>l</mi><mi>s</mi><mi>e</mi></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="2.8453em"/><msub><mi>θ</mi><mi>t</mi></msub><mo>←</mo><msub><mi>θ</mi><mrow><mi>t</mi><mo>−</mo><mn>1</mn></mrow></msub><mo>−</mo><mi>γ</mi><msub><mi>g</mi><mi>t</mi></msub><mi mathvariant="normal">/</mi><mo fence="false" stretchy="true" minsize="1.2em" maxsize="1.2em">(</mo><msqrt><mover accent="true"><msub><mi>v</mi><mi>t</mi></msub><mo>~</mo></mover></msqrt><mo>+</mo><mi>ϵ</mi><mo fence="false" stretchy="true" minsize="1.2em" maxsize="1.2em">)</mo><mspace width="0.8536em"/></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mpadded height="0em" voffset="0em"><mspace mathbackground="black" width="31.298em" height="0.04em"></mspace></mpadded></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mrow><mrow><mi mathvariant="bold">r</mi><mi mathvariant="bold">e</mi><mi mathvariant="bold">t</mi><mi mathvariant="bold">u</mi><mi mathvariant="bold">r</mi><mi mathvariant="bold">n</mi></mrow><mtext> </mtext><msub><mi mathvariant="bold">θ</mi><mi mathvariant="bold">t</mi></msub></mrow></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mpadded height="0em" voffset="0em"><mspace mathbackground="black" width="31.298em" height="0.04em"></mspace></mpadded></mrow></mstyle></mtd></mtr></mtable><annotation encoding="application/x-tex">\begin{aligned}
@@ -723,7 +723,7 @@ <h1>RMSprop<a class="headerlink" href="#rmsprop" title="Permalink to this headin
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.RMSprop.add_param_group">
-<span class="sig-name descname"><span class="pre">add_param_group</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">param_group</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L1003"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.RMSprop.add_param_group" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">add_param_group</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">param_group</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/optimizer.py#L1003"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.RMSprop.add_param_group" title="Permalink to this definition">¶</a></dt>
 <dd><p>Add a param group to the <a class="reference internal" href="../optim.html#torch.optim.Optimizer" title="torch.optim.Optimizer"><code class="xref py py-class docutils literal notranslate"><span class="pre">Optimizer</span></code></a> s <cite>param_groups</cite>.</p>
 <p>This can be useful when fine tuning a pre-trained network as frozen layers can be made
 trainable and added to the <a class="reference internal" href="../optim.html#torch.optim.Optimizer" title="torch.optim.Optimizer"><code class="xref py py-class docutils literal notranslate"><span class="pre">Optimizer</span></code></a> as training progresses.</p>
@@ -737,7 +737,7 @@ <h1>RMSprop<a class="headerlink" href="#rmsprop" title="Permalink to this headin
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.RMSprop.load_state_dict">
-<span class="sig-name descname"><span class="pre">load_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state_dict</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L832"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.RMSprop.load_state_dict" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">load_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state_dict</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/optimizer.py#L832"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.RMSprop.load_state_dict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Load the optimizer state.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -761,7 +761,7 @@ <h1>RMSprop<a class="headerlink" href="#rmsprop" title="Permalink to this headin
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.RMSprop.register_load_state_dict_post_hook">
-<span class="sig-name descname"><span class="pre">register_load_state_dict_post_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L798"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.RMSprop.register_load_state_dict_post_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_load_state_dict_post_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/optimizer.py#L798"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.RMSprop.register_load_state_dict_post_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register a load_state_dict post-hook which will be called after
 <a class="reference internal" href="torch.optim.Optimizer.load_state_dict.html#torch.optim.Optimizer.load_state_dict" title="torch.optim.Optimizer.load_state_dict"><code class="xref py py-meth docutils literal notranslate"><span class="pre">load_state_dict()</span></code></a> is called. It should have the
 following signature:</p>
@@ -795,7 +795,7 @@ <h1>RMSprop<a class="headerlink" href="#rmsprop" title="Permalink to this headin
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.RMSprop.register_load_state_dict_pre_hook">
-<span class="sig-name descname"><span class="pre">register_load_state_dict_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L759"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.RMSprop.register_load_state_dict_pre_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_load_state_dict_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/optimizer.py#L759"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.RMSprop.register_load_state_dict_pre_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register a load_state_dict pre-hook which will be called before
 <a class="reference internal" href="torch.optim.Optimizer.load_state_dict.html#torch.optim.Optimizer.load_state_dict" title="torch.optim.Optimizer.load_state_dict"><code class="xref py py-meth docutils literal notranslate"><span class="pre">load_state_dict()</span></code></a> is called. It should have the
 following signature:</p>
@@ -832,7 +832,7 @@ <h1>RMSprop<a class="headerlink" href="#rmsprop" title="Permalink to this headin
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.RMSprop.register_state_dict_post_hook">
-<span class="sig-name descname"><span class="pre">register_state_dict_post_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L601"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.RMSprop.register_state_dict_post_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_state_dict_post_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/optimizer.py#L601"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.RMSprop.register_state_dict_post_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register a state dict post-hook which will be called after <a class="reference internal" href="torch.optim.Optimizer.state_dict.html#torch.optim.Optimizer.state_dict" title="torch.optim.Optimizer.state_dict"><code class="xref py py-meth docutils literal notranslate"><span class="pre">state_dict()</span></code></a> is called.</p>
 <p>It should have the following signature:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">hook</span><span class="p">(</span><span class="n">optimizer</span><span class="p">,</span> <span class="n">state_dict</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">state_dict</span> <span class="ow">or</span> <span class="kc">None</span>
@@ -864,7 +864,7 @@ <h1>RMSprop<a class="headerlink" href="#rmsprop" title="Permalink to this headin
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.RMSprop.register_state_dict_pre_hook">
-<span class="sig-name descname"><span class="pre">register_state_dict_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L569"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.RMSprop.register_state_dict_pre_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_state_dict_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/optimizer.py#L569"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.RMSprop.register_state_dict_pre_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register a state dict pre-hook which will be called before <a class="reference internal" href="torch.optim.Optimizer.state_dict.html#torch.optim.Optimizer.state_dict" title="torch.optim.Optimizer.state_dict"><code class="xref py py-meth docutils literal notranslate"><span class="pre">state_dict()</span></code></a> is called.</p>
 <p>It should have the following signature:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">hook</span><span class="p">(</span><span class="n">optimizer</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span>
@@ -896,7 +896,7 @@ <h1>RMSprop<a class="headerlink" href="#rmsprop" title="Permalink to this headin
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.RMSprop.register_step_post_hook">
-<span class="sig-name descname"><span class="pre">register_step_post_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L548"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.RMSprop.register_step_post_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_step_post_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/optimizer.py#L548"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.RMSprop.register_step_post_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register an optimizer step post hook which will be called after optimizer step.</p>
 <p>It should have the following signature:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">hook</span><span class="p">(</span><span class="n">optimizer</span><span class="p">,</span> <span class="n">args</span><span class="p">,</span> <span class="n">kwargs</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span>
@@ -919,7 +919,7 @@ <h1>RMSprop<a class="headerlink" href="#rmsprop" title="Permalink to this headin
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.RMSprop.register_step_pre_hook">
-<span class="sig-name descname"><span class="pre">register_step_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L525"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.RMSprop.register_step_pre_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_step_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/optimizer.py#L525"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.RMSprop.register_step_pre_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register an optimizer step pre hook which will be called before optimizer step.</p>
 <p>It should have the following signature:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">hook</span><span class="p">(</span><span class="n">optimizer</span><span class="p">,</span> <span class="n">args</span><span class="p">,</span> <span class="n">kwargs</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span> <span class="ow">or</span> <span class="n">modified</span> <span class="n">args</span> <span class="ow">and</span> <span class="n">kwargs</span>
@@ -944,7 +944,7 @@ <h1>RMSprop<a class="headerlink" href="#rmsprop" title="Permalink to this headin
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.RMSprop.state_dict">
-<span class="sig-name descname"><span class="pre">state_dict</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L635"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.RMSprop.state_dict" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">state_dict</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/optimizer.py#L635"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.RMSprop.state_dict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the state of the optimizer as a <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.13)"><code class="xref py py-class docutils literal notranslate"><span class="pre">dict</span></code></a>.</p>
 <p>It contains two entries:</p>
 <ul class="simple">
@@ -1007,7 +1007,7 @@ <h1>RMSprop<a class="headerlink" href="#rmsprop" title="Permalink to this headin
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.RMSprop.step">
-<span class="sig-name descname"><span class="pre">step</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">closure</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/rmsprop.html#RMSprop.step"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/rmsprop.py#L142"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.RMSprop.step" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">step</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">closure</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/rmsprop.html#RMSprop.step"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/rmsprop.py#L142"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.RMSprop.step" title="Permalink to this definition">¶</a></dt>
 <dd><p>Perform a single optimization step.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -1019,7 +1019,7 @@ <h1>RMSprop<a class="headerlink" href="#rmsprop" title="Permalink to this headin
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.RMSprop.zero_grad">
-<span class="sig-name descname"><span class="pre">zero_grad</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">set_to_none</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L931"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.RMSprop.zero_grad" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">zero_grad</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">set_to_none</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/optimizer.py#L931"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.RMSprop.zero_grad" title="Permalink to this definition">¶</a></dt>
 <dd><p>Reset the gradients of all optimized <a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.Tensor</span></code></a> s.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.optim.Rprop.html b/main/generated/torch.optim.Rprop.html
index 7368cc33b5a..f00674856a4 100644
--- a/main/generated/torch.optim.Rprop.html
+++ b/main/generated/torch.optim.Rprop.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>Rprop<a class="headerlink" href="#rprop" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.optim.Rprop">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.optim.</span></span><span class="sig-name descname"><span class="pre">Rprop</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">params</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lr</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.01</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">etas</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">(0.5,</span> <span class="pre">1.2)</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">step_sizes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">(1e-06,</span> <span class="pre">50)</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">capturable</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">foreach</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">maximize</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">differentiable</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/rprop.html#Rprop"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/rprop.py#L28"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Rprop" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.optim.</span></span><span class="sig-name descname"><span class="pre">Rprop</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">params</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lr</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.01</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">etas</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">(0.5,</span> <span class="pre">1.2)</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">step_sizes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">(1e-06,</span> <span class="pre">50)</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">capturable</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">foreach</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">maximize</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">differentiable</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/rprop.html#Rprop"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/rprop.py#L28"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Rprop" title="Permalink to this definition">¶</a></dt>
 <dd><p>Implements the resilient backpropagation algorithm.</p>
 <div class="math">
 <span class="katex-display"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML" display="block"><semantics><mtable rowspacing="0.25em" columnalign="right left" columnspacing="0em"><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mpadded height="0em" voffset="0em"><mspace mathbackground="black" width="31.298em" height="0.04em"></mspace></mpadded></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mtext mathvariant="bold">input</mtext><mo>:</mo><msub><mi>θ</mi><mn>0</mn></msub><mo>∈</mo><msup><mi mathvariant="bold">R</mi><mi>d</mi></msup><mtext> (params)</mtext><mo separator="true">,</mo><mi>f</mi><mo stretchy="false">(</mo><mi>θ</mi><mo stretchy="false">)</mo><mtext> (objective)</mtext><mo separator="true">,</mo></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="3.6989em"/><msub><mi>η</mi><mrow><mo>+</mo><mi mathvariant="normal">/</mi><mo>−</mo></mrow></msub><mtext> (etaplus, etaminus)</mtext><mo separator="true">,</mo><msub><mi mathvariant="normal">Γ</mi><mrow><mi>m</mi><mi>a</mi><mi>x</mi><mi mathvariant="normal">/</mi><mi>m</mi><mi>i</mi><mi>n</mi></mrow></msub><mtext> (step sizes)</mtext></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mtext mathvariant="bold">initialize</mtext><mo>:</mo><msubsup><mi>g</mi><mrow><mi>p</mi><mi>r</mi><mi>e</mi><mi>v</mi></mrow><mn>0</mn></msubsup><mo>←</mo><mn>0</mn><mo separator="true">,</mo><mtext> </mtext><msub><mi>η</mi><mn>0</mn></msub><mo>←</mo><mtext>lr (learning rate)</mtext></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mpadded height="0em" voffset="0em"><mspace mathbackground="black" width="31.298em" height="0.04em"></mspace></mpadded></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mtext mathvariant="bold">for</mtext><mtext> </mtext><mi>t</mi><mo>=</mo><mn>1</mn><mtext> </mtext><mtext mathvariant="bold">to</mtext><mtext> </mtext><mo>…</mo><mtext> </mtext><mtext mathvariant="bold">do</mtext></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="1.4226em"/><msub><mi>g</mi><mi>t</mi></msub><mo>←</mo><msub><mi mathvariant="normal">∇</mi><mi>θ</mi></msub><msub><mi>f</mi><mi>t</mi></msub><mo stretchy="false">(</mo><msub><mi>θ</mi><mrow><mi>t</mi><mo>−</mo><mn>1</mn></mrow></msub><mo stretchy="false">)</mo></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="1.4226em"/><mtext mathvariant="bold">for</mtext><mtext> </mtext><mi>i</mi><mo>=</mo><mn>0</mn><mo separator="true">,</mo><mn>1</mn><mo separator="true">,</mo><mo>…</mo><mo separator="true">,</mo><mi>d</mi><mo>−</mo><mn>1</mn><mtext> </mtext><mrow><mi mathvariant="bold">d</mi><mi mathvariant="bold">o</mi></mrow></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="2.8453em"/><mtext mathvariant="bold">if</mtext><mtext> </mtext><msubsup><mi>g</mi><mrow><mi>p</mi><mi>r</mi><mi>e</mi><mi>v</mi></mrow><mi>i</mi></msubsup><msubsup><mi>g</mi><mi>t</mi><mi>i</mi></msubsup><mo>&gt;</mo><mn>0</mn></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="4.2679em"/><msubsup><mi>η</mi><mi>t</mi><mi>i</mi></msubsup><mo>←</mo><mrow><mi mathvariant="normal">m</mi><mi mathvariant="normal">i</mi><mi mathvariant="normal">n</mi></mrow><mo stretchy="false">(</mo><msubsup><mi>η</mi><mrow><mi>t</mi><mo>−</mo><mn>1</mn></mrow><mi>i</mi></msubsup><msub><mi>η</mi><mo lspace="0em" rspace="0em">+</mo></msub><mo separator="true">,</mo><msub><mi mathvariant="normal">Γ</mi><mrow><mi>m</mi><mi>a</mi><mi>x</mi></mrow></msub><mo stretchy="false">)</mo></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="2.8453em"/><mrow><mtext mathvariant="bold">else</mtext><mtext> </mtext><mtext mathvariant="bold">if</mtext></mrow><mtext> </mtext><msubsup><mi>g</mi><mrow><mi>p</mi><mi>r</mi><mi>e</mi><mi>v</mi></mrow><mi>i</mi></msubsup><msubsup><mi>g</mi><mi>t</mi><mi>i</mi></msubsup><mo>&lt;</mo><mn>0</mn></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="4.2679em"/><msubsup><mi>η</mi><mi>t</mi><mi>i</mi></msubsup><mo>←</mo><mrow><mi mathvariant="normal">m</mi><mi mathvariant="normal">a</mi><mi mathvariant="normal">x</mi></mrow><mo stretchy="false">(</mo><msubsup><mi>η</mi><mrow><mi>t</mi><mo>−</mo><mn>1</mn></mrow><mi>i</mi></msubsup><msub><mi>η</mi><mo lspace="0em" rspace="0em">−</mo></msub><mo separator="true">,</mo><msub><mi mathvariant="normal">Γ</mi><mrow><mi>m</mi><mi>i</mi><mi>n</mi></mrow></msub><mo stretchy="false">)</mo></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="4.2679em"/><msubsup><mi>g</mi><mi>t</mi><mi>i</mi></msubsup><mo>←</mo><mn>0</mn></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="2.8453em"/><mtext mathvariant="bold">else</mtext><mtext> </mtext></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="4.2679em"/><msubsup><mi>η</mi><mi>t</mi><mi>i</mi></msubsup><mo>←</mo><msubsup><mi>η</mi><mrow><mi>t</mi><mo>−</mo><mn>1</mn></mrow><mi>i</mi></msubsup></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="1.4226em"/><msub><mi>θ</mi><mi>t</mi></msub><mo>←</mo><msub><mi>θ</mi><mrow><mi>t</mi><mo>−</mo><mn>1</mn></mrow></msub><mo>−</mo><msub><mi>η</mi><mi>t</mi></msub><mrow><mi mathvariant="normal">s</mi><mi mathvariant="normal">i</mi><mi mathvariant="normal">g</mi><mi mathvariant="normal">n</mi></mrow><mo stretchy="false">(</mo><msub><mi>g</mi><mi>t</mi></msub><mo stretchy="false">)</mo></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="1.4226em"/><msub><mi>g</mi><mrow><mi>p</mi><mi>r</mi><mi>e</mi><mi>v</mi></mrow></msub><mo>←</mo><msub><mi>g</mi><mi>t</mi></msub></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mpadded height="0em" voffset="0em"><mspace mathbackground="black" width="31.298em" height="0.04em"></mspace></mpadded></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mrow><mrow><mi mathvariant="bold">r</mi><mi mathvariant="bold">e</mi><mi mathvariant="bold">t</mi><mi mathvariant="bold">u</mi><mi mathvariant="bold">r</mi><mi mathvariant="bold">n</mi></mrow><mtext> </mtext><msub><mi mathvariant="bold">θ</mi><mi mathvariant="bold">t</mi></msub></mrow></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mpadded height="0em" voffset="0em"><mspace mathbackground="black" width="31.298em" height="0.04em"></mspace></mpadded></mrow></mstyle></mtd></mtr></mtable><annotation encoding="application/x-tex">\begin{aligned}
@@ -680,7 +680,7 @@ <h1>Rprop<a class="headerlink" href="#rprop" title="Permalink to this heading">
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.Rprop.add_param_group">
-<span class="sig-name descname"><span class="pre">add_param_group</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">param_group</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L1003"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Rprop.add_param_group" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">add_param_group</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">param_group</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/optimizer.py#L1003"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Rprop.add_param_group" title="Permalink to this definition">¶</a></dt>
 <dd><p>Add a param group to the <a class="reference internal" href="../optim.html#torch.optim.Optimizer" title="torch.optim.Optimizer"><code class="xref py py-class docutils literal notranslate"><span class="pre">Optimizer</span></code></a> s <cite>param_groups</cite>.</p>
 <p>This can be useful when fine tuning a pre-trained network as frozen layers can be made
 trainable and added to the <a class="reference internal" href="../optim.html#torch.optim.Optimizer" title="torch.optim.Optimizer"><code class="xref py py-class docutils literal notranslate"><span class="pre">Optimizer</span></code></a> as training progresses.</p>
@@ -694,7 +694,7 @@ <h1>Rprop<a class="headerlink" href="#rprop" title="Permalink to this heading">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.Rprop.load_state_dict">
-<span class="sig-name descname"><span class="pre">load_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state_dict</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L832"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Rprop.load_state_dict" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">load_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state_dict</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/optimizer.py#L832"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Rprop.load_state_dict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Load the optimizer state.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -718,7 +718,7 @@ <h1>Rprop<a class="headerlink" href="#rprop" title="Permalink to this heading">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.Rprop.register_load_state_dict_post_hook">
-<span class="sig-name descname"><span class="pre">register_load_state_dict_post_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L798"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Rprop.register_load_state_dict_post_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_load_state_dict_post_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/optimizer.py#L798"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Rprop.register_load_state_dict_post_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register a load_state_dict post-hook which will be called after
 <a class="reference internal" href="torch.optim.Optimizer.load_state_dict.html#torch.optim.Optimizer.load_state_dict" title="torch.optim.Optimizer.load_state_dict"><code class="xref py py-meth docutils literal notranslate"><span class="pre">load_state_dict()</span></code></a> is called. It should have the
 following signature:</p>
@@ -752,7 +752,7 @@ <h1>Rprop<a class="headerlink" href="#rprop" title="Permalink to this heading">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.Rprop.register_load_state_dict_pre_hook">
-<span class="sig-name descname"><span class="pre">register_load_state_dict_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L759"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Rprop.register_load_state_dict_pre_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_load_state_dict_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/optimizer.py#L759"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Rprop.register_load_state_dict_pre_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register a load_state_dict pre-hook which will be called before
 <a class="reference internal" href="torch.optim.Optimizer.load_state_dict.html#torch.optim.Optimizer.load_state_dict" title="torch.optim.Optimizer.load_state_dict"><code class="xref py py-meth docutils literal notranslate"><span class="pre">load_state_dict()</span></code></a> is called. It should have the
 following signature:</p>
@@ -789,7 +789,7 @@ <h1>Rprop<a class="headerlink" href="#rprop" title="Permalink to this heading">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.Rprop.register_state_dict_post_hook">
-<span class="sig-name descname"><span class="pre">register_state_dict_post_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L601"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Rprop.register_state_dict_post_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_state_dict_post_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/optimizer.py#L601"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Rprop.register_state_dict_post_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register a state dict post-hook which will be called after <a class="reference internal" href="torch.optim.Optimizer.state_dict.html#torch.optim.Optimizer.state_dict" title="torch.optim.Optimizer.state_dict"><code class="xref py py-meth docutils literal notranslate"><span class="pre">state_dict()</span></code></a> is called.</p>
 <p>It should have the following signature:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">hook</span><span class="p">(</span><span class="n">optimizer</span><span class="p">,</span> <span class="n">state_dict</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">state_dict</span> <span class="ow">or</span> <span class="kc">None</span>
@@ -821,7 +821,7 @@ <h1>Rprop<a class="headerlink" href="#rprop" title="Permalink to this heading">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.Rprop.register_state_dict_pre_hook">
-<span class="sig-name descname"><span class="pre">register_state_dict_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L569"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Rprop.register_state_dict_pre_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_state_dict_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/optimizer.py#L569"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Rprop.register_state_dict_pre_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register a state dict pre-hook which will be called before <a class="reference internal" href="torch.optim.Optimizer.state_dict.html#torch.optim.Optimizer.state_dict" title="torch.optim.Optimizer.state_dict"><code class="xref py py-meth docutils literal notranslate"><span class="pre">state_dict()</span></code></a> is called.</p>
 <p>It should have the following signature:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">hook</span><span class="p">(</span><span class="n">optimizer</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span>
@@ -853,7 +853,7 @@ <h1>Rprop<a class="headerlink" href="#rprop" title="Permalink to this heading">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.Rprop.register_step_post_hook">
-<span class="sig-name descname"><span class="pre">register_step_post_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L548"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Rprop.register_step_post_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_step_post_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/optimizer.py#L548"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Rprop.register_step_post_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register an optimizer step post hook which will be called after optimizer step.</p>
 <p>It should have the following signature:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">hook</span><span class="p">(</span><span class="n">optimizer</span><span class="p">,</span> <span class="n">args</span><span class="p">,</span> <span class="n">kwargs</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span>
@@ -876,7 +876,7 @@ <h1>Rprop<a class="headerlink" href="#rprop" title="Permalink to this heading">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.Rprop.register_step_pre_hook">
-<span class="sig-name descname"><span class="pre">register_step_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L525"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Rprop.register_step_pre_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_step_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/optimizer.py#L525"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Rprop.register_step_pre_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register an optimizer step pre hook which will be called before optimizer step.</p>
 <p>It should have the following signature:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">hook</span><span class="p">(</span><span class="n">optimizer</span><span class="p">,</span> <span class="n">args</span><span class="p">,</span> <span class="n">kwargs</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span> <span class="ow">or</span> <span class="n">modified</span> <span class="n">args</span> <span class="ow">and</span> <span class="n">kwargs</span>
@@ -901,7 +901,7 @@ <h1>Rprop<a class="headerlink" href="#rprop" title="Permalink to this heading">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.Rprop.state_dict">
-<span class="sig-name descname"><span class="pre">state_dict</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L635"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Rprop.state_dict" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">state_dict</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/optimizer.py#L635"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Rprop.state_dict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the state of the optimizer as a <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.13)"><code class="xref py py-class docutils literal notranslate"><span class="pre">dict</span></code></a>.</p>
 <p>It contains two entries:</p>
 <ul class="simple">
@@ -964,7 +964,7 @@ <h1>Rprop<a class="headerlink" href="#rprop" title="Permalink to this heading">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.Rprop.step">
-<span class="sig-name descname"><span class="pre">step</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">closure</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/rprop.html#Rprop.step"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/rprop.py#L116"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Rprop.step" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">step</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">closure</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/rprop.html#Rprop.step"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/rprop.py#L116"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Rprop.step" title="Permalink to this definition">¶</a></dt>
 <dd><p>Perform a single optimization step.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -976,7 +976,7 @@ <h1>Rprop<a class="headerlink" href="#rprop" title="Permalink to this heading">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.Rprop.zero_grad">
-<span class="sig-name descname"><span class="pre">zero_grad</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">set_to_none</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L931"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Rprop.zero_grad" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">zero_grad</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">set_to_none</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/optimizer.py#L931"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Rprop.zero_grad" title="Permalink to this definition">¶</a></dt>
 <dd><p>Reset the gradients of all optimized <a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.Tensor</span></code></a> s.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.optim.SGD.html b/main/generated/torch.optim.SGD.html
index 6c27676f3e7..bacfe2047e7 100644
--- a/main/generated/torch.optim.SGD.html
+++ b/main/generated/torch.optim.SGD.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>SGD<a class="headerlink" href="#sgd" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.optim.SGD">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.optim.</span></span><span class="sig-name descname"><span class="pre">SGD</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">params</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lr</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">momentum</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dampening</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weight_decay</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">nesterov</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">maximize</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">foreach</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">differentiable</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fused</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/sgd.html#SGD"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/sgd.py#L26"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.SGD" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.optim.</span></span><span class="sig-name descname"><span class="pre">SGD</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">params</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lr</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">momentum</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dampening</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weight_decay</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">nesterov</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">maximize</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">foreach</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">differentiable</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fused</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/sgd.html#SGD"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/sgd.py#L26"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.SGD" title="Permalink to this definition">¶</a></dt>
 <dd><p>Implements stochastic gradient descent (optionally with momentum).</p>
 <div class="math">
 <span class="katex-display"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML" display="block"><semantics><mtable rowspacing="0.25em" columnalign="right left" columnspacing="0em"><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mpadded height="0em" voffset="0em"><mspace mathbackground="black" width="31.298em" height="0.04em"></mspace></mpadded></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mtext mathvariant="bold">input</mtext><mo>:</mo><mi>γ</mi><mtext> (lr)</mtext><mo separator="true">,</mo><mtext> </mtext><msub><mi>θ</mi><mn>0</mn></msub><mtext> (params)</mtext><mo separator="true">,</mo><mtext> </mtext><mi>f</mi><mo stretchy="false">(</mo><mi>θ</mi><mo stretchy="false">)</mo><mtext> (objective)</mtext><mo separator="true">,</mo><mtext> </mtext><mi>λ</mi><mtext> (weight decay)</mtext><mo separator="true">,</mo></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="3.6989em"/><mtext> </mtext><mi>μ</mi><mtext> (momentum)</mtext><mo separator="true">,</mo><mtext> </mtext><mi>τ</mi><mtext> (dampening)</mtext><mo separator="true">,</mo><mtext> </mtext><mrow><mtext> </mtext><mtext mathvariant="italic">nesterov,</mtext></mrow><mtext> </mtext><mrow><mtext> </mtext><mtext mathvariant="italic">maximize</mtext></mrow></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mpadded height="0em" voffset="0em"><mspace mathbackground="black" width="31.298em" height="0.04em"></mspace></mpadded></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mtext mathvariant="bold">for</mtext><mtext> </mtext><mi>t</mi><mo>=</mo><mn>1</mn><mtext> </mtext><mtext mathvariant="bold">to</mtext><mtext> </mtext><mo>…</mo><mtext> </mtext><mtext mathvariant="bold">do</mtext></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="1.4226em"/><msub><mi>g</mi><mi>t</mi></msub><mo>←</mo><msub><mi mathvariant="normal">∇</mi><mi>θ</mi></msub><msub><mi>f</mi><mi>t</mi></msub><mo stretchy="false">(</mo><msub><mi>θ</mi><mrow><mi>t</mi><mo>−</mo><mn>1</mn></mrow></msub><mo stretchy="false">)</mo></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="1.4226em"/><mtext mathvariant="bold">if</mtext><mtext> </mtext><mi>λ</mi><mo mathvariant="normal">≠</mo><mn>0</mn></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="2.8453em"/><msub><mi>g</mi><mi>t</mi></msub><mo>←</mo><msub><mi>g</mi><mi>t</mi></msub><mo>+</mo><mi>λ</mi><msub><mi>θ</mi><mrow><mi>t</mi><mo>−</mo><mn>1</mn></mrow></msub></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="1.4226em"/><mtext mathvariant="bold">if</mtext><mtext> </mtext><mi>μ</mi><mo mathvariant="normal">≠</mo><mn>0</mn></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="2.8453em"/><mtext mathvariant="bold">if</mtext><mtext> </mtext><mi>t</mi><mo>&gt;</mo><mn>1</mn></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="4.2679em"/><msub><mtext mathvariant="bold">b</mtext><mi>t</mi></msub><mo>←</mo><mi>μ</mi><msub><mtext mathvariant="bold">b</mtext><mrow><mi>t</mi><mo>−</mo><mn>1</mn></mrow></msub><mo>+</mo><mo stretchy="false">(</mo><mn>1</mn><mo>−</mo><mi>τ</mi><mo stretchy="false">)</mo><msub><mi>g</mi><mi>t</mi></msub></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="2.8453em"/><mtext mathvariant="bold">else</mtext></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="4.2679em"/><msub><mtext mathvariant="bold">b</mtext><mi>t</mi></msub><mo>←</mo><msub><mi>g</mi><mi>t</mi></msub></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="2.8453em"/><mtext mathvariant="bold">if</mtext><mtext> </mtext><mtext mathvariant="italic">nesterov</mtext></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="4.2679em"/><msub><mi>g</mi><mi>t</mi></msub><mo>←</mo><msub><mi>g</mi><mi>t</mi></msub><mo>+</mo><mi>μ</mi><msub><mtext mathvariant="bold">b</mtext><mi>t</mi></msub></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="2.8453em"/><mtext mathvariant="bold">else</mtext></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="4.2679em"/><msub><mi>g</mi><mi>t</mi></msub><mo>←</mo><msub><mtext mathvariant="bold">b</mtext><mi>t</mi></msub></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="1.4226em"/><mtext mathvariant="bold">if</mtext><mtext> </mtext><mtext mathvariant="italic">maximize</mtext></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="2.8453em"/><msub><mi>θ</mi><mi>t</mi></msub><mo>←</mo><msub><mi>θ</mi><mrow><mi>t</mi><mo>−</mo><mn>1</mn></mrow></msub><mo>+</mo><mi>γ</mi><msub><mi>g</mi><mi>t</mi></msub></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="1.4226em"/><mtext mathvariant="bold">else</mtext></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mspace width="2.8453em"/><msub><mi>θ</mi><mi>t</mi></msub><mo>←</mo><msub><mi>θ</mi><mrow><mi>t</mi><mo>−</mo><mn>1</mn></mrow></msub><mo>−</mo><mi>γ</mi><msub><mi>g</mi><mi>t</mi></msub></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mpadded height="0em" voffset="0em"><mspace mathbackground="black" width="31.298em" height="0.04em"></mspace></mpadded></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mrow><mrow><mi mathvariant="bold">r</mi><mi mathvariant="bold">e</mi><mi mathvariant="bold">t</mi><mi mathvariant="bold">u</mi><mi mathvariant="bold">r</mi><mi mathvariant="bold">n</mi></mrow><mtext> </mtext><msub><mi mathvariant="bold">θ</mi><mi mathvariant="bold">t</mi></msub></mrow></mrow></mstyle></mtd></mtr><mtr><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow></mrow></mstyle></mtd><mtd><mstyle scriptlevel="0" displaystyle="true"><mrow><mrow></mrow><mpadded height="0em" voffset="0em"><mspace mathbackground="black" width="31.298em" height="0.04em"></mspace></mpadded></mrow></mstyle></mtd></mtr></mtable><annotation encoding="application/x-tex">\begin{aligned}
@@ -724,7 +724,7 @@ <h1>SGD<a class="headerlink" href="#sgd" title="Permalink to this heading">¶</a
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.SGD.add_param_group">
-<span class="sig-name descname"><span class="pre">add_param_group</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">param_group</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L1003"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.SGD.add_param_group" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">add_param_group</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">param_group</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/optimizer.py#L1003"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.SGD.add_param_group" title="Permalink to this definition">¶</a></dt>
 <dd><p>Add a param group to the <a class="reference internal" href="../optim.html#torch.optim.Optimizer" title="torch.optim.Optimizer"><code class="xref py py-class docutils literal notranslate"><span class="pre">Optimizer</span></code></a> s <cite>param_groups</cite>.</p>
 <p>This can be useful when fine tuning a pre-trained network as frozen layers can be made
 trainable and added to the <a class="reference internal" href="../optim.html#torch.optim.Optimizer" title="torch.optim.Optimizer"><code class="xref py py-class docutils literal notranslate"><span class="pre">Optimizer</span></code></a> as training progresses.</p>
@@ -738,7 +738,7 @@ <h1>SGD<a class="headerlink" href="#sgd" title="Permalink to this heading">¶</a
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.SGD.load_state_dict">
-<span class="sig-name descname"><span class="pre">load_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state_dict</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L832"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.SGD.load_state_dict" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">load_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state_dict</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/optimizer.py#L832"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.SGD.load_state_dict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Load the optimizer state.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -762,7 +762,7 @@ <h1>SGD<a class="headerlink" href="#sgd" title="Permalink to this heading">¶</a
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.SGD.register_load_state_dict_post_hook">
-<span class="sig-name descname"><span class="pre">register_load_state_dict_post_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L798"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.SGD.register_load_state_dict_post_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_load_state_dict_post_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/optimizer.py#L798"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.SGD.register_load_state_dict_post_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register a load_state_dict post-hook which will be called after
 <a class="reference internal" href="torch.optim.Optimizer.load_state_dict.html#torch.optim.Optimizer.load_state_dict" title="torch.optim.Optimizer.load_state_dict"><code class="xref py py-meth docutils literal notranslate"><span class="pre">load_state_dict()</span></code></a> is called. It should have the
 following signature:</p>
@@ -796,7 +796,7 @@ <h1>SGD<a class="headerlink" href="#sgd" title="Permalink to this heading">¶</a
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.SGD.register_load_state_dict_pre_hook">
-<span class="sig-name descname"><span class="pre">register_load_state_dict_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L759"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.SGD.register_load_state_dict_pre_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_load_state_dict_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/optimizer.py#L759"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.SGD.register_load_state_dict_pre_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register a load_state_dict pre-hook which will be called before
 <a class="reference internal" href="torch.optim.Optimizer.load_state_dict.html#torch.optim.Optimizer.load_state_dict" title="torch.optim.Optimizer.load_state_dict"><code class="xref py py-meth docutils literal notranslate"><span class="pre">load_state_dict()</span></code></a> is called. It should have the
 following signature:</p>
@@ -833,7 +833,7 @@ <h1>SGD<a class="headerlink" href="#sgd" title="Permalink to this heading">¶</a
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.SGD.register_state_dict_post_hook">
-<span class="sig-name descname"><span class="pre">register_state_dict_post_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L601"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.SGD.register_state_dict_post_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_state_dict_post_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/optimizer.py#L601"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.SGD.register_state_dict_post_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register a state dict post-hook which will be called after <a class="reference internal" href="torch.optim.Optimizer.state_dict.html#torch.optim.Optimizer.state_dict" title="torch.optim.Optimizer.state_dict"><code class="xref py py-meth docutils literal notranslate"><span class="pre">state_dict()</span></code></a> is called.</p>
 <p>It should have the following signature:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">hook</span><span class="p">(</span><span class="n">optimizer</span><span class="p">,</span> <span class="n">state_dict</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">state_dict</span> <span class="ow">or</span> <span class="kc">None</span>
@@ -865,7 +865,7 @@ <h1>SGD<a class="headerlink" href="#sgd" title="Permalink to this heading">¶</a
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.SGD.register_state_dict_pre_hook">
-<span class="sig-name descname"><span class="pre">register_state_dict_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L569"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.SGD.register_state_dict_pre_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_state_dict_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/optimizer.py#L569"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.SGD.register_state_dict_pre_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register a state dict pre-hook which will be called before <a class="reference internal" href="torch.optim.Optimizer.state_dict.html#torch.optim.Optimizer.state_dict" title="torch.optim.Optimizer.state_dict"><code class="xref py py-meth docutils literal notranslate"><span class="pre">state_dict()</span></code></a> is called.</p>
 <p>It should have the following signature:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">hook</span><span class="p">(</span><span class="n">optimizer</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span>
@@ -897,7 +897,7 @@ <h1>SGD<a class="headerlink" href="#sgd" title="Permalink to this heading">¶</a
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.SGD.register_step_post_hook">
-<span class="sig-name descname"><span class="pre">register_step_post_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L548"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.SGD.register_step_post_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_step_post_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/optimizer.py#L548"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.SGD.register_step_post_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register an optimizer step post hook which will be called after optimizer step.</p>
 <p>It should have the following signature:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">hook</span><span class="p">(</span><span class="n">optimizer</span><span class="p">,</span> <span class="n">args</span><span class="p">,</span> <span class="n">kwargs</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span>
@@ -920,7 +920,7 @@ <h1>SGD<a class="headerlink" href="#sgd" title="Permalink to this heading">¶</a
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.SGD.register_step_pre_hook">
-<span class="sig-name descname"><span class="pre">register_step_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L525"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.SGD.register_step_pre_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_step_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/optimizer.py#L525"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.SGD.register_step_pre_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register an optimizer step pre hook which will be called before optimizer step.</p>
 <p>It should have the following signature:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">hook</span><span class="p">(</span><span class="n">optimizer</span><span class="p">,</span> <span class="n">args</span><span class="p">,</span> <span class="n">kwargs</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span> <span class="ow">or</span> <span class="n">modified</span> <span class="n">args</span> <span class="ow">and</span> <span class="n">kwargs</span>
@@ -945,7 +945,7 @@ <h1>SGD<a class="headerlink" href="#sgd" title="Permalink to this heading">¶</a
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.SGD.state_dict">
-<span class="sig-name descname"><span class="pre">state_dict</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L635"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.SGD.state_dict" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">state_dict</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/optimizer.py#L635"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.SGD.state_dict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the state of the optimizer as a <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.13)"><code class="xref py py-class docutils literal notranslate"><span class="pre">dict</span></code></a>.</p>
 <p>It contains two entries:</p>
 <ul class="simple">
@@ -1008,7 +1008,7 @@ <h1>SGD<a class="headerlink" href="#sgd" title="Permalink to this heading">¶</a
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.SGD.step">
-<span class="sig-name descname"><span class="pre">step</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">closure</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/sgd.html#SGD.step"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/sgd.py#L103"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.SGD.step" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">step</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">closure</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/sgd.html#SGD.step"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/sgd.py#L103"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.SGD.step" title="Permalink to this definition">¶</a></dt>
 <dd><p>Perform a single optimization step.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -1020,7 +1020,7 @@ <h1>SGD<a class="headerlink" href="#sgd" title="Permalink to this heading">¶</a
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.SGD.zero_grad">
-<span class="sig-name descname"><span class="pre">zero_grad</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">set_to_none</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L931"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.SGD.zero_grad" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">zero_grad</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">set_to_none</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/optimizer.py#L931"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.SGD.zero_grad" title="Permalink to this definition">¶</a></dt>
 <dd><p>Reset the gradients of all optimized <a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.Tensor</span></code></a> s.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.optim.SparseAdam.html b/main/generated/torch.optim.SparseAdam.html
index 9df8cb3fc6e..fcf622464da 100644
--- a/main/generated/torch.optim.SparseAdam.html
+++ b/main/generated/torch.optim.SparseAdam.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>SparseAdam<a class="headerlink" href="#sparseadam" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.optim.SparseAdam">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.optim.</span></span><span class="sig-name descname"><span class="pre">SparseAdam</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">params</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lr</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">betas</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">(0.9,</span> <span class="pre">0.999)</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-08</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">maximize</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/sparse_adam.html#SparseAdam"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/sparse_adam.py#L14"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.SparseAdam" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.optim.</span></span><span class="sig-name descname"><span class="pre">SparseAdam</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">params</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lr</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">betas</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">(0.9,</span> <span class="pre">0.999)</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-08</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">maximize</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/sparse_adam.html#SparseAdam"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/sparse_adam.py#L14"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.SparseAdam" title="Permalink to this definition">¶</a></dt>
 <dd><p>SparseAdam implements a masked version of the Adam algorithm
 suitable for sparse gradients. Currently, due to implementation constraints (explained
 below), SparseAdam is only intended for a narrow subset of use cases, specifically
@@ -671,7 +671,7 @@ <h1>SparseAdam<a class="headerlink" href="#sparseadam" title="Permalink to this
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.SparseAdam.add_param_group">
-<span class="sig-name descname"><span class="pre">add_param_group</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">param_group</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L1003"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.SparseAdam.add_param_group" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">add_param_group</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">param_group</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/optimizer.py#L1003"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.SparseAdam.add_param_group" title="Permalink to this definition">¶</a></dt>
 <dd><p>Add a param group to the <a class="reference internal" href="../optim.html#torch.optim.Optimizer" title="torch.optim.Optimizer"><code class="xref py py-class docutils literal notranslate"><span class="pre">Optimizer</span></code></a> s <cite>param_groups</cite>.</p>
 <p>This can be useful when fine tuning a pre-trained network as frozen layers can be made
 trainable and added to the <a class="reference internal" href="../optim.html#torch.optim.Optimizer" title="torch.optim.Optimizer"><code class="xref py py-class docutils literal notranslate"><span class="pre">Optimizer</span></code></a> as training progresses.</p>
@@ -685,7 +685,7 @@ <h1>SparseAdam<a class="headerlink" href="#sparseadam" title="Permalink to this
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.SparseAdam.load_state_dict">
-<span class="sig-name descname"><span class="pre">load_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state_dict</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L832"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.SparseAdam.load_state_dict" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">load_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state_dict</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/optimizer.py#L832"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.SparseAdam.load_state_dict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Load the optimizer state.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -709,7 +709,7 @@ <h1>SparseAdam<a class="headerlink" href="#sparseadam" title="Permalink to this
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.SparseAdam.register_load_state_dict_post_hook">
-<span class="sig-name descname"><span class="pre">register_load_state_dict_post_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L798"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.SparseAdam.register_load_state_dict_post_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_load_state_dict_post_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/optimizer.py#L798"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.SparseAdam.register_load_state_dict_post_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register a load_state_dict post-hook which will be called after
 <a class="reference internal" href="torch.optim.Optimizer.load_state_dict.html#torch.optim.Optimizer.load_state_dict" title="torch.optim.Optimizer.load_state_dict"><code class="xref py py-meth docutils literal notranslate"><span class="pre">load_state_dict()</span></code></a> is called. It should have the
 following signature:</p>
@@ -743,7 +743,7 @@ <h1>SparseAdam<a class="headerlink" href="#sparseadam" title="Permalink to this
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.SparseAdam.register_load_state_dict_pre_hook">
-<span class="sig-name descname"><span class="pre">register_load_state_dict_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L759"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.SparseAdam.register_load_state_dict_pre_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_load_state_dict_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/optimizer.py#L759"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.SparseAdam.register_load_state_dict_pre_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register a load_state_dict pre-hook which will be called before
 <a class="reference internal" href="torch.optim.Optimizer.load_state_dict.html#torch.optim.Optimizer.load_state_dict" title="torch.optim.Optimizer.load_state_dict"><code class="xref py py-meth docutils literal notranslate"><span class="pre">load_state_dict()</span></code></a> is called. It should have the
 following signature:</p>
@@ -780,7 +780,7 @@ <h1>SparseAdam<a class="headerlink" href="#sparseadam" title="Permalink to this
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.SparseAdam.register_state_dict_post_hook">
-<span class="sig-name descname"><span class="pre">register_state_dict_post_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L601"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.SparseAdam.register_state_dict_post_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_state_dict_post_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/optimizer.py#L601"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.SparseAdam.register_state_dict_post_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register a state dict post-hook which will be called after <a class="reference internal" href="torch.optim.Optimizer.state_dict.html#torch.optim.Optimizer.state_dict" title="torch.optim.Optimizer.state_dict"><code class="xref py py-meth docutils literal notranslate"><span class="pre">state_dict()</span></code></a> is called.</p>
 <p>It should have the following signature:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">hook</span><span class="p">(</span><span class="n">optimizer</span><span class="p">,</span> <span class="n">state_dict</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">state_dict</span> <span class="ow">or</span> <span class="kc">None</span>
@@ -812,7 +812,7 @@ <h1>SparseAdam<a class="headerlink" href="#sparseadam" title="Permalink to this
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.SparseAdam.register_state_dict_pre_hook">
-<span class="sig-name descname"><span class="pre">register_state_dict_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L569"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.SparseAdam.register_state_dict_pre_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_state_dict_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/optimizer.py#L569"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.SparseAdam.register_state_dict_pre_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register a state dict pre-hook which will be called before <a class="reference internal" href="torch.optim.Optimizer.state_dict.html#torch.optim.Optimizer.state_dict" title="torch.optim.Optimizer.state_dict"><code class="xref py py-meth docutils literal notranslate"><span class="pre">state_dict()</span></code></a> is called.</p>
 <p>It should have the following signature:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">hook</span><span class="p">(</span><span class="n">optimizer</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span>
@@ -844,7 +844,7 @@ <h1>SparseAdam<a class="headerlink" href="#sparseadam" title="Permalink to this
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.SparseAdam.register_step_post_hook">
-<span class="sig-name descname"><span class="pre">register_step_post_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L548"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.SparseAdam.register_step_post_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_step_post_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/optimizer.py#L548"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.SparseAdam.register_step_post_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register an optimizer step post hook which will be called after optimizer step.</p>
 <p>It should have the following signature:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">hook</span><span class="p">(</span><span class="n">optimizer</span><span class="p">,</span> <span class="n">args</span><span class="p">,</span> <span class="n">kwargs</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span>
@@ -867,7 +867,7 @@ <h1>SparseAdam<a class="headerlink" href="#sparseadam" title="Permalink to this
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.SparseAdam.register_step_pre_hook">
-<span class="sig-name descname"><span class="pre">register_step_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L525"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.SparseAdam.register_step_pre_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_step_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/optimizer.py#L525"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.SparseAdam.register_step_pre_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register an optimizer step pre hook which will be called before optimizer step.</p>
 <p>It should have the following signature:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">hook</span><span class="p">(</span><span class="n">optimizer</span><span class="p">,</span> <span class="n">args</span><span class="p">,</span> <span class="n">kwargs</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span> <span class="ow">or</span> <span class="n">modified</span> <span class="n">args</span> <span class="ow">and</span> <span class="n">kwargs</span>
@@ -892,7 +892,7 @@ <h1>SparseAdam<a class="headerlink" href="#sparseadam" title="Permalink to this
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.SparseAdam.state_dict">
-<span class="sig-name descname"><span class="pre">state_dict</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L635"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.SparseAdam.state_dict" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">state_dict</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/optimizer.py#L635"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.SparseAdam.state_dict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the state of the optimizer as a <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.13)"><code class="xref py py-class docutils literal notranslate"><span class="pre">dict</span></code></a>.</p>
 <p>It contains two entries:</p>
 <ul class="simple">
@@ -955,7 +955,7 @@ <h1>SparseAdam<a class="headerlink" href="#sparseadam" title="Permalink to this
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.SparseAdam.step">
-<span class="sig-name descname"><span class="pre">step</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">closure</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/sparse_adam.html#SparseAdam.step"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/sparse_adam.py#L58"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.SparseAdam.step" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">step</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">closure</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/sparse_adam.html#SparseAdam.step"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/sparse_adam.py#L58"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.SparseAdam.step" title="Permalink to this definition">¶</a></dt>
 <dd><p>Perform a single optimization step.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -967,7 +967,7 @@ <h1>SparseAdam<a class="headerlink" href="#sparseadam" title="Permalink to this
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.SparseAdam.zero_grad">
-<span class="sig-name descname"><span class="pre">zero_grad</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">set_to_none</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L931"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.SparseAdam.zero_grad" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">zero_grad</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">set_to_none</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/optimizer.py#L931"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.SparseAdam.zero_grad" title="Permalink to this definition">¶</a></dt>
 <dd><p>Reset the gradients of all optimized <a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.Tensor</span></code></a> s.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.optim.lr_scheduler.ChainedScheduler.html b/main/generated/torch.optim.lr_scheduler.ChainedScheduler.html
index 8d24ffadc27..506572d5f4e 100644
--- a/main/generated/torch.optim.lr_scheduler.ChainedScheduler.html
+++ b/main/generated/torch.optim.lr_scheduler.ChainedScheduler.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>ChainedScheduler<a class="headerlink" href="#chainedscheduler" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.optim.lr_scheduler.ChainedScheduler">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.optim.lr_scheduler.</span></span><span class="sig-name descname"><span class="pre">ChainedScheduler</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">schedulers</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">optimizer</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/lr_scheduler.html#ChainedScheduler"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L1138"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.ChainedScheduler" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.optim.lr_scheduler.</span></span><span class="sig-name descname"><span class="pre">ChainedScheduler</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">schedulers</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">optimizer</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/lr_scheduler.html#ChainedScheduler"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/lr_scheduler.py#L1138"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.ChainedScheduler" title="Permalink to this definition">¶</a></dt>
 <dd><p>Chains a list of learning rate schedulers.</p>
 <p>Takes in a sequence of chainable learning rate schedulers and calls their
 step() functions consecutively in just one call to step().</p>
@@ -642,7 +642,7 @@ <h1>ChainedScheduler<a class="headerlink" href="#chainedscheduler" title="Permal
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.lr_scheduler.ChainedScheduler.get_last_lr">
-<span class="sig-name descname"><span class="pre">get_last_lr</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L172"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.ChainedScheduler.get_last_lr" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">get_last_lr</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/lr_scheduler.py#L172"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.ChainedScheduler.get_last_lr" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return last computed learning rate by current scheduler.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -653,7 +653,7 @@ <h1>ChainedScheduler<a class="headerlink" href="#chainedscheduler" title="Permal
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.lr_scheduler.ChainedScheduler.get_lr">
-<span class="sig-name descname"><span class="pre">get_lr</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L176"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.ChainedScheduler.get_lr" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">get_lr</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/lr_scheduler.py#L176"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.ChainedScheduler.get_lr" title="Permalink to this definition">¶</a></dt>
 <dd><p>Compute learning rate using chainable form of the scheduler.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -664,7 +664,7 @@ <h1>ChainedScheduler<a class="headerlink" href="#chainedscheduler" title="Permal
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.lr_scheduler.ChainedScheduler.load_state_dict">
-<span class="sig-name descname"><span class="pre">load_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state_dict</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/lr_scheduler.html#ChainedScheduler.load_state_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L1224"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.ChainedScheduler.load_state_dict" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">load_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state_dict</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/lr_scheduler.html#ChainedScheduler.load_state_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/lr_scheduler.py#L1224"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.ChainedScheduler.load_state_dict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Load the scheduler’s state.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -676,7 +676,7 @@ <h1>ChainedScheduler<a class="headerlink" href="#chainedscheduler" title="Permal
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.lr_scheduler.ChainedScheduler.print_lr">
-<span class="sig-name descname"><span class="pre">print_lr</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">is_verbose</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lr</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">epoch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L180"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.ChainedScheduler.print_lr" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">print_lr</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">is_verbose</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lr</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">epoch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/lr_scheduler.py#L180"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.ChainedScheduler.print_lr" title="Permalink to this definition">¶</a></dt>
 <dd><p>Display the current learning rate.</p>
 <div class="deprecated">
 <p><span class="versionmodified deprecated">Deprecated since version 2.4: </span><code class="docutils literal notranslate"><span class="pre">print_lr()</span></code> is deprecated. Please use <code class="docutils literal notranslate"><span class="pre">get_last_lr()</span></code> to access the
@@ -688,7 +688,7 @@ <h1>ChainedScheduler<a class="headerlink" href="#chainedscheduler" title="Permal
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.lr_scheduler.ChainedScheduler.state_dict">
-<span class="sig-name descname"><span class="pre">state_dict</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/lr_scheduler.html#ChainedScheduler.state_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L1205"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.ChainedScheduler.state_dict" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">state_dict</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/lr_scheduler.html#ChainedScheduler.state_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/lr_scheduler.py#L1205"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.ChainedScheduler.state_dict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the state of the scheduler as a <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.13)"><code class="xref py py-class docutils literal notranslate"><span class="pre">dict</span></code></a>.</p>
 <p>It contains an entry for every variable in self.__dict__ which
 is not the optimizer.
@@ -697,7 +697,7 @@ <h1>ChainedScheduler<a class="headerlink" href="#chainedscheduler" title="Permal
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.lr_scheduler.ChainedScheduler.step">
-<span class="sig-name descname"><span class="pre">step</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/lr_scheduler.html#ChainedScheduler.step"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L1197"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.ChainedScheduler.step" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">step</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/lr_scheduler.html#ChainedScheduler.step"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/lr_scheduler.py#L1197"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.ChainedScheduler.step" title="Permalink to this definition">¶</a></dt>
 <dd><p>Perform a step.</p>
 </dd></dl>
 
diff --git a/main/generated/torch.optim.lr_scheduler.ConstantLR.html b/main/generated/torch.optim.lr_scheduler.ConstantLR.html
index f19efd61724..4252de0c4d6 100644
--- a/main/generated/torch.optim.lr_scheduler.ConstantLR.html
+++ b/main/generated/torch.optim.lr_scheduler.ConstantLR.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>ConstantLR<a class="headerlink" href="#constantlr" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.optim.lr_scheduler.ConstantLR">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.optim.lr_scheduler.</span></span><span class="sig-name descname"><span class="pre">ConstantLR</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">optimizer</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">factor</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.3333333333333333</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">total_iters</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">last_epoch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'deprecated'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/lr_scheduler.html#ConstantLR"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L611"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.ConstantLR" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.optim.lr_scheduler.</span></span><span class="sig-name descname"><span class="pre">ConstantLR</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">optimizer</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">factor</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.3333333333333333</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">total_iters</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">last_epoch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'deprecated'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/lr_scheduler.html#ConstantLR"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/lr_scheduler.py#L611"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.ConstantLR" title="Permalink to this definition">¶</a></dt>
 <dd><p>Multiply the learning rate of each parameter group by a small constant factor.</p>
 <p>The multiplication is done until the number of epoch reaches a pre-defined milestone: total_iters.
 Notice that such multiplication of the small constant factor can
@@ -652,7 +652,7 @@ <h1>ConstantLR<a class="headerlink" href="#constantlr" title="Permalink to this
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.lr_scheduler.ConstantLR.get_last_lr">
-<span class="sig-name descname"><span class="pre">get_last_lr</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L172"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.ConstantLR.get_last_lr" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">get_last_lr</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/lr_scheduler.py#L172"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.ConstantLR.get_last_lr" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return last computed learning rate by current scheduler.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -663,13 +663,13 @@ <h1>ConstantLR<a class="headerlink" href="#constantlr" title="Permalink to this
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.lr_scheduler.ConstantLR.get_lr">
-<span class="sig-name descname"><span class="pre">get_lr</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/lr_scheduler.html#ConstantLR.get_lr"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L664"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.ConstantLR.get_lr" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">get_lr</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/lr_scheduler.html#ConstantLR.get_lr"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/lr_scheduler.py#L664"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.ConstantLR.get_lr" title="Permalink to this definition">¶</a></dt>
 <dd><p>Compute the learning rate of each parameter group.</p>
 </dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.lr_scheduler.ConstantLR.load_state_dict">
-<span class="sig-name descname"><span class="pre">load_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state_dict</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L163"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.ConstantLR.load_state_dict" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">load_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state_dict</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/lr_scheduler.py#L163"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.ConstantLR.load_state_dict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Load the scheduler’s state.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -681,7 +681,7 @@ <h1>ConstantLR<a class="headerlink" href="#constantlr" title="Permalink to this
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.lr_scheduler.ConstantLR.print_lr">
-<span class="sig-name descname"><span class="pre">print_lr</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">is_verbose</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lr</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">epoch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L180"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.ConstantLR.print_lr" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">print_lr</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">is_verbose</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lr</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">epoch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/lr_scheduler.py#L180"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.ConstantLR.print_lr" title="Permalink to this definition">¶</a></dt>
 <dd><p>Display the current learning rate.</p>
 <div class="deprecated">
 <p><span class="versionmodified deprecated">Deprecated since version 2.4: </span><code class="docutils literal notranslate"><span class="pre">print_lr()</span></code> is deprecated. Please use <code class="docutils literal notranslate"><span class="pre">get_last_lr()</span></code> to access the
@@ -693,7 +693,7 @@ <h1>ConstantLR<a class="headerlink" href="#constantlr" title="Permalink to this
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.lr_scheduler.ConstantLR.state_dict">
-<span class="sig-name descname"><span class="pre">state_dict</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L153"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.ConstantLR.state_dict" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">state_dict</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/lr_scheduler.py#L153"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.ConstantLR.state_dict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the state of the scheduler as a <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.13)"><code class="xref py py-class docutils literal notranslate"><span class="pre">dict</span></code></a>.</p>
 <p>It contains an entry for every variable in self.__dict__ which
 is not the optimizer.</p>
@@ -701,7 +701,7 @@ <h1>ConstantLR<a class="headerlink" href="#constantlr" title="Permalink to this
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.lr_scheduler.ConstantLR.step">
-<span class="sig-name descname"><span class="pre">step</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">epoch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L208"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.ConstantLR.step" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">step</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">epoch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/lr_scheduler.py#L208"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.ConstantLR.step" title="Permalink to this definition">¶</a></dt>
 <dd><p>Perform a step.</p>
 <dl class="field-list simple">
 </dl>
diff --git a/main/generated/torch.optim.lr_scheduler.CosineAnnealingLR.html b/main/generated/torch.optim.lr_scheduler.CosineAnnealingLR.html
index f1c698568b9..9ec7f4207c4 100644
--- a/main/generated/torch.optim.lr_scheduler.CosineAnnealingLR.html
+++ b/main/generated/torch.optim.lr_scheduler.CosineAnnealingLR.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>CosineAnnealingLR<a class="headerlink" href="#cosineannealinglr" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.optim.lr_scheduler.CosineAnnealingLR">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.optim.lr_scheduler.</span></span><span class="sig-name descname"><span class="pre">CosineAnnealingLR</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">optimizer</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">T_max</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eta_min</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">last_epoch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'deprecated'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/lr_scheduler.html#CosineAnnealingLR"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L1043"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.CosineAnnealingLR" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.optim.lr_scheduler.</span></span><span class="sig-name descname"><span class="pre">CosineAnnealingLR</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">optimizer</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">T_max</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eta_min</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">last_epoch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'deprecated'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/lr_scheduler.html#CosineAnnealingLR"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/lr_scheduler.py#L1043"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.CosineAnnealingLR" title="Permalink to this definition">¶</a></dt>
 <dd><p>Set the learning rate of each parameter group using a cosine annealing schedule.</p>
 <p>The <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><msub><mi>η</mi><mrow><mi>m</mi><mi>a</mi><mi>x</mi></mrow></msub></mrow><annotation encoding="application/x-tex">\eta_{max}</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.625em;vertical-align:-0.1944em;"></span><span class="mord"><span class="mord mathnormal" style="margin-right:0.03588em;">η</span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.1514em;"><span style="top:-2.55em;margin-left:-0.0359em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mord mathnormal mtight">ma</span><span class="mord mathnormal mtight">x</span></span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.15em;"><span></span></span></span></span></span></span></span></span></span></span> is set to the initial lr and
 <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><msub><mi>T</mi><mrow><mi>c</mi><mi>u</mi><mi>r</mi></mrow></msub></mrow><annotation encoding="application/x-tex">T_{cur}</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.8333em;vertical-align:-0.15em;"></span><span class="mord"><span class="mord mathnormal" style="margin-right:0.13889em;">T</span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.1514em;"><span style="top:-2.55em;margin-left:-0.1389em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mord mathnormal mtight">c</span><span class="mord mathnormal mtight">u</span><span class="mord mathnormal mtight" style="margin-right:0.02778em;">r</span></span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.15em;"><span></span></span></span></span></span></span></span></span></span></span> is the number of epochs since the last restart in SGDR:</p>
@@ -656,7 +656,7 @@ <h1>CosineAnnealingLR<a class="headerlink" href="#cosineannealinglr" title="Perm
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.lr_scheduler.CosineAnnealingLR.get_last_lr">
-<span class="sig-name descname"><span class="pre">get_last_lr</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L172"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.CosineAnnealingLR.get_last_lr" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">get_last_lr</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/lr_scheduler.py#L172"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.CosineAnnealingLR.get_last_lr" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return last computed learning rate by current scheduler.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -667,13 +667,13 @@ <h1>CosineAnnealingLR<a class="headerlink" href="#cosineannealinglr" title="Perm
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.lr_scheduler.CosineAnnealingLR.get_lr">
-<span class="sig-name descname"><span class="pre">get_lr</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/lr_scheduler.html#CosineAnnealingLR.get_lr"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L1100"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.CosineAnnealingLR.get_lr" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">get_lr</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/lr_scheduler.html#CosineAnnealingLR.get_lr"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/lr_scheduler.py#L1100"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.CosineAnnealingLR.get_lr" title="Permalink to this definition">¶</a></dt>
 <dd><p>Retrieve the learning rate of each parameter group.</p>
 </dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.lr_scheduler.CosineAnnealingLR.load_state_dict">
-<span class="sig-name descname"><span class="pre">load_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state_dict</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L163"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.CosineAnnealingLR.load_state_dict" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">load_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state_dict</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/lr_scheduler.py#L163"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.CosineAnnealingLR.load_state_dict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Load the scheduler’s state.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -685,7 +685,7 @@ <h1>CosineAnnealingLR<a class="headerlink" href="#cosineannealinglr" title="Perm
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.lr_scheduler.CosineAnnealingLR.print_lr">
-<span class="sig-name descname"><span class="pre">print_lr</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">is_verbose</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lr</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">epoch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L180"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.CosineAnnealingLR.print_lr" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">print_lr</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">is_verbose</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lr</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">epoch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/lr_scheduler.py#L180"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.CosineAnnealingLR.print_lr" title="Permalink to this definition">¶</a></dt>
 <dd><p>Display the current learning rate.</p>
 <div class="deprecated">
 <p><span class="versionmodified deprecated">Deprecated since version 2.4: </span><code class="docutils literal notranslate"><span class="pre">print_lr()</span></code> is deprecated. Please use <code class="docutils literal notranslate"><span class="pre">get_last_lr()</span></code> to access the
@@ -697,7 +697,7 @@ <h1>CosineAnnealingLR<a class="headerlink" href="#cosineannealinglr" title="Perm
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.lr_scheduler.CosineAnnealingLR.state_dict">
-<span class="sig-name descname"><span class="pre">state_dict</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L153"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.CosineAnnealingLR.state_dict" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">state_dict</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/lr_scheduler.py#L153"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.CosineAnnealingLR.state_dict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the state of the scheduler as a <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.13)"><code class="xref py py-class docutils literal notranslate"><span class="pre">dict</span></code></a>.</p>
 <p>It contains an entry for every variable in self.__dict__ which
 is not the optimizer.</p>
@@ -705,7 +705,7 @@ <h1>CosineAnnealingLR<a class="headerlink" href="#cosineannealinglr" title="Perm
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.lr_scheduler.CosineAnnealingLR.step">
-<span class="sig-name descname"><span class="pre">step</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">epoch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L208"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.CosineAnnealingLR.step" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">step</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">epoch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/lr_scheduler.py#L208"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.CosineAnnealingLR.step" title="Permalink to this definition">¶</a></dt>
 <dd><p>Perform a step.</p>
 <dl class="field-list simple">
 </dl>
diff --git a/main/generated/torch.optim.lr_scheduler.CosineAnnealingWarmRestarts.html b/main/generated/torch.optim.lr_scheduler.CosineAnnealingWarmRestarts.html
index ee492a09c76..d072fc929de 100644
--- a/main/generated/torch.optim.lr_scheduler.CosineAnnealingWarmRestarts.html
+++ b/main/generated/torch.optim.lr_scheduler.CosineAnnealingWarmRestarts.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>CosineAnnealingWarmRestarts<a class="headerlink" href="#cosineannealingwarmrestarts" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.optim.lr_scheduler.CosineAnnealingWarmRestarts">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.optim.lr_scheduler.</span></span><span class="sig-name descname"><span class="pre">CosineAnnealingWarmRestarts</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">optimizer</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">T_0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">T_mult</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eta_min</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">last_epoch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'deprecated'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/lr_scheduler.html#CosineAnnealingWarmRestarts"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L1736"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.CosineAnnealingWarmRestarts" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.optim.lr_scheduler.</span></span><span class="sig-name descname"><span class="pre">CosineAnnealingWarmRestarts</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">optimizer</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">T_0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">T_mult</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eta_min</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">last_epoch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'deprecated'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/lr_scheduler.html#CosineAnnealingWarmRestarts"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/lr_scheduler.py#L1736"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.CosineAnnealingWarmRestarts" title="Permalink to this definition">¶</a></dt>
 <dd><p>Set the learning rate of each parameter group using a cosine annealing schedule.</p>
 <p>The <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><msub><mi>η</mi><mrow><mi>m</mi><mi>a</mi><mi>x</mi></mrow></msub></mrow><annotation encoding="application/x-tex">\eta_{max}</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.625em;vertical-align:-0.1944em;"></span><span class="mord"><span class="mord mathnormal" style="margin-right:0.03588em;">η</span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.1514em;"><span style="top:-2.55em;margin-left:-0.0359em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mord mathnormal mtight">ma</span><span class="mord mathnormal mtight">x</span></span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.15em;"><span></span></span></span></span></span></span></span></span></span></span> is set to the initial lr, <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><msub><mi>T</mi><mrow><mi>c</mi><mi>u</mi><mi>r</mi></mrow></msub></mrow><annotation encoding="application/x-tex">T_{cur}</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.8333em;vertical-align:-0.15em;"></span><span class="mord"><span class="mord mathnormal" style="margin-right:0.13889em;">T</span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.1514em;"><span style="top:-2.55em;margin-left:-0.1389em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mord mathnormal mtight">c</span><span class="mord mathnormal mtight">u</span><span class="mord mathnormal mtight" style="margin-right:0.02778em;">r</span></span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.15em;"><span></span></span></span></span></span></span></span></span></span></span>
 is the number of epochs since the last restart and <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><msub><mi>T</mi><mi>i</mi></msub></mrow><annotation encoding="application/x-tex">T_{i}</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.8333em;vertical-align:-0.15em;"></span><span class="mord"><span class="mord mathnormal" style="margin-right:0.13889em;">T</span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.3117em;"><span style="top:-2.55em;margin-left:-0.1389em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mord mathnormal mtight">i</span></span></span></span></span><span class="vlist-s">​</span></span><span class="vlist-r"><span class="vlist" style="height:0.15em;"><span></span></span></span></span></span></span></span></span></span></span> is the number
@@ -645,7 +645,7 @@ <h1>CosineAnnealingWarmRestarts<a class="headerlink" href="#cosineannealingwarmr
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.lr_scheduler.CosineAnnealingWarmRestarts.get_last_lr">
-<span class="sig-name descname"><span class="pre">get_last_lr</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L172"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.CosineAnnealingWarmRestarts.get_last_lr" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">get_last_lr</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/lr_scheduler.py#L172"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.CosineAnnealingWarmRestarts.get_last_lr" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return last computed learning rate by current scheduler.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -656,13 +656,13 @@ <h1>CosineAnnealingWarmRestarts<a class="headerlink" href="#cosineannealingwarmr
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.lr_scheduler.CosineAnnealingWarmRestarts.get_lr">
-<span class="sig-name descname"><span class="pre">get_lr</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/lr_scheduler.html#CosineAnnealingWarmRestarts.get_lr"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L1794"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.CosineAnnealingWarmRestarts.get_lr" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">get_lr</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/lr_scheduler.html#CosineAnnealingWarmRestarts.get_lr"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/lr_scheduler.py#L1794"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.CosineAnnealingWarmRestarts.get_lr" title="Permalink to this definition">¶</a></dt>
 <dd><p>Compute the initial learning rate.</p>
 </dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.lr_scheduler.CosineAnnealingWarmRestarts.load_state_dict">
-<span class="sig-name descname"><span class="pre">load_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state_dict</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L163"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.CosineAnnealingWarmRestarts.load_state_dict" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">load_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state_dict</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/lr_scheduler.py#L163"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.CosineAnnealingWarmRestarts.load_state_dict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Load the scheduler’s state.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -674,7 +674,7 @@ <h1>CosineAnnealingWarmRestarts<a class="headerlink" href="#cosineannealingwarmr
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.lr_scheduler.CosineAnnealingWarmRestarts.print_lr">
-<span class="sig-name descname"><span class="pre">print_lr</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">is_verbose</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lr</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">epoch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L180"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.CosineAnnealingWarmRestarts.print_lr" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">print_lr</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">is_verbose</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lr</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">epoch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/lr_scheduler.py#L180"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.CosineAnnealingWarmRestarts.print_lr" title="Permalink to this definition">¶</a></dt>
 <dd><p>Display the current learning rate.</p>
 <div class="deprecated">
 <p><span class="versionmodified deprecated">Deprecated since version 2.4: </span><code class="docutils literal notranslate"><span class="pre">print_lr()</span></code> is deprecated. Please use <code class="docutils literal notranslate"><span class="pre">get_last_lr()</span></code> to access the
@@ -686,7 +686,7 @@ <h1>CosineAnnealingWarmRestarts<a class="headerlink" href="#cosineannealingwarmr
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.lr_scheduler.CosineAnnealingWarmRestarts.state_dict">
-<span class="sig-name descname"><span class="pre">state_dict</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L153"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.CosineAnnealingWarmRestarts.state_dict" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">state_dict</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/lr_scheduler.py#L153"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.CosineAnnealingWarmRestarts.state_dict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the state of the scheduler as a <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.13)"><code class="xref py py-class docutils literal notranslate"><span class="pre">dict</span></code></a>.</p>
 <p>It contains an entry for every variable in self.__dict__ which
 is not the optimizer.</p>
@@ -694,7 +694,7 @@ <h1>CosineAnnealingWarmRestarts<a class="headerlink" href="#cosineannealingwarmr
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.lr_scheduler.CosineAnnealingWarmRestarts.step">
-<span class="sig-name descname"><span class="pre">step</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">epoch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/lr_scheduler.html#CosineAnnealingWarmRestarts.step"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L1806"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.CosineAnnealingWarmRestarts.step" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">step</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">epoch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/lr_scheduler.html#CosineAnnealingWarmRestarts.step"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/lr_scheduler.py#L1806"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.CosineAnnealingWarmRestarts.step" title="Permalink to this definition">¶</a></dt>
 <dd><p>Step could be called after every batch update.</p>
 <p class="rubric">Example</p>
 <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">scheduler</span> <span class="o">=</span> <span class="n">CosineAnnealingWarmRestarts</span><span class="p">(</span><span class="n">optimizer</span><span class="p">,</span> <span class="n">T_0</span><span class="p">,</span> <span class="n">T_mult</span><span class="p">)</span>
diff --git a/main/generated/torch.optim.lr_scheduler.CyclicLR.html b/main/generated/torch.optim.lr_scheduler.CyclicLR.html
index 6911bc9c8bd..289621c7593 100644
--- a/main/generated/torch.optim.lr_scheduler.CyclicLR.html
+++ b/main/generated/torch.optim.lr_scheduler.CyclicLR.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>CyclicLR<a class="headerlink" href="#cycliclr" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.optim.lr_scheduler.CyclicLR">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.optim.lr_scheduler.</span></span><span class="sig-name descname"><span class="pre">CyclicLR</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">optimizer</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">base_lr</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_lr</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">step_size_up</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2000</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">step_size_down</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'triangular'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">gamma</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale_fn</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'cycle'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cycle_momentum</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">base_momentum</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.8</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_momentum</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.9</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">last_epoch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'deprecated'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/lr_scheduler.html#CyclicLR"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L1455"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.CyclicLR" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.optim.lr_scheduler.</span></span><span class="sig-name descname"><span class="pre">CyclicLR</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">optimizer</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">base_lr</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_lr</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">step_size_up</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2000</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">step_size_down</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'triangular'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">gamma</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale_fn</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'cycle'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cycle_momentum</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">base_momentum</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.8</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_momentum</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.9</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">last_epoch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'deprecated'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/lr_scheduler.html#CyclicLR"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/lr_scheduler.py#L1455"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.CyclicLR" title="Permalink to this definition">¶</a></dt>
 <dd><p>Sets the learning rate of each parameter group according to cyclical learning rate policy (CLR).</p>
 <p>The policy cycles the learning rate between two boundaries with a constant frequency,
 as detailed in the paper <a class="reference external" href="https://arxiv.org/abs/1506.01186">Cyclical Learning Rates for Training Neural Networks</a>.
@@ -709,7 +709,7 @@ <h1>CyclicLR<a class="headerlink" href="#cycliclr" title="Permalink to this head
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.lr_scheduler.CyclicLR.get_last_lr">
-<span class="sig-name descname"><span class="pre">get_last_lr</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L172"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.CyclicLR.get_last_lr" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">get_last_lr</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/lr_scheduler.py#L172"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.CyclicLR.get_last_lr" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return last computed learning rate by current scheduler.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -720,7 +720,7 @@ <h1>CyclicLR<a class="headerlink" href="#cycliclr" title="Permalink to this head
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.lr_scheduler.CyclicLR.get_lr">
-<span class="sig-name descname"><span class="pre">get_lr</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/lr_scheduler.html#CyclicLR.get_lr"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L1666"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.CyclicLR.get_lr" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">get_lr</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/lr_scheduler.html#CyclicLR.get_lr"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/lr_scheduler.py#L1666"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.CyclicLR.get_lr" title="Permalink to this definition">¶</a></dt>
 <dd><p>Calculate the learning rate at batch index.</p>
 <p>This function treats <cite>self.last_epoch</cite> as the last batch index.</p>
 <p>If <cite>self.cycle_momentum</cite> is <code class="docutils literal notranslate"><span class="pre">True</span></code>, this function has a side effect of
@@ -729,13 +729,13 @@ <h1>CyclicLR<a class="headerlink" href="#cycliclr" title="Permalink to this head
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.lr_scheduler.CyclicLR.load_state_dict">
-<span class="sig-name descname"><span class="pre">load_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state_dict</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/lr_scheduler.html#CyclicLR.load_state_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L1727"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.CyclicLR.load_state_dict" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">load_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state_dict</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/lr_scheduler.html#CyclicLR.load_state_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/lr_scheduler.py#L1727"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.CyclicLR.load_state_dict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Load the scheduler’s state.</p>
 </dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.lr_scheduler.CyclicLR.print_lr">
-<span class="sig-name descname"><span class="pre">print_lr</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">is_verbose</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lr</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">epoch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L180"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.CyclicLR.print_lr" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">print_lr</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">is_verbose</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lr</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">epoch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/lr_scheduler.py#L180"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.CyclicLR.print_lr" title="Permalink to this definition">¶</a></dt>
 <dd><p>Display the current learning rate.</p>
 <div class="deprecated">
 <p><span class="versionmodified deprecated">Deprecated since version 2.4: </span><code class="docutils literal notranslate"><span class="pre">print_lr()</span></code> is deprecated. Please use <code class="docutils literal notranslate"><span class="pre">get_last_lr()</span></code> to access the
@@ -747,7 +747,7 @@ <h1>CyclicLR<a class="headerlink" href="#cycliclr" title="Permalink to this head
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.lr_scheduler.CyclicLR.scale_fn">
-<span class="sig-name descname"><span class="pre">scale_fn</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/lr_scheduler.html#CyclicLR.scale_fn"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L1647"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.CyclicLR.scale_fn" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">scale_fn</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/lr_scheduler.html#CyclicLR.scale_fn"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/lr_scheduler.py#L1647"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.CyclicLR.scale_fn" title="Permalink to this definition">¶</a></dt>
 <dd><p>Get the scaling policy.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -758,7 +758,7 @@ <h1>CyclicLR<a class="headerlink" href="#cycliclr" title="Permalink to this head
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.lr_scheduler.CyclicLR.step">
-<span class="sig-name descname"><span class="pre">step</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">epoch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L208"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.CyclicLR.step" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">step</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">epoch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/lr_scheduler.py#L208"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.CyclicLR.step" title="Permalink to this definition">¶</a></dt>
 <dd><p>Perform a step.</p>
 <dl class="field-list simple">
 </dl>
diff --git a/main/generated/torch.optim.lr_scheduler.ExponentialLR.html b/main/generated/torch.optim.lr_scheduler.ExponentialLR.html
index 8bae2fee081..c56173c31e7 100644
--- a/main/generated/torch.optim.lr_scheduler.ExponentialLR.html
+++ b/main/generated/torch.optim.lr_scheduler.ExponentialLR.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>ExponentialLR<a class="headerlink" href="#exponentiallr" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.optim.lr_scheduler.ExponentialLR">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.optim.lr_scheduler.</span></span><span class="sig-name descname"><span class="pre">ExponentialLR</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">optimizer</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">gamma</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">last_epoch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'deprecated'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/lr_scheduler.html#ExponentialLR"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L787"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.ExponentialLR" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.optim.lr_scheduler.</span></span><span class="sig-name descname"><span class="pre">ExponentialLR</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">optimizer</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">gamma</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">last_epoch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'deprecated'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/lr_scheduler.html#ExponentialLR"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/lr_scheduler.py#L787"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.ExponentialLR" title="Permalink to this definition">¶</a></dt>
 <dd><p>Decays the learning rate of each parameter group by gamma every epoch.</p>
 <p>When last_epoch=-1, sets initial lr as lr.</p>
 <dl class="field-list simple">
@@ -633,7 +633,7 @@ <h1>ExponentialLR<a class="headerlink" href="#exponentiallr" title="Permalink to
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.lr_scheduler.ExponentialLR.get_last_lr">
-<span class="sig-name descname"><span class="pre">get_last_lr</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L172"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.ExponentialLR.get_last_lr" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">get_last_lr</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/lr_scheduler.py#L172"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.ExponentialLR.get_last_lr" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return last computed learning rate by current scheduler.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -644,13 +644,13 @@ <h1>ExponentialLR<a class="headerlink" href="#exponentiallr" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.lr_scheduler.ExponentialLR.get_lr">
-<span class="sig-name descname"><span class="pre">get_lr</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/lr_scheduler.html#ExponentialLR.get_lr"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L814"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.ExponentialLR.get_lr" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">get_lr</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/lr_scheduler.html#ExponentialLR.get_lr"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/lr_scheduler.py#L814"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.ExponentialLR.get_lr" title="Permalink to this definition">¶</a></dt>
 <dd><p>Compute the learning rate of each parameter group.</p>
 </dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.lr_scheduler.ExponentialLR.load_state_dict">
-<span class="sig-name descname"><span class="pre">load_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state_dict</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L163"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.ExponentialLR.load_state_dict" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">load_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state_dict</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/lr_scheduler.py#L163"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.ExponentialLR.load_state_dict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Load the scheduler’s state.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -662,7 +662,7 @@ <h1>ExponentialLR<a class="headerlink" href="#exponentiallr" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.lr_scheduler.ExponentialLR.print_lr">
-<span class="sig-name descname"><span class="pre">print_lr</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">is_verbose</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lr</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">epoch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L180"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.ExponentialLR.print_lr" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">print_lr</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">is_verbose</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lr</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">epoch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/lr_scheduler.py#L180"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.ExponentialLR.print_lr" title="Permalink to this definition">¶</a></dt>
 <dd><p>Display the current learning rate.</p>
 <div class="deprecated">
 <p><span class="versionmodified deprecated">Deprecated since version 2.4: </span><code class="docutils literal notranslate"><span class="pre">print_lr()</span></code> is deprecated. Please use <code class="docutils literal notranslate"><span class="pre">get_last_lr()</span></code> to access the
@@ -674,7 +674,7 @@ <h1>ExponentialLR<a class="headerlink" href="#exponentiallr" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.lr_scheduler.ExponentialLR.state_dict">
-<span class="sig-name descname"><span class="pre">state_dict</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L153"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.ExponentialLR.state_dict" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">state_dict</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/lr_scheduler.py#L153"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.ExponentialLR.state_dict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the state of the scheduler as a <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.13)"><code class="xref py py-class docutils literal notranslate"><span class="pre">dict</span></code></a>.</p>
 <p>It contains an entry for every variable in self.__dict__ which
 is not the optimizer.</p>
@@ -682,7 +682,7 @@ <h1>ExponentialLR<a class="headerlink" href="#exponentiallr" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.lr_scheduler.ExponentialLR.step">
-<span class="sig-name descname"><span class="pre">step</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">epoch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L208"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.ExponentialLR.step" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">step</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">epoch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/lr_scheduler.py#L208"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.ExponentialLR.step" title="Permalink to this definition">¶</a></dt>
 <dd><p>Perform a step.</p>
 <dl class="field-list simple">
 </dl>
diff --git a/main/generated/torch.optim.lr_scheduler.LRScheduler.html b/main/generated/torch.optim.lr_scheduler.LRScheduler.html
index 124bb16eea9..34161c258fb 100644
--- a/main/generated/torch.optim.lr_scheduler.LRScheduler.html
+++ b/main/generated/torch.optim.lr_scheduler.LRScheduler.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,13 +612,13 @@
 <h1>LRScheduler<a class="headerlink" href="#lrscheduler" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.optim.lr_scheduler.LRScheduler">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.optim.lr_scheduler.</span></span><span class="sig-name descname"><span class="pre">LRScheduler</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">optimizer</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">last_epoch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'deprecated'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/lr_scheduler.html#LRScheduler"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L86"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.LRScheduler" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.optim.lr_scheduler.</span></span><span class="sig-name descname"><span class="pre">LRScheduler</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">optimizer</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">last_epoch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'deprecated'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/lr_scheduler.html#LRScheduler"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/lr_scheduler.py#L86"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.LRScheduler" title="Permalink to this definition">¶</a></dt>
 <dd><p>Adjusts the learning rate during optimization.</p>
 <dl class="field-list simple">
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.lr_scheduler.LRScheduler.get_last_lr">
-<span class="sig-name descname"><span class="pre">get_last_lr</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/lr_scheduler.html#LRScheduler.get_last_lr"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L172"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.LRScheduler.get_last_lr" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">get_last_lr</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/lr_scheduler.html#LRScheduler.get_last_lr"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/lr_scheduler.py#L172"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.LRScheduler.get_last_lr" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return last computed learning rate by current scheduler.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -629,7 +629,7 @@ <h1>LRScheduler<a class="headerlink" href="#lrscheduler" title="Permalink to thi
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.lr_scheduler.LRScheduler.get_lr">
-<span class="sig-name descname"><span class="pre">get_lr</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/lr_scheduler.html#LRScheduler.get_lr"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L176"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.LRScheduler.get_lr" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">get_lr</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/lr_scheduler.html#LRScheduler.get_lr"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/lr_scheduler.py#L176"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.LRScheduler.get_lr" title="Permalink to this definition">¶</a></dt>
 <dd><p>Compute learning rate using chainable form of the scheduler.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -640,7 +640,7 @@ <h1>LRScheduler<a class="headerlink" href="#lrscheduler" title="Permalink to thi
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.lr_scheduler.LRScheduler.load_state_dict">
-<span class="sig-name descname"><span class="pre">load_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state_dict</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/lr_scheduler.html#LRScheduler.load_state_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L163"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.LRScheduler.load_state_dict" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">load_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state_dict</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/lr_scheduler.html#LRScheduler.load_state_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/lr_scheduler.py#L163"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.LRScheduler.load_state_dict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Load the scheduler’s state.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -652,7 +652,7 @@ <h1>LRScheduler<a class="headerlink" href="#lrscheduler" title="Permalink to thi
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.lr_scheduler.LRScheduler.print_lr">
-<span class="sig-name descname"><span class="pre">print_lr</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">is_verbose</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lr</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">epoch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/lr_scheduler.html#LRScheduler.print_lr"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L180"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.LRScheduler.print_lr" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">print_lr</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">is_verbose</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lr</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">epoch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/lr_scheduler.html#LRScheduler.print_lr"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/lr_scheduler.py#L180"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.LRScheduler.print_lr" title="Permalink to this definition">¶</a></dt>
 <dd><p>Display the current learning rate.</p>
 <div class="deprecated">
 <p><span class="versionmodified deprecated">Deprecated since version 2.4: </span><code class="docutils literal notranslate"><span class="pre">print_lr()</span></code> is deprecated. Please use <code class="docutils literal notranslate"><span class="pre">get_last_lr()</span></code> to access the
@@ -664,7 +664,7 @@ <h1>LRScheduler<a class="headerlink" href="#lrscheduler" title="Permalink to thi
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.lr_scheduler.LRScheduler.state_dict">
-<span class="sig-name descname"><span class="pre">state_dict</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/lr_scheduler.html#LRScheduler.state_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L153"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.LRScheduler.state_dict" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">state_dict</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/lr_scheduler.html#LRScheduler.state_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/lr_scheduler.py#L153"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.LRScheduler.state_dict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the state of the scheduler as a <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.13)"><code class="xref py py-class docutils literal notranslate"><span class="pre">dict</span></code></a>.</p>
 <p>It contains an entry for every variable in self.__dict__ which
 is not the optimizer.</p>
@@ -672,7 +672,7 @@ <h1>LRScheduler<a class="headerlink" href="#lrscheduler" title="Permalink to thi
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.lr_scheduler.LRScheduler.step">
-<span class="sig-name descname"><span class="pre">step</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">epoch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/lr_scheduler.html#LRScheduler.step"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L208"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.LRScheduler.step" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">step</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">epoch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/lr_scheduler.html#LRScheduler.step"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/lr_scheduler.py#L208"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.LRScheduler.step" title="Permalink to this definition">¶</a></dt>
 <dd><p>Perform a step.</p>
 <dl class="field-list simple">
 </dl>
diff --git a/main/generated/torch.optim.lr_scheduler.LambdaLR.html b/main/generated/torch.optim.lr_scheduler.LambdaLR.html
index d74bfbc20d5..1a51631af7e 100644
--- a/main/generated/torch.optim.lr_scheduler.LambdaLR.html
+++ b/main/generated/torch.optim.lr_scheduler.LambdaLR.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>LambdaLR<a class="headerlink" href="#lambdalr" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.optim.lr_scheduler.LambdaLR">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.optim.lr_scheduler.</span></span><span class="sig-name descname"><span class="pre">LambdaLR</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">optimizer</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lr_lambda</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">last_epoch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'deprecated'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/lr_scheduler.html#LambdaLR"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L286"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.LambdaLR" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.optim.lr_scheduler.</span></span><span class="sig-name descname"><span class="pre">LambdaLR</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">optimizer</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lr_lambda</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">last_epoch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'deprecated'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/lr_scheduler.html#LambdaLR"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/lr_scheduler.py#L286"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.LambdaLR" title="Permalink to this definition">¶</a></dt>
 <dd><p>Sets the initial learning rate.</p>
 <p>The learning rate of each parameter group is set to the initial lr
 times a given function. When last_epoch=-1, sets initial lr as lr.</p>
@@ -647,7 +647,7 @@ <h1>LambdaLR<a class="headerlink" href="#lambdalr" title="Permalink to this head
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.lr_scheduler.LambdaLR.get_last_lr">
-<span class="sig-name descname"><span class="pre">get_last_lr</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L172"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.LambdaLR.get_last_lr" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">get_last_lr</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/lr_scheduler.py#L172"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.LambdaLR.get_last_lr" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return last computed learning rate by current scheduler.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -658,13 +658,13 @@ <h1>LambdaLR<a class="headerlink" href="#lambdalr" title="Permalink to this head
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.lr_scheduler.LambdaLR.get_lr">
-<span class="sig-name descname"><span class="pre">get_lr</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/lr_scheduler.html#LambdaLR.get_lr"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L379"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.LambdaLR.get_lr" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">get_lr</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/lr_scheduler.html#LambdaLR.get_lr"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/lr_scheduler.py#L379"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.LambdaLR.get_lr" title="Permalink to this definition">¶</a></dt>
 <dd><p>Compute learning rate.</p>
 </dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.lr_scheduler.LambdaLR.load_state_dict">
-<span class="sig-name descname"><span class="pre">load_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state_dict</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/lr_scheduler.html#LambdaLR.load_state_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L360"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.LambdaLR.load_state_dict" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">load_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state_dict</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/lr_scheduler.html#LambdaLR.load_state_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/lr_scheduler.py#L360"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.LambdaLR.load_state_dict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Load the scheduler’s state.</p>
 <p>When saving or loading the scheduler, please make sure to also save or load the state of the optimizer.</p>
 <dl class="field-list simple">
@@ -677,7 +677,7 @@ <h1>LambdaLR<a class="headerlink" href="#lambdalr" title="Permalink to this head
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.lr_scheduler.LambdaLR.print_lr">
-<span class="sig-name descname"><span class="pre">print_lr</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">is_verbose</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lr</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">epoch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L180"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.LambdaLR.print_lr" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">print_lr</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">is_verbose</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lr</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">epoch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/lr_scheduler.py#L180"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.LambdaLR.print_lr" title="Permalink to this definition">¶</a></dt>
 <dd><p>Display the current learning rate.</p>
 <div class="deprecated">
 <p><span class="versionmodified deprecated">Deprecated since version 2.4: </span><code class="docutils literal notranslate"><span class="pre">print_lr()</span></code> is deprecated. Please use <code class="docutils literal notranslate"><span class="pre">get_last_lr()</span></code> to access the
@@ -689,7 +689,7 @@ <h1>LambdaLR<a class="headerlink" href="#lambdalr" title="Permalink to this head
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.lr_scheduler.LambdaLR.state_dict">
-<span class="sig-name descname"><span class="pre">state_dict</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/lr_scheduler.html#LambdaLR.state_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L337"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.LambdaLR.state_dict" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">state_dict</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/lr_scheduler.html#LambdaLR.state_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/lr_scheduler.py#L337"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.LambdaLR.state_dict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the state of the scheduler as a <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.13)"><code class="xref py py-class docutils literal notranslate"><span class="pre">dict</span></code></a>.</p>
 <p>It contains an entry for every variable in self.__dict__ which
 is not the optimizer.
@@ -700,7 +700,7 @@ <h1>LambdaLR<a class="headerlink" href="#lambdalr" title="Permalink to this head
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.lr_scheduler.LambdaLR.step">
-<span class="sig-name descname"><span class="pre">step</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">epoch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L208"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.LambdaLR.step" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">step</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">epoch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/lr_scheduler.py#L208"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.LambdaLR.step" title="Permalink to this definition">¶</a></dt>
 <dd><p>Perform a step.</p>
 <dl class="field-list simple">
 </dl>
diff --git a/main/generated/torch.optim.lr_scheduler.LinearLR.html b/main/generated/torch.optim.lr_scheduler.LinearLR.html
index ccc5bda32e6..a5ecb8ae115 100644
--- a/main/generated/torch.optim.lr_scheduler.LinearLR.html
+++ b/main/generated/torch.optim.lr_scheduler.LinearLR.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>LinearLR<a class="headerlink" href="#linearlr" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.optim.lr_scheduler.LinearLR">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.optim.lr_scheduler.</span></span><span class="sig-name descname"><span class="pre">LinearLR</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">optimizer</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">start_factor</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.3333333333333333</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">end_factor</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">total_iters</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">last_epoch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'deprecated'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/lr_scheduler.html#LinearLR"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L686"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.LinearLR" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.optim.lr_scheduler.</span></span><span class="sig-name descname"><span class="pre">LinearLR</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">optimizer</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">start_factor</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.3333333333333333</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">end_factor</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">total_iters</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">last_epoch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'deprecated'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/lr_scheduler.html#LinearLR"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/lr_scheduler.py#L686"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.LinearLR" title="Permalink to this definition">¶</a></dt>
 <dd><p>Decays the learning rate of each parameter group by linearly changing small multiplicative factor.</p>
 <p>The multiplication is done until the number of epoch reaches a pre-defined milestone: total_iters.
 Notice that such decay can happen simultaneously with other changes to the learning rate
@@ -655,7 +655,7 @@ <h1>LinearLR<a class="headerlink" href="#linearlr" title="Permalink to this head
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.lr_scheduler.LinearLR.get_last_lr">
-<span class="sig-name descname"><span class="pre">get_last_lr</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L172"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.LinearLR.get_last_lr" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">get_last_lr</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/lr_scheduler.py#L172"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.LinearLR.get_last_lr" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return last computed learning rate by current scheduler.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -666,13 +666,13 @@ <h1>LinearLR<a class="headerlink" href="#linearlr" title="Permalink to this head
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.lr_scheduler.LinearLR.get_lr">
-<span class="sig-name descname"><span class="pre">get_lr</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/lr_scheduler.html#LinearLR.get_lr"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L749"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.LinearLR.get_lr" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">get_lr</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/lr_scheduler.html#LinearLR.get_lr"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/lr_scheduler.py#L749"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.LinearLR.get_lr" title="Permalink to this definition">¶</a></dt>
 <dd><p>Compute the learning rate.</p>
 </dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.lr_scheduler.LinearLR.load_state_dict">
-<span class="sig-name descname"><span class="pre">load_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state_dict</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L163"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.LinearLR.load_state_dict" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">load_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state_dict</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/lr_scheduler.py#L163"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.LinearLR.load_state_dict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Load the scheduler’s state.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -684,7 +684,7 @@ <h1>LinearLR<a class="headerlink" href="#linearlr" title="Permalink to this head
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.lr_scheduler.LinearLR.print_lr">
-<span class="sig-name descname"><span class="pre">print_lr</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">is_verbose</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lr</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">epoch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L180"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.LinearLR.print_lr" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">print_lr</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">is_verbose</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lr</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">epoch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/lr_scheduler.py#L180"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.LinearLR.print_lr" title="Permalink to this definition">¶</a></dt>
 <dd><p>Display the current learning rate.</p>
 <div class="deprecated">
 <p><span class="versionmodified deprecated">Deprecated since version 2.4: </span><code class="docutils literal notranslate"><span class="pre">print_lr()</span></code> is deprecated. Please use <code class="docutils literal notranslate"><span class="pre">get_last_lr()</span></code> to access the
@@ -696,7 +696,7 @@ <h1>LinearLR<a class="headerlink" href="#linearlr" title="Permalink to this head
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.lr_scheduler.LinearLR.state_dict">
-<span class="sig-name descname"><span class="pre">state_dict</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L153"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.LinearLR.state_dict" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">state_dict</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/lr_scheduler.py#L153"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.LinearLR.state_dict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the state of the scheduler as a <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.13)"><code class="xref py py-class docutils literal notranslate"><span class="pre">dict</span></code></a>.</p>
 <p>It contains an entry for every variable in self.__dict__ which
 is not the optimizer.</p>
@@ -704,7 +704,7 @@ <h1>LinearLR<a class="headerlink" href="#linearlr" title="Permalink to this head
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.lr_scheduler.LinearLR.step">
-<span class="sig-name descname"><span class="pre">step</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">epoch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L208"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.LinearLR.step" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">step</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">epoch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/lr_scheduler.py#L208"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.LinearLR.step" title="Permalink to this definition">¶</a></dt>
 <dd><p>Perform a step.</p>
 <dl class="field-list simple">
 </dl>
diff --git a/main/generated/torch.optim.lr_scheduler.MultiStepLR.html b/main/generated/torch.optim.lr_scheduler.MultiStepLR.html
index 4b3a25acee1..c2c408dfc11 100644
--- a/main/generated/torch.optim.lr_scheduler.MultiStepLR.html
+++ b/main/generated/torch.optim.lr_scheduler.MultiStepLR.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>MultiStepLR<a class="headerlink" href="#multisteplr" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.optim.lr_scheduler.MultiStepLR">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.optim.lr_scheduler.</span></span><span class="sig-name descname"><span class="pre">MultiStepLR</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">optimizer</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">milestones</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">gamma</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">last_epoch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'deprecated'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/lr_scheduler.html#MultiStepLR"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L548"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.MultiStepLR" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.optim.lr_scheduler.</span></span><span class="sig-name descname"><span class="pre">MultiStepLR</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">optimizer</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">milestones</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">gamma</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">last_epoch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'deprecated'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/lr_scheduler.html#MultiStepLR"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/lr_scheduler.py#L548"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.MultiStepLR" title="Permalink to this definition">¶</a></dt>
 <dd><p>Decays the learning rate of each parameter group by gamma once the number of epoch reaches one of the milestones.</p>
 <p>Notice that such decay can happen simultaneously with other changes to the learning rate
 from outside this scheduler. When last_epoch=-1, sets initial lr as lr.</p>
@@ -648,7 +648,7 @@ <h1>MultiStepLR<a class="headerlink" href="#multisteplr" title="Permalink to thi
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.lr_scheduler.MultiStepLR.get_last_lr">
-<span class="sig-name descname"><span class="pre">get_last_lr</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L172"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.MultiStepLR.get_last_lr" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">get_last_lr</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/lr_scheduler.py#L172"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.MultiStepLR.get_last_lr" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return last computed learning rate by current scheduler.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -659,13 +659,13 @@ <h1>MultiStepLR<a class="headerlink" href="#multisteplr" title="Permalink to thi
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.lr_scheduler.MultiStepLR.get_lr">
-<span class="sig-name descname"><span class="pre">get_lr</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/lr_scheduler.html#MultiStepLR.get_lr"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L592"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.MultiStepLR.get_lr" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">get_lr</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/lr_scheduler.html#MultiStepLR.get_lr"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/lr_scheduler.py#L592"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.MultiStepLR.get_lr" title="Permalink to this definition">¶</a></dt>
 <dd><p>Compute the learning rate of each parameter group.</p>
 </dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.lr_scheduler.MultiStepLR.load_state_dict">
-<span class="sig-name descname"><span class="pre">load_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state_dict</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L163"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.MultiStepLR.load_state_dict" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">load_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state_dict</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/lr_scheduler.py#L163"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.MultiStepLR.load_state_dict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Load the scheduler’s state.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -677,7 +677,7 @@ <h1>MultiStepLR<a class="headerlink" href="#multisteplr" title="Permalink to thi
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.lr_scheduler.MultiStepLR.print_lr">
-<span class="sig-name descname"><span class="pre">print_lr</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">is_verbose</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lr</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">epoch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L180"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.MultiStepLR.print_lr" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">print_lr</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">is_verbose</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lr</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">epoch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/lr_scheduler.py#L180"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.MultiStepLR.print_lr" title="Permalink to this definition">¶</a></dt>
 <dd><p>Display the current learning rate.</p>
 <div class="deprecated">
 <p><span class="versionmodified deprecated">Deprecated since version 2.4: </span><code class="docutils literal notranslate"><span class="pre">print_lr()</span></code> is deprecated. Please use <code class="docutils literal notranslate"><span class="pre">get_last_lr()</span></code> to access the
@@ -689,7 +689,7 @@ <h1>MultiStepLR<a class="headerlink" href="#multisteplr" title="Permalink to thi
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.lr_scheduler.MultiStepLR.state_dict">
-<span class="sig-name descname"><span class="pre">state_dict</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L153"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.MultiStepLR.state_dict" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">state_dict</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/lr_scheduler.py#L153"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.MultiStepLR.state_dict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the state of the scheduler as a <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.13)"><code class="xref py py-class docutils literal notranslate"><span class="pre">dict</span></code></a>.</p>
 <p>It contains an entry for every variable in self.__dict__ which
 is not the optimizer.</p>
@@ -697,7 +697,7 @@ <h1>MultiStepLR<a class="headerlink" href="#multisteplr" title="Permalink to thi
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.lr_scheduler.MultiStepLR.step">
-<span class="sig-name descname"><span class="pre">step</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">epoch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L208"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.MultiStepLR.step" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">step</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">epoch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/lr_scheduler.py#L208"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.MultiStepLR.step" title="Permalink to this definition">¶</a></dt>
 <dd><p>Perform a step.</p>
 <dl class="field-list simple">
 </dl>
diff --git a/main/generated/torch.optim.lr_scheduler.MultiplicativeLR.html b/main/generated/torch.optim.lr_scheduler.MultiplicativeLR.html
index 3fc6465fbfe..5fa98514095 100644
--- a/main/generated/torch.optim.lr_scheduler.MultiplicativeLR.html
+++ b/main/generated/torch.optim.lr_scheduler.MultiplicativeLR.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>MultiplicativeLR<a class="headerlink" href="#multiplicativelr" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.optim.lr_scheduler.MultiplicativeLR">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.optim.lr_scheduler.</span></span><span class="sig-name descname"><span class="pre">MultiplicativeLR</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">optimizer</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lr_lambda</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">last_epoch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'deprecated'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/lr_scheduler.html#MultiplicativeLR"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L389"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.MultiplicativeLR" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.optim.lr_scheduler.</span></span><span class="sig-name descname"><span class="pre">MultiplicativeLR</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">optimizer</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lr_lambda</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">last_epoch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'deprecated'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/lr_scheduler.html#MultiplicativeLR"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/lr_scheduler.py#L389"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.MultiplicativeLR" title="Permalink to this definition">¶</a></dt>
 <dd><p>Multiply the learning rate of each parameter group by the factor given in the specified function.</p>
 <p>When last_epoch=-1, set initial lr as lr.</p>
 <dl class="field-list simple">
@@ -644,7 +644,7 @@ <h1>MultiplicativeLR<a class="headerlink" href="#multiplicativelr" title="Permal
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.lr_scheduler.MultiplicativeLR.get_last_lr">
-<span class="sig-name descname"><span class="pre">get_last_lr</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L172"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.MultiplicativeLR.get_last_lr" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">get_last_lr</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/lr_scheduler.py#L172"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.MultiplicativeLR.get_last_lr" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return last computed learning rate by current scheduler.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -655,13 +655,13 @@ <h1>MultiplicativeLR<a class="headerlink" href="#multiplicativelr" title="Permal
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.lr_scheduler.MultiplicativeLR.get_lr">
-<span class="sig-name descname"><span class="pre">get_lr</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/lr_scheduler.html#MultiplicativeLR.get_lr"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L475"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.MultiplicativeLR.get_lr" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">get_lr</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/lr_scheduler.html#MultiplicativeLR.get_lr"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/lr_scheduler.py#L475"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.MultiplicativeLR.get_lr" title="Permalink to this definition">¶</a></dt>
 <dd><p>Compute the learning rate of each parameter group.</p>
 </dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.lr_scheduler.MultiplicativeLR.load_state_dict">
-<span class="sig-name descname"><span class="pre">load_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state_dict</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/lr_scheduler.html#MultiplicativeLR.load_state_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L458"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.MultiplicativeLR.load_state_dict" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">load_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state_dict</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/lr_scheduler.html#MultiplicativeLR.load_state_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/lr_scheduler.py#L458"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.MultiplicativeLR.load_state_dict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Load the scheduler’s state.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -673,7 +673,7 @@ <h1>MultiplicativeLR<a class="headerlink" href="#multiplicativelr" title="Permal
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.lr_scheduler.MultiplicativeLR.print_lr">
-<span class="sig-name descname"><span class="pre">print_lr</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">is_verbose</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lr</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">epoch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L180"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.MultiplicativeLR.print_lr" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">print_lr</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">is_verbose</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lr</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">epoch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/lr_scheduler.py#L180"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.MultiplicativeLR.print_lr" title="Permalink to this definition">¶</a></dt>
 <dd><p>Display the current learning rate.</p>
 <div class="deprecated">
 <p><span class="versionmodified deprecated">Deprecated since version 2.4: </span><code class="docutils literal notranslate"><span class="pre">print_lr()</span></code> is deprecated. Please use <code class="docutils literal notranslate"><span class="pre">get_last_lr()</span></code> to access the
@@ -685,7 +685,7 @@ <h1>MultiplicativeLR<a class="headerlink" href="#multiplicativelr" title="Permal
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.lr_scheduler.MultiplicativeLR.state_dict">
-<span class="sig-name descname"><span class="pre">state_dict</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/lr_scheduler.html#MultiplicativeLR.state_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L437"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.MultiplicativeLR.state_dict" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">state_dict</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/lr_scheduler.html#MultiplicativeLR.state_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/lr_scheduler.py#L437"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.MultiplicativeLR.state_dict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the state of the scheduler as a <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.13)"><code class="xref py py-class docutils literal notranslate"><span class="pre">dict</span></code></a>.</p>
 <p>It contains an entry for every variable in self.__dict__ which
 is not the optimizer.
@@ -695,7 +695,7 @@ <h1>MultiplicativeLR<a class="headerlink" href="#multiplicativelr" title="Permal
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.lr_scheduler.MultiplicativeLR.step">
-<span class="sig-name descname"><span class="pre">step</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">epoch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L208"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.MultiplicativeLR.step" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">step</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">epoch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/lr_scheduler.py#L208"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.MultiplicativeLR.step" title="Permalink to this definition">¶</a></dt>
 <dd><p>Perform a step.</p>
 <dl class="field-list simple">
 </dl>
diff --git a/main/generated/torch.optim.lr_scheduler.OneCycleLR.html b/main/generated/torch.optim.lr_scheduler.OneCycleLR.html
index f61b78d5059..00bfb6d9c94 100644
--- a/main/generated/torch.optim.lr_scheduler.OneCycleLR.html
+++ b/main/generated/torch.optim.lr_scheduler.OneCycleLR.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>OneCycleLR<a class="headerlink" href="#onecyclelr" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.optim.lr_scheduler.OneCycleLR">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.optim.lr_scheduler.</span></span><span class="sig-name descname"><span class="pre">OneCycleLR</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">optimizer</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_lr</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">total_steps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">epochs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">steps_per_epoch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pct_start</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.3</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">anneal_strategy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'cos'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cycle_momentum</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">base_momentum</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.85</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_momentum</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.95</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">div_factor</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">25.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">final_div_factor</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10000.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">three_phase</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">last_epoch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'deprecated'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/lr_scheduler.html#OneCycleLR"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L1878"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.OneCycleLR" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.optim.lr_scheduler.</span></span><span class="sig-name descname"><span class="pre">OneCycleLR</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">optimizer</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_lr</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">total_steps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">epochs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">steps_per_epoch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pct_start</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.3</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">anneal_strategy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'cos'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cycle_momentum</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">base_momentum</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.85</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_momentum</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.95</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">div_factor</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">25.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">final_div_factor</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10000.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">three_phase</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">last_epoch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'deprecated'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/lr_scheduler.html#OneCycleLR"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/lr_scheduler.py#L1878"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.OneCycleLR" title="Permalink to this definition">¶</a></dt>
 <dd><p>Sets the learning rate of each parameter group according to the 1cycle learning rate policy.</p>
 <p>The 1cycle policy anneals the learning rate from an initial learning rate to some maximum
 learning rate and then from that maximum learning rate to some minimum learning rate much
@@ -715,7 +715,7 @@ <h1>OneCycleLR<a class="headerlink" href="#onecyclelr" title="Permalink to this
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.lr_scheduler.OneCycleLR.get_last_lr">
-<span class="sig-name descname"><span class="pre">get_last_lr</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L172"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.OneCycleLR.get_last_lr" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">get_last_lr</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/lr_scheduler.py#L172"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.OneCycleLR.get_last_lr" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return last computed learning rate by current scheduler.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -726,13 +726,13 @@ <h1>OneCycleLR<a class="headerlink" href="#onecyclelr" title="Permalink to this
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.lr_scheduler.OneCycleLR.get_lr">
-<span class="sig-name descname"><span class="pre">get_lr</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/lr_scheduler.html#OneCycleLR.get_lr"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L2142"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.OneCycleLR.get_lr" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">get_lr</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/lr_scheduler.html#OneCycleLR.get_lr"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/lr_scheduler.py#L2142"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.OneCycleLR.get_lr" title="Permalink to this definition">¶</a></dt>
 <dd><p>Compute the learning rate of each parameter group.</p>
 </dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.lr_scheduler.OneCycleLR.load_state_dict">
-<span class="sig-name descname"><span class="pre">load_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state_dict</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L163"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.OneCycleLR.load_state_dict" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">load_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state_dict</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/lr_scheduler.py#L163"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.OneCycleLR.load_state_dict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Load the scheduler’s state.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -744,7 +744,7 @@ <h1>OneCycleLR<a class="headerlink" href="#onecyclelr" title="Permalink to this
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.lr_scheduler.OneCycleLR.print_lr">
-<span class="sig-name descname"><span class="pre">print_lr</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">is_verbose</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lr</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">epoch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L180"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.OneCycleLR.print_lr" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">print_lr</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">is_verbose</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lr</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">epoch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/lr_scheduler.py#L180"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.OneCycleLR.print_lr" title="Permalink to this definition">¶</a></dt>
 <dd><p>Display the current learning rate.</p>
 <div class="deprecated">
 <p><span class="versionmodified deprecated">Deprecated since version 2.4: </span><code class="docutils literal notranslate"><span class="pre">print_lr()</span></code> is deprecated. Please use <code class="docutils literal notranslate"><span class="pre">get_last_lr()</span></code> to access the
@@ -756,7 +756,7 @@ <h1>OneCycleLR<a class="headerlink" href="#onecyclelr" title="Permalink to this
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.lr_scheduler.OneCycleLR.state_dict">
-<span class="sig-name descname"><span class="pre">state_dict</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L153"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.OneCycleLR.state_dict" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">state_dict</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/lr_scheduler.py#L153"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.OneCycleLR.state_dict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the state of the scheduler as a <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.13)"><code class="xref py py-class docutils literal notranslate"><span class="pre">dict</span></code></a>.</p>
 <p>It contains an entry for every variable in self.__dict__ which
 is not the optimizer.</p>
@@ -764,7 +764,7 @@ <h1>OneCycleLR<a class="headerlink" href="#onecyclelr" title="Permalink to this
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.lr_scheduler.OneCycleLR.step">
-<span class="sig-name descname"><span class="pre">step</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">epoch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L208"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.OneCycleLR.step" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">step</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">epoch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/lr_scheduler.py#L208"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.OneCycleLR.step" title="Permalink to this definition">¶</a></dt>
 <dd><p>Perform a step.</p>
 <dl class="field-list simple">
 </dl>
diff --git a/main/generated/torch.optim.lr_scheduler.PolynomialLR.html b/main/generated/torch.optim.lr_scheduler.PolynomialLR.html
index 2f66f1d4f53..bd451560de8 100644
--- a/main/generated/torch.optim.lr_scheduler.PolynomialLR.html
+++ b/main/generated/torch.optim.lr_scheduler.PolynomialLR.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>PolynomialLR<a class="headerlink" href="#polynomiallr" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.optim.lr_scheduler.PolynomialLR">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.optim.lr_scheduler.</span></span><span class="sig-name descname"><span class="pre">PolynomialLR</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">optimizer</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">total_iters</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">power</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">last_epoch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'deprecated'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/lr_scheduler.html#PolynomialLR"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L976"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.PolynomialLR" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.optim.lr_scheduler.</span></span><span class="sig-name descname"><span class="pre">PolynomialLR</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">optimizer</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">total_iters</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">power</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">last_epoch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'deprecated'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/lr_scheduler.html#PolynomialLR"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/lr_scheduler.py#L976"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.PolynomialLR" title="Permalink to this definition">¶</a></dt>
 <dd><p>Decays the learning rate of each parameter group using a polynomial function in the given total_iters.</p>
 <p>When last_epoch=-1, sets initial lr as lr.</p>
 <dl class="field-list simple">
@@ -647,7 +647,7 @@ <h1>PolynomialLR<a class="headerlink" href="#polynomiallr" title="Permalink to t
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.lr_scheduler.PolynomialLR.get_last_lr">
-<span class="sig-name descname"><span class="pre">get_last_lr</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L172"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.PolynomialLR.get_last_lr" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">get_last_lr</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/lr_scheduler.py#L172"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.PolynomialLR.get_last_lr" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return last computed learning rate by current scheduler.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -658,13 +658,13 @@ <h1>PolynomialLR<a class="headerlink" href="#polynomiallr" title="Permalink to t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.lr_scheduler.PolynomialLR.get_lr">
-<span class="sig-name descname"><span class="pre">get_lr</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/lr_scheduler.html#PolynomialLR.get_lr"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L1019"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.PolynomialLR.get_lr" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">get_lr</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/lr_scheduler.html#PolynomialLR.get_lr"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/lr_scheduler.py#L1019"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.PolynomialLR.get_lr" title="Permalink to this definition">¶</a></dt>
 <dd><p>Compute the learning rate.</p>
 </dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.lr_scheduler.PolynomialLR.load_state_dict">
-<span class="sig-name descname"><span class="pre">load_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state_dict</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L163"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.PolynomialLR.load_state_dict" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">load_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state_dict</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/lr_scheduler.py#L163"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.PolynomialLR.load_state_dict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Load the scheduler’s state.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -676,7 +676,7 @@ <h1>PolynomialLR<a class="headerlink" href="#polynomiallr" title="Permalink to t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.lr_scheduler.PolynomialLR.print_lr">
-<span class="sig-name descname"><span class="pre">print_lr</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">is_verbose</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lr</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">epoch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L180"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.PolynomialLR.print_lr" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">print_lr</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">is_verbose</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lr</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">epoch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/lr_scheduler.py#L180"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.PolynomialLR.print_lr" title="Permalink to this definition">¶</a></dt>
 <dd><p>Display the current learning rate.</p>
 <div class="deprecated">
 <p><span class="versionmodified deprecated">Deprecated since version 2.4: </span><code class="docutils literal notranslate"><span class="pre">print_lr()</span></code> is deprecated. Please use <code class="docutils literal notranslate"><span class="pre">get_last_lr()</span></code> to access the
@@ -688,7 +688,7 @@ <h1>PolynomialLR<a class="headerlink" href="#polynomiallr" title="Permalink to t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.lr_scheduler.PolynomialLR.state_dict">
-<span class="sig-name descname"><span class="pre">state_dict</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L153"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.PolynomialLR.state_dict" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">state_dict</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/lr_scheduler.py#L153"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.PolynomialLR.state_dict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the state of the scheduler as a <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.13)"><code class="xref py py-class docutils literal notranslate"><span class="pre">dict</span></code></a>.</p>
 <p>It contains an entry for every variable in self.__dict__ which
 is not the optimizer.</p>
@@ -696,7 +696,7 @@ <h1>PolynomialLR<a class="headerlink" href="#polynomiallr" title="Permalink to t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.lr_scheduler.PolynomialLR.step">
-<span class="sig-name descname"><span class="pre">step</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">epoch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L208"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.PolynomialLR.step" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">step</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">epoch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/lr_scheduler.py#L208"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.PolynomialLR.step" title="Permalink to this definition">¶</a></dt>
 <dd><p>Perform a step.</p>
 <dl class="field-list simple">
 </dl>
diff --git a/main/generated/torch.optim.lr_scheduler.ReduceLROnPlateau.html b/main/generated/torch.optim.lr_scheduler.ReduceLROnPlateau.html
index a624b596c24..db1fb1ca6ab 100644
--- a/main/generated/torch.optim.lr_scheduler.ReduceLROnPlateau.html
+++ b/main/generated/torch.optim.lr_scheduler.ReduceLROnPlateau.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>ReduceLROnPlateau<a class="headerlink" href="#reducelronplateau" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.optim.lr_scheduler.ReduceLROnPlateau">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.optim.lr_scheduler.</span></span><span class="sig-name descname"><span class="pre">ReduceLROnPlateau</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">optimizer</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'min'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">factor</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">patience</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">threshold</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">threshold_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'rel'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cooldown</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_lr</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-08</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'deprecated'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/lr_scheduler.html#ReduceLROnPlateau"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L1241"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.ReduceLROnPlateau" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.optim.lr_scheduler.</span></span><span class="sig-name descname"><span class="pre">ReduceLROnPlateau</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">optimizer</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'min'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">factor</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">patience</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">threshold</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0001</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">threshold_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'rel'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cooldown</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_lr</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-08</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'deprecated'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/lr_scheduler.html#ReduceLROnPlateau"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/lr_scheduler.py#L1241"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.ReduceLROnPlateau" title="Permalink to this definition">¶</a></dt>
 <dd><p>Reduce learning rate when a metric has stopped improving.</p>
 <p>Models often benefit from reducing the learning rate by a factor
 of 2-10 once learning stagnates. This scheduler reads a metrics
@@ -677,7 +677,7 @@ <h1>ReduceLROnPlateau<a class="headerlink" href="#reducelronplateau" title="Perm
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.lr_scheduler.ReduceLROnPlateau.get_last_lr">
-<span class="sig-name descname"><span class="pre">get_last_lr</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L172"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.ReduceLROnPlateau.get_last_lr" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">get_last_lr</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/lr_scheduler.py#L172"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.ReduceLROnPlateau.get_last_lr" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return last computed learning rate by current scheduler.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -688,7 +688,7 @@ <h1>ReduceLROnPlateau<a class="headerlink" href="#reducelronplateau" title="Perm
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.lr_scheduler.ReduceLROnPlateau.get_lr">
-<span class="sig-name descname"><span class="pre">get_lr</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L176"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.ReduceLROnPlateau.get_lr" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">get_lr</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/lr_scheduler.py#L176"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.ReduceLROnPlateau.get_lr" title="Permalink to this definition">¶</a></dt>
 <dd><p>Compute learning rate using chainable form of the scheduler.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -699,13 +699,13 @@ <h1>ReduceLROnPlateau<a class="headerlink" href="#reducelronplateau" title="Perm
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.lr_scheduler.ReduceLROnPlateau.load_state_dict">
-<span class="sig-name descname"><span class="pre">load_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state_dict</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/lr_scheduler.html#ReduceLROnPlateau.load_state_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L1447"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.ReduceLROnPlateau.load_state_dict" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">load_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state_dict</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/lr_scheduler.html#ReduceLROnPlateau.load_state_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/lr_scheduler.py#L1447"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.ReduceLROnPlateau.load_state_dict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Load the scheduler’s state.</p>
 </dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.lr_scheduler.ReduceLROnPlateau.print_lr">
-<span class="sig-name descname"><span class="pre">print_lr</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">is_verbose</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lr</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">epoch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L180"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.ReduceLROnPlateau.print_lr" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">print_lr</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">is_verbose</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lr</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">epoch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/lr_scheduler.py#L180"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.ReduceLROnPlateau.print_lr" title="Permalink to this definition">¶</a></dt>
 <dd><p>Display the current learning rate.</p>
 <div class="deprecated">
 <p><span class="versionmodified deprecated">Deprecated since version 2.4: </span><code class="docutils literal notranslate"><span class="pre">print_lr()</span></code> is deprecated. Please use <code class="docutils literal notranslate"><span class="pre">get_last_lr()</span></code> to access the
@@ -717,7 +717,7 @@ <h1>ReduceLROnPlateau<a class="headerlink" href="#reducelronplateau" title="Perm
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.lr_scheduler.ReduceLROnPlateau.step">
-<span class="sig-name descname"><span class="pre">step</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">metrics</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">epoch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/lr_scheduler.html#ReduceLROnPlateau.step"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L1360"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.ReduceLROnPlateau.step" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">step</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">metrics</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">epoch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/lr_scheduler.html#ReduceLROnPlateau.step"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/lr_scheduler.py#L1360"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.ReduceLROnPlateau.step" title="Permalink to this definition">¶</a></dt>
 <dd><p>Perform a step.</p>
 <dl class="field-list simple">
 </dl>
diff --git a/main/generated/torch.optim.lr_scheduler.SequentialLR.html b/main/generated/torch.optim.lr_scheduler.SequentialLR.html
index b0d9d156663..1b62d4dd5ec 100644
--- a/main/generated/torch.optim.lr_scheduler.SequentialLR.html
+++ b/main/generated/torch.optim.lr_scheduler.SequentialLR.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>SequentialLR<a class="headerlink" href="#sequentiallr" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.optim.lr_scheduler.SequentialLR">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.optim.lr_scheduler.</span></span><span class="sig-name descname"><span class="pre">SequentialLR</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">optimizer</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">schedulers</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">milestones</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">last_epoch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'deprecated'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/lr_scheduler.html#SequentialLR"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L826"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.SequentialLR" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.optim.lr_scheduler.</span></span><span class="sig-name descname"><span class="pre">SequentialLR</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">optimizer</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">schedulers</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">milestones</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">last_epoch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'deprecated'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/lr_scheduler.html#SequentialLR"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/lr_scheduler.py#L826"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.SequentialLR" title="Permalink to this definition">¶</a></dt>
 <dd><p>Contains a list of schedulers expected to be called sequentially during the optimization process.</p>
 <p>Specifically, the schedulers will be called according to the milestone points, which should provide exact
 intervals by which each scheduler should be called at a given epoch.</p>
@@ -650,7 +650,7 @@ <h1>SequentialLR<a class="headerlink" href="#sequentiallr" title="Permalink to t
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.lr_scheduler.SequentialLR.get_last_lr">
-<span class="sig-name descname"><span class="pre">get_last_lr</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L172"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.SequentialLR.get_last_lr" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">get_last_lr</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/lr_scheduler.py#L172"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.SequentialLR.get_last_lr" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return last computed learning rate by current scheduler.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -661,7 +661,7 @@ <h1>SequentialLR<a class="headerlink" href="#sequentiallr" title="Permalink to t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.lr_scheduler.SequentialLR.get_lr">
-<span class="sig-name descname"><span class="pre">get_lr</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L176"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.SequentialLR.get_lr" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">get_lr</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/lr_scheduler.py#L176"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.SequentialLR.get_lr" title="Permalink to this definition">¶</a></dt>
 <dd><p>Compute learning rate using chainable form of the scheduler.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -672,7 +672,7 @@ <h1>SequentialLR<a class="headerlink" href="#sequentiallr" title="Permalink to t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.lr_scheduler.SequentialLR.load_state_dict">
-<span class="sig-name descname"><span class="pre">load_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state_dict</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/lr_scheduler.html#SequentialLR.load_state_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L959"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.SequentialLR.load_state_dict" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">load_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state_dict</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/lr_scheduler.html#SequentialLR.load_state_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/lr_scheduler.py#L959"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.SequentialLR.load_state_dict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Load the scheduler’s state.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -684,7 +684,7 @@ <h1>SequentialLR<a class="headerlink" href="#sequentiallr" title="Permalink to t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.lr_scheduler.SequentialLR.print_lr">
-<span class="sig-name descname"><span class="pre">print_lr</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">is_verbose</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lr</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">epoch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L180"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.SequentialLR.print_lr" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">print_lr</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">is_verbose</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lr</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">epoch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/lr_scheduler.py#L180"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.SequentialLR.print_lr" title="Permalink to this definition">¶</a></dt>
 <dd><p>Display the current learning rate.</p>
 <div class="deprecated">
 <p><span class="versionmodified deprecated">Deprecated since version 2.4: </span><code class="docutils literal notranslate"><span class="pre">print_lr()</span></code> is deprecated. Please use <code class="docutils literal notranslate"><span class="pre">get_last_lr()</span></code> to access the
@@ -696,14 +696,14 @@ <h1>SequentialLR<a class="headerlink" href="#sequentiallr" title="Permalink to t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.lr_scheduler.SequentialLR.recursive_undo">
-<span class="sig-name descname"><span class="pre">recursive_undo</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sched</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/lr_scheduler.html#SequentialLR.recursive_undo"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L915"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.SequentialLR.recursive_undo" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">recursive_undo</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">sched</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/lr_scheduler.html#SequentialLR.recursive_undo"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/lr_scheduler.py#L915"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.SequentialLR.recursive_undo" title="Permalink to this definition">¶</a></dt>
 <dd><p>Recursively undo any step performed by the initialisation of
 schedulers.</p>
 </dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.lr_scheduler.SequentialLR.state_dict">
-<span class="sig-name descname"><span class="pre">state_dict</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/lr_scheduler.html#SequentialLR.state_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L940"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.SequentialLR.state_dict" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">state_dict</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/lr_scheduler.html#SequentialLR.state_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/lr_scheduler.py#L940"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.SequentialLR.state_dict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the state of the scheduler as a <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.13)"><code class="xref py py-class docutils literal notranslate"><span class="pre">dict</span></code></a>.</p>
 <p>It contains an entry for every variable in self.__dict__ which
 is not the optimizer.
@@ -712,7 +712,7 @@ <h1>SequentialLR<a class="headerlink" href="#sequentiallr" title="Permalink to t
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.lr_scheduler.SequentialLR.step">
-<span class="sig-name descname"><span class="pre">step</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/lr_scheduler.html#SequentialLR.step"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L928"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.SequentialLR.step" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">step</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/lr_scheduler.html#SequentialLR.step"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/lr_scheduler.py#L928"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.SequentialLR.step" title="Permalink to this definition">¶</a></dt>
 <dd><p>Perform a step.</p>
 </dd></dl>
 
diff --git a/main/generated/torch.optim.lr_scheduler.StepLR.html b/main/generated/torch.optim.lr_scheduler.StepLR.html
index 7b60c0dbaba..8b9544e41bd 100644
--- a/main/generated/torch.optim.lr_scheduler.StepLR.html
+++ b/main/generated/torch.optim.lr_scheduler.StepLR.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>StepLR<a class="headerlink" href="#steplr" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.optim.lr_scheduler.StepLR">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.optim.lr_scheduler.</span></span><span class="sig-name descname"><span class="pre">StepLR</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">optimizer</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">step_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">gamma</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">last_epoch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'deprecated'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/lr_scheduler.html#StepLR"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L488"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.StepLR" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.optim.lr_scheduler.</span></span><span class="sig-name descname"><span class="pre">StepLR</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">optimizer</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">step_size</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">gamma</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">last_epoch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'deprecated'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/lr_scheduler.html#StepLR"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/lr_scheduler.py#L488"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.StepLR" title="Permalink to this definition">¶</a></dt>
 <dd><p>Decays the learning rate of each parameter group by gamma every step_size epochs.</p>
 <p>Notice that such decay can happen simultaneously with other changes to the learning rate
 from outside this scheduler. When last_epoch=-1, sets initial lr as lr.</p>
@@ -649,7 +649,7 @@ <h1>StepLR<a class="headerlink" href="#steplr" title="Permalink to this heading"
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.lr_scheduler.StepLR.get_last_lr">
-<span class="sig-name descname"><span class="pre">get_last_lr</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L172"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.StepLR.get_last_lr" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">get_last_lr</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/lr_scheduler.py#L172"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.StepLR.get_last_lr" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return last computed learning rate by current scheduler.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -660,13 +660,13 @@ <h1>StepLR<a class="headerlink" href="#steplr" title="Permalink to this heading"
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.lr_scheduler.StepLR.get_lr">
-<span class="sig-name descname"><span class="pre">get_lr</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/lr_scheduler.html#StepLR.get_lr"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L533"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.StepLR.get_lr" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">get_lr</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/lr_scheduler.html#StepLR.get_lr"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/lr_scheduler.py#L533"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.StepLR.get_lr" title="Permalink to this definition">¶</a></dt>
 <dd><p>Compute the learning rate of each parameter group.</p>
 </dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.lr_scheduler.StepLR.load_state_dict">
-<span class="sig-name descname"><span class="pre">load_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state_dict</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L163"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.StepLR.load_state_dict" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">load_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state_dict</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/lr_scheduler.py#L163"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.StepLR.load_state_dict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Load the scheduler’s state.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -678,7 +678,7 @@ <h1>StepLR<a class="headerlink" href="#steplr" title="Permalink to this heading"
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.lr_scheduler.StepLR.print_lr">
-<span class="sig-name descname"><span class="pre">print_lr</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">is_verbose</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lr</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">epoch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L180"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.StepLR.print_lr" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">print_lr</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">is_verbose</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lr</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">epoch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/lr_scheduler.py#L180"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.StepLR.print_lr" title="Permalink to this definition">¶</a></dt>
 <dd><p>Display the current learning rate.</p>
 <div class="deprecated">
 <p><span class="versionmodified deprecated">Deprecated since version 2.4: </span><code class="docutils literal notranslate"><span class="pre">print_lr()</span></code> is deprecated. Please use <code class="docutils literal notranslate"><span class="pre">get_last_lr()</span></code> to access the
@@ -690,7 +690,7 @@ <h1>StepLR<a class="headerlink" href="#steplr" title="Permalink to this heading"
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.lr_scheduler.StepLR.state_dict">
-<span class="sig-name descname"><span class="pre">state_dict</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L153"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.StepLR.state_dict" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">state_dict</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/lr_scheduler.py#L153"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.StepLR.state_dict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the state of the scheduler as a <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.13)"><code class="xref py py-class docutils literal notranslate"><span class="pre">dict</span></code></a>.</p>
 <p>It contains an entry for every variable in self.__dict__ which
 is not the optimizer.</p>
@@ -698,7 +698,7 @@ <h1>StepLR<a class="headerlink" href="#steplr" title="Permalink to this heading"
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.lr_scheduler.StepLR.step">
-<span class="sig-name descname"><span class="pre">step</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">epoch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L208"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.StepLR.step" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">step</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">epoch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/lr_scheduler.py#L208"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.lr_scheduler.StepLR.step" title="Permalink to this definition">¶</a></dt>
 <dd><p>Perform a step.</p>
 <dl class="field-list simple">
 </dl>
diff --git a/main/generated/torch.optim.swa_utils.AveragedModel.html b/main/generated/torch.optim.swa_utils.AveragedModel.html
index e66cb01b961..567312b651e 100644
--- a/main/generated/torch.optim.swa_utils.AveragedModel.html
+++ b/main/generated/torch.optim.swa_utils.AveragedModel.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>AveragedModel<a class="headerlink" href="#averagedmodel" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.optim.swa_utils.AveragedModel">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.optim.swa_utils.</span></span><span class="sig-name descname"><span class="pre">AveragedModel</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">avg_fn</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">multi_avg_fn</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_buffers</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/swa_utils.html#AveragedModel"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/swa_utils.py#L116"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.AveragedModel" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.optim.swa_utils.</span></span><span class="sig-name descname"><span class="pre">AveragedModel</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">avg_fn</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">multi_avg_fn</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_buffers</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/swa_utils.html#AveragedModel"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/swa_utils.py#L116"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.AveragedModel" title="Permalink to this definition">¶</a></dt>
 <dd><p>Implements averaged model for Stochastic Weight Averaging (SWA) and Exponential Moving Average (EMA).</p>
 <p>Stochastic Weight Averaging was proposed in <a class="reference external" href="https://arxiv.org/abs/1803.05407">Averaging Weights Leads to
 Wider Optima and Better Generalization</a> by Pavel Izmailov, Dmitrii
@@ -701,7 +701,7 @@ <h1>AveragedModel<a class="headerlink" href="#averagedmodel" title="Permalink to
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.swa_utils.AveragedModel.add_module">
-<span class="sig-name descname"><span class="pre">add_module</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">module</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L622"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.AveragedModel.add_module" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">add_module</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">module</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/module.py#L622"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.AveragedModel.add_module" title="Permalink to this definition">¶</a></dt>
 <dd><p>Add a child module to the current module.</p>
 <p>The module can be accessed as an attribute using the given name.</p>
 <dl class="field-list simple">
@@ -717,7 +717,7 @@ <h1>AveragedModel<a class="headerlink" href="#averagedmodel" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.swa_utils.AveragedModel.apply">
-<span class="sig-name descname"><span class="pre">apply</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">fn</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L1005"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.AveragedModel.apply" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">apply</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">fn</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/module.py#L1005"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.AveragedModel.apply" title="Permalink to this definition">¶</a></dt>
 <dd><p>Apply <code class="docutils literal notranslate"><span class="pre">fn</span></code> recursively to every submodule (as returned by <code class="docutils literal notranslate"><span class="pre">.children()</span></code>) as well as self.</p>
 <p>Typical use includes initializing the parameters of a model
 (see also <a class="reference internal" href="../nn.init.html#nn-init-doc"><span class="std std-ref">torch.nn.init</span></a>).</p>
@@ -759,7 +759,7 @@ <h1>AveragedModel<a class="headerlink" href="#averagedmodel" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.swa_utils.AveragedModel.bfloat16">
-<span class="sig-name descname"><span class="pre">bfloat16</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L1180"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.AveragedModel.bfloat16" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">bfloat16</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/module.py#L1180"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.AveragedModel.bfloat16" title="Permalink to this definition">¶</a></dt>
 <dd><p>Casts all floating point parameters and buffers to <code class="docutils literal notranslate"><span class="pre">bfloat16</span></code> datatype.</p>
 <div class="admonition note">
 <p class="admonition-title">Note</p>
@@ -777,7 +777,7 @@ <h1>AveragedModel<a class="headerlink" href="#averagedmodel" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.swa_utils.AveragedModel.buffers">
-<span class="sig-name descname"><span class="pre">buffers</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">recurse</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L2675"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.AveragedModel.buffers" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">buffers</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">recurse</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/module.py#L2675"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.AveragedModel.buffers" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return an iterator over module buffers.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -803,7 +803,7 @@ <h1>AveragedModel<a class="headerlink" href="#averagedmodel" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.swa_utils.AveragedModel.children">
-<span class="sig-name descname"><span class="pre">children</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L2729"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.AveragedModel.children" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">children</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/module.py#L2729"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.AveragedModel.children" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return an iterator over immediate children modules.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Yields</dt>
@@ -817,7 +817,7 @@ <h1>AveragedModel<a class="headerlink" href="#averagedmodel" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.swa_utils.AveragedModel.compile">
-<span class="sig-name descname"><span class="pre">compile</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L2992"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.AveragedModel.compile" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">compile</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/module.py#L2992"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.AveragedModel.compile" title="Permalink to this definition">¶</a></dt>
 <dd><p>Compile this Module’s forward using <a class="reference internal" href="torch.compile.html#torch.compile" title="torch.compile"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.compile()</span></code></a>.</p>
 <p>This Module’s <cite>__call__</cite> method is compiled and all arguments are passed as-is
 to <a class="reference internal" href="torch.compile.html#torch.compile" title="torch.compile"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.compile()</span></code></a>.</p>
@@ -826,7 +826,7 @@ <h1>AveragedModel<a class="headerlink" href="#averagedmodel" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.swa_utils.AveragedModel.cpu">
-<span class="sig-name descname"><span class="pre">cpu</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L1122"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.AveragedModel.cpu" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">cpu</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/module.py#L1122"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.AveragedModel.cpu" title="Permalink to this definition">¶</a></dt>
 <dd><p>Move all model parameters and buffers to the CPU.</p>
 <div class="admonition note">
 <p class="admonition-title">Note</p>
@@ -844,7 +844,7 @@ <h1>AveragedModel<a class="headerlink" href="#averagedmodel" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.swa_utils.AveragedModel.cuda">
-<span class="sig-name descname"><span class="pre">cuda</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L1046"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.AveragedModel.cuda" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">cuda</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/module.py#L1046"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.AveragedModel.cuda" title="Permalink to this definition">¶</a></dt>
 <dd><p>Move all model parameters and buffers to the GPU.</p>
 <p>This also makes associated parameters and buffers different objects. So
 it should be called before constructing the optimizer if the module will
@@ -869,7 +869,7 @@ <h1>AveragedModel<a class="headerlink" href="#averagedmodel" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.swa_utils.AveragedModel.double">
-<span class="sig-name descname"><span class="pre">double</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L1158"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.AveragedModel.double" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">double</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/module.py#L1158"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.AveragedModel.double" title="Permalink to this definition">¶</a></dt>
 <dd><p>Casts all floating point parameters and buffers to <code class="docutils literal notranslate"><span class="pre">double</span></code> datatype.</p>
 <div class="admonition note">
 <p class="admonition-title">Note</p>
@@ -887,7 +887,7 @@ <h1>AveragedModel<a class="headerlink" href="#averagedmodel" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.swa_utils.AveragedModel.eval">
-<span class="sig-name descname"><span class="pre">eval</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L2856"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.AveragedModel.eval" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">eval</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/module.py#L2856"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.AveragedModel.eval" title="Permalink to this definition">¶</a></dt>
 <dd><p>Set the module in evaluation mode.</p>
 <p>This has an effect only on certain modules. See the documentation of
 particular modules for details of their behaviors in training/evaluation
@@ -908,7 +908,7 @@ <h1>AveragedModel<a class="headerlink" href="#averagedmodel" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.swa_utils.AveragedModel.extra_repr">
-<span class="sig-name descname"><span class="pre">extra_repr</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L2932"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.AveragedModel.extra_repr" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">extra_repr</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/module.py#L2932"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.AveragedModel.extra_repr" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the extra representation of the module.</p>
 <p>To print customized extra information, you should re-implement
 this method in your own modules. Both single-line and multi-line
@@ -922,7 +922,7 @@ <h1>AveragedModel<a class="headerlink" href="#averagedmodel" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.swa_utils.AveragedModel.float">
-<span class="sig-name descname"><span class="pre">float</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L1147"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.AveragedModel.float" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">float</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/module.py#L1147"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.AveragedModel.float" title="Permalink to this definition">¶</a></dt>
 <dd><p>Casts all floating point parameters and buffers to <code class="docutils literal notranslate"><span class="pre">float</span></code> datatype.</p>
 <div class="admonition note">
 <p class="admonition-title">Note</p>
@@ -940,13 +940,13 @@ <h1>AveragedModel<a class="headerlink" href="#averagedmodel" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.swa_utils.AveragedModel.forward">
-<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/swa_utils.html#AveragedModel.forward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/swa_utils.py#L241"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.AveragedModel.forward" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/swa_utils.html#AveragedModel.forward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/swa_utils.py#L241"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.AveragedModel.forward" title="Permalink to this definition">¶</a></dt>
 <dd><p>Forward pass.</p>
 </dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.swa_utils.AveragedModel.get_buffer">
-<span class="sig-name descname"><span class="pre">get_buffer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">target</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L836"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.AveragedModel.get_buffer" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">get_buffer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">target</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/module.py#L836"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.AveragedModel.get_buffer" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the buffer given by <code class="docutils literal notranslate"><span class="pre">target</span></code> if it exists, otherwise throw an error.</p>
 <p>See the docstring for <code class="docutils literal notranslate"><span class="pre">get_submodule</span></code> for a more detailed
 explanation of this method’s functionality as well as how to
@@ -973,7 +973,7 @@ <h1>AveragedModel<a class="headerlink" href="#averagedmodel" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.swa_utils.AveragedModel.get_extra_state">
-<span class="sig-name descname"><span class="pre">get_extra_state</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L872"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.AveragedModel.get_extra_state" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">get_extra_state</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/module.py#L872"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.AveragedModel.get_extra_state" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return any extra state to include in the module’s state_dict.</p>
 <p>Implement this and a corresponding <a class="reference internal" href="#torch.optim.swa_utils.AveragedModel.set_extra_state" title="torch.optim.swa_utils.AveragedModel.set_extra_state"><code class="xref py py-func docutils literal notranslate"><span class="pre">set_extra_state()</span></code></a> for your module
 if you need to store extra state. This function is called when building the
@@ -994,7 +994,7 @@ <h1>AveragedModel<a class="headerlink" href="#averagedmodel" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.swa_utils.AveragedModel.get_parameter">
-<span class="sig-name descname"><span class="pre">get_parameter</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">target</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L800"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.AveragedModel.get_parameter" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">get_parameter</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">target</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/module.py#L800"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.AveragedModel.get_parameter" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the parameter given by <code class="docutils literal notranslate"><span class="pre">target</span></code> if it exists, otherwise throw an error.</p>
 <p>See the docstring for <code class="docutils literal notranslate"><span class="pre">get_submodule</span></code> for a more detailed
 explanation of this method’s functionality as well as how to
@@ -1021,7 +1021,7 @@ <h1>AveragedModel<a class="headerlink" href="#averagedmodel" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.swa_utils.AveragedModel.get_submodule">
-<span class="sig-name descname"><span class="pre">get_submodule</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">target</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L654"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.AveragedModel.get_submodule" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">get_submodule</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">target</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/module.py#L654"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.AveragedModel.get_submodule" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the submodule given by <code class="docutils literal notranslate"><span class="pre">target</span></code> if it exists, otherwise throw an error.</p>
 <p>For example, let’s say you have an <code class="docutils literal notranslate"><span class="pre">nn.Module</span></code> <code class="docutils literal notranslate"><span class="pre">A</span></code> that
 looks like this:</p>
@@ -1070,7 +1070,7 @@ <h1>AveragedModel<a class="headerlink" href="#averagedmodel" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.swa_utils.AveragedModel.half">
-<span class="sig-name descname"><span class="pre">half</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L1169"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.AveragedModel.half" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">half</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/module.py#L1169"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.AveragedModel.half" title="Permalink to this definition">¶</a></dt>
 <dd><p>Casts all floating point parameters and buffers to <code class="docutils literal notranslate"><span class="pre">half</span></code> datatype.</p>
 <div class="admonition note">
 <p class="admonition-title">Note</p>
@@ -1088,7 +1088,7 @@ <h1>AveragedModel<a class="headerlink" href="#averagedmodel" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.swa_utils.AveragedModel.ipu">
-<span class="sig-name descname"><span class="pre">ipu</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L1065"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.AveragedModel.ipu" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">ipu</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/module.py#L1065"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.AveragedModel.ipu" title="Permalink to this definition">¶</a></dt>
 <dd><p>Move all model parameters and buffers to the IPU.</p>
 <p>This also makes associated parameters and buffers different objects. So
 it should be called before constructing the optimizer if the module will
@@ -1113,7 +1113,7 @@ <h1>AveragedModel<a class="headerlink" href="#averagedmodel" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.swa_utils.AveragedModel.load_state_dict">
-<span class="sig-name descname"><span class="pre">load_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state_dict</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">strict</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">assign</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L2483"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.AveragedModel.load_state_dict" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">load_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state_dict</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">strict</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">assign</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/module.py#L2483"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.AveragedModel.load_state_dict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Copy parameters and buffers from <a class="reference internal" href="#torch.optim.swa_utils.AveragedModel.state_dict" title="torch.optim.swa_utils.AveragedModel.state_dict"><code class="xref py py-attr docutils literal notranslate"><span class="pre">state_dict</span></code></a> into this module and its descendants.</p>
 <p>If <code class="xref py py-attr docutils literal notranslate"><span class="pre">strict</span></code> is <code class="docutils literal notranslate"><span class="pre">True</span></code>, then
 the keys of <a class="reference internal" href="#torch.optim.swa_utils.AveragedModel.state_dict" title="torch.optim.swa_utils.AveragedModel.state_dict"><code class="xref py py-attr docutils literal notranslate"><span class="pre">state_dict</span></code></a> must exactly match the keys returned
@@ -1168,7 +1168,7 @@ <h1>AveragedModel<a class="headerlink" href="#averagedmodel" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.swa_utils.AveragedModel.modules">
-<span class="sig-name descname"><span class="pre">modules</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L2758"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.AveragedModel.modules" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">modules</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/module.py#L2758"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.AveragedModel.modules" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return an iterator over all modules in the network.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Yields</dt>
@@ -1200,7 +1200,7 @@ <h1>AveragedModel<a class="headerlink" href="#averagedmodel" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.swa_utils.AveragedModel.mtia">
-<span class="sig-name descname"><span class="pre">mtia</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L1103"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.AveragedModel.mtia" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">mtia</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/module.py#L1103"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.AveragedModel.mtia" title="Permalink to this definition">¶</a></dt>
 <dd><p>Move all model parameters and buffers to the MTIA.</p>
 <p>This also makes associated parameters and buffers different objects. So
 it should be called before constructing the optimizer if the module will
@@ -1225,7 +1225,7 @@ <h1>AveragedModel<a class="headerlink" href="#averagedmodel" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.swa_utils.AveragedModel.named_buffers">
-<span class="sig-name descname"><span class="pre">named_buffers</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">prefix</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">recurse</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">remove_duplicate</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L2698"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.AveragedModel.named_buffers" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">named_buffers</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">prefix</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">recurse</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">remove_duplicate</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/module.py#L2698"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.AveragedModel.named_buffers" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return an iterator over module buffers, yielding both the name of the buffer as well as the buffer itself.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -1254,7 +1254,7 @@ <h1>AveragedModel<a class="headerlink" href="#averagedmodel" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.swa_utils.AveragedModel.named_children">
-<span class="sig-name descname"><span class="pre">named_children</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L2738"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.AveragedModel.named_children" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">named_children</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/module.py#L2738"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.AveragedModel.named_children" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return an iterator over immediate children modules, yielding both the name of the module as well as the module itself.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Yields</dt>
@@ -1274,7 +1274,7 @@ <h1>AveragedModel<a class="headerlink" href="#averagedmodel" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.swa_utils.AveragedModel.named_modules">
-<span class="sig-name descname"><span class="pre">named_modules</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">memo</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prefix</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">remove_duplicate</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L2785"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.AveragedModel.named_modules" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">named_modules</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">memo</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prefix</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">remove_duplicate</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/module.py#L2785"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.AveragedModel.named_modules" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return an iterator over all modules in the network, yielding both the name of the module as well as the module itself.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -1311,7 +1311,7 @@ <h1>AveragedModel<a class="headerlink" href="#averagedmodel" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.swa_utils.AveragedModel.named_parameters">
-<span class="sig-name descname"><span class="pre">named_parameters</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">prefix</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">recurse</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">remove_duplicate</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L2643"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.AveragedModel.named_parameters" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">named_parameters</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">prefix</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">recurse</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">remove_duplicate</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/module.py#L2643"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.AveragedModel.named_parameters" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return an iterator over module parameters, yielding both the name of the parameter as well as the parameter itself.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -1341,7 +1341,7 @@ <h1>AveragedModel<a class="headerlink" href="#averagedmodel" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.swa_utils.AveragedModel.parameters">
-<span class="sig-name descname"><span class="pre">parameters</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">recurse</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L2618"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.AveragedModel.parameters" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">parameters</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">recurse</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/module.py#L2618"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.AveragedModel.parameters" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return an iterator over module parameters.</p>
 <p>This is typically passed to an optimizer.</p>
 <dl class="field-list simple">
@@ -1368,7 +1368,7 @@ <h1>AveragedModel<a class="headerlink" href="#averagedmodel" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.swa_utils.AveragedModel.register_backward_hook">
-<span class="sig-name descname"><span class="pre">register_backward_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L1404"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.AveragedModel.register_backward_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_backward_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/module.py#L1404"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.AveragedModel.register_backward_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register a backward hook on the module.</p>
 <p>This function is deprecated in favor of <a class="reference internal" href="torch.nn.Module.html#torch.nn.Module.register_full_backward_hook" title="torch.nn.Module.register_full_backward_hook"><code class="xref py py-meth docutils literal notranslate"><span class="pre">register_full_backward_hook()</span></code></a> and
 the behavior of this function will change in future versions.</p>
@@ -1385,7 +1385,7 @@ <h1>AveragedModel<a class="headerlink" href="#averagedmodel" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.swa_utils.AveragedModel.register_buffer">
-<span class="sig-name descname"><span class="pre">register_buffer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">persistent</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L510"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.AveragedModel.register_buffer" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_buffer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">persistent</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/module.py#L510"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.AveragedModel.register_buffer" title="Permalink to this definition">¶</a></dt>
 <dd><p>Add a buffer to the module.</p>
 <p>This is typically used to register a buffer that should not to be
 considered a model parameter. For example, BatchNorm’s <code class="docutils literal notranslate"><span class="pre">running_mean</span></code>
@@ -1417,7 +1417,7 @@ <h1>AveragedModel<a class="headerlink" href="#averagedmodel" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.swa_utils.AveragedModel.register_forward_hook">
-<span class="sig-name descname"><span class="pre">register_forward_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">with_kwargs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">always_call</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L1656"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.AveragedModel.register_forward_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_forward_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">with_kwargs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">always_call</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/module.py#L1656"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.AveragedModel.register_forward_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register a forward hook on the module.</p>
 <p>The hook will be called every time after <a class="reference internal" href="#torch.optim.swa_utils.AveragedModel.forward" title="torch.optim.swa_utils.AveragedModel.forward"><code class="xref py py-func docutils literal notranslate"><span class="pre">forward()</span></code></a> has computed an output.</p>
 <p>If <code class="docutils literal notranslate"><span class="pre">with_kwargs</span></code> is <code class="docutils literal notranslate"><span class="pre">False</span></code> or not specified, the input contains only
@@ -1468,7 +1468,7 @@ <h1>AveragedModel<a class="headerlink" href="#averagedmodel" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.swa_utils.AveragedModel.register_forward_pre_hook">
-<span class="sig-name descname"><span class="pre">register_forward_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">with_kwargs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L1590"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.AveragedModel.register_forward_pre_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_forward_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">with_kwargs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/module.py#L1590"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.AveragedModel.register_forward_pre_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register a forward pre-hook on the module.</p>
 <p>The hook will be called every time before <a class="reference internal" href="#torch.optim.swa_utils.AveragedModel.forward" title="torch.optim.swa_utils.AveragedModel.forward"><code class="xref py py-func docutils literal notranslate"><span class="pre">forward()</span></code></a> is invoked.</p>
 <p>If <code class="docutils literal notranslate"><span class="pre">with_kwargs</span></code> is false or not specified, the input contains only
@@ -1518,7 +1518,7 @@ <h1>AveragedModel<a class="headerlink" href="#averagedmodel" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.swa_utils.AveragedModel.register_full_backward_hook">
-<span class="sig-name descname"><span class="pre">register_full_backward_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L1430"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.AveragedModel.register_full_backward_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_full_backward_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/module.py#L1430"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.AveragedModel.register_full_backward_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register a backward hook on the module.</p>
 <p>The hook will be called every time the gradients with respect to a module
 are computed, i.e. the hook will execute if and only if the gradients with
@@ -1569,7 +1569,7 @@ <h1>AveragedModel<a class="headerlink" href="#averagedmodel" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.swa_utils.AveragedModel.register_full_backward_pre_hook">
-<span class="sig-name descname"><span class="pre">register_full_backward_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L1355"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.AveragedModel.register_full_backward_pre_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_full_backward_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/module.py#L1355"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.AveragedModel.register_full_backward_pre_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register a backward pre-hook on the module.</p>
 <p>The hook will be called every time the gradients for the module are computed.
 The hook should have the following signature:</p>
@@ -1615,7 +1615,7 @@ <h1>AveragedModel<a class="headerlink" href="#averagedmodel" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.swa_utils.AveragedModel.register_load_state_dict_post_hook">
-<span class="sig-name descname"><span class="pre">register_load_state_dict_post_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L2273"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.AveragedModel.register_load_state_dict_post_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_load_state_dict_post_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/module.py#L2273"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.AveragedModel.register_load_state_dict_post_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register a post-hook to be run after module’s <code class="xref py py-meth docutils literal notranslate"><span class="pre">load_state_dict()</span></code> is called.</p>
 <dl class="simple">
 <dt>It should have the following signature::</dt><dd><p>hook(module, incompatible_keys) -&gt; None</p>
@@ -1645,7 +1645,7 @@ <h1>AveragedModel<a class="headerlink" href="#averagedmodel" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.swa_utils.AveragedModel.register_load_state_dict_pre_hook">
-<span class="sig-name descname"><span class="pre">register_load_state_dict_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L2261"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.AveragedModel.register_load_state_dict_pre_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_load_state_dict_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/module.py#L2261"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.AveragedModel.register_load_state_dict_pre_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register a pre-hook to be run before module’s <code class="xref py py-meth docutils literal notranslate"><span class="pre">load_state_dict()</span></code> is called.</p>
 <dl class="simple">
 <dt>It should have the following signature::</dt><dd><p>hook(module, state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs) -&gt; None  # noqa: B950</p>
@@ -1661,7 +1661,7 @@ <h1>AveragedModel<a class="headerlink" href="#averagedmodel" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.swa_utils.AveragedModel.register_module">
-<span class="sig-name descname"><span class="pre">register_module</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">module</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L650"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.AveragedModel.register_module" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_module</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">module</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/module.py#L650"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.AveragedModel.register_module" title="Permalink to this definition">¶</a></dt>
 <dd><p>Alias for <a class="reference internal" href="#torch.optim.swa_utils.AveragedModel.add_module" title="torch.optim.swa_utils.AveragedModel.add_module"><code class="xref py py-func docutils literal notranslate"><span class="pre">add_module()</span></code></a>.</p>
 <dl class="field-list simple">
 </dl>
@@ -1669,7 +1669,7 @@ <h1>AveragedModel<a class="headerlink" href="#averagedmodel" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.swa_utils.AveragedModel.register_parameter">
-<span class="sig-name descname"><span class="pre">register_parameter</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">param</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L572"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.AveragedModel.register_parameter" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_parameter</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">param</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/module.py#L572"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.AveragedModel.register_parameter" title="Permalink to this definition">¶</a></dt>
 <dd><p>Add a parameter to the module.</p>
 <p>The parameter can be accessed as an attribute using given name.</p>
 <dl class="field-list simple">
@@ -1688,7 +1688,7 @@ <h1>AveragedModel<a class="headerlink" href="#averagedmodel" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.swa_utils.AveragedModel.register_state_dict_post_hook">
-<span class="sig-name descname"><span class="pre">register_state_dict_post_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L2071"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.AveragedModel.register_state_dict_post_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_state_dict_post_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/module.py#L2071"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.AveragedModel.register_state_dict_post_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register a post-hook for the <a class="reference internal" href="torch.nn.Module.html#torch.nn.Module.state_dict" title="torch.nn.Module.state_dict"><code class="xref py py-meth docutils literal notranslate"><span class="pre">state_dict()</span></code></a> method.</p>
 <dl class="simple">
 <dt>It should have the following signature::</dt><dd><p>hook(module, state_dict, prefix, local_metadata) -&gt; None</p>
@@ -1699,7 +1699,7 @@ <h1>AveragedModel<a class="headerlink" href="#averagedmodel" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.swa_utils.AveragedModel.register_state_dict_pre_hook">
-<span class="sig-name descname"><span class="pre">register_state_dict_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L2095"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.AveragedModel.register_state_dict_pre_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_state_dict_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/module.py#L2095"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.AveragedModel.register_state_dict_pre_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register a pre-hook for the <a class="reference internal" href="torch.nn.Module.html#torch.nn.Module.state_dict" title="torch.nn.Module.state_dict"><code class="xref py py-meth docutils literal notranslate"><span class="pre">state_dict()</span></code></a> method.</p>
 <dl class="simple">
 <dt>It should have the following signature::</dt><dd><p>hook(module, prefix, keep_vars) -&gt; None</p>
@@ -1711,7 +1711,7 @@ <h1>AveragedModel<a class="headerlink" href="#averagedmodel" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.swa_utils.AveragedModel.requires_grad_">
-<span class="sig-name descname"><span class="pre">requires_grad_</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">requires_grad</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L2874"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.AveragedModel.requires_grad_" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">requires_grad_</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">requires_grad</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/module.py#L2874"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.AveragedModel.requires_grad_" title="Permalink to this definition">¶</a></dt>
 <dd><p>Change if autograd should record operations on parameters in this module.</p>
 <p>This method sets the parameters’ <code class="xref py py-attr docutils literal notranslate"><span class="pre">requires_grad</span></code> attributes
 in-place.</p>
@@ -1735,7 +1735,7 @@ <h1>AveragedModel<a class="headerlink" href="#averagedmodel" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.swa_utils.AveragedModel.set_extra_state">
-<span class="sig-name descname"><span class="pre">set_extra_state</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L893"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.AveragedModel.set_extra_state" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">set_extra_state</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/module.py#L893"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.AveragedModel.set_extra_state" title="Permalink to this definition">¶</a></dt>
 <dd><p>Set extra state contained in the loaded <cite>state_dict</cite>.</p>
 <p>This function is called from <a class="reference internal" href="#torch.optim.swa_utils.AveragedModel.load_state_dict" title="torch.optim.swa_utils.AveragedModel.load_state_dict"><code class="xref py py-func docutils literal notranslate"><span class="pre">load_state_dict()</span></code></a> to handle any extra state
 found within the <cite>state_dict</cite>. Implement this function and a corresponding
@@ -1750,7 +1750,7 @@ <h1>AveragedModel<a class="headerlink" href="#averagedmodel" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.swa_utils.AveragedModel.set_submodule">
-<span class="sig-name descname"><span class="pre">set_submodule</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">target</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">strict</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L719"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.AveragedModel.set_submodule" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">set_submodule</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">target</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">strict</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/module.py#L719"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.AveragedModel.set_submodule" title="Permalink to this definition">¶</a></dt>
 <dd><p>Set the submodule given by <code class="docutils literal notranslate"><span class="pre">target</span></code> if it exists, otherwise throw an error.</p>
 <div class="admonition note">
 <p class="admonition-title">Note</p>
@@ -1808,7 +1808,7 @@ <h1>AveragedModel<a class="headerlink" href="#averagedmodel" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.swa_utils.AveragedModel.share_memory">
-<span class="sig-name descname"><span class="pre">share_memory</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L2925"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.AveragedModel.share_memory" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">share_memory</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/module.py#L2925"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.AveragedModel.share_memory" title="Permalink to this definition">¶</a></dt>
 <dd><p>See <a class="reference internal" href="torch.Tensor.share_memory_.html#torch.Tensor.share_memory_" title="torch.Tensor.share_memory_"><code class="xref py py-meth docutils literal notranslate"><span class="pre">torch.Tensor.share_memory_()</span></code></a>.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -1819,7 +1819,7 @@ <h1>AveragedModel<a class="headerlink" href="#averagedmodel" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.swa_utils.AveragedModel.state_dict">
-<span class="sig-name descname"><span class="pre">state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">destination</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prefix</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">keep_vars</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L2152"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.AveragedModel.state_dict" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">destination</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prefix</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">keep_vars</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/module.py#L2152"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.AveragedModel.state_dict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return a dictionary containing references to the whole state of the module.</p>
 <p>Both parameters and persistent buffers (e.g. running averages) are
 included. Keys are corresponding parameter and buffer names.
@@ -1872,27 +1872,27 @@ <h1>AveragedModel<a class="headerlink" href="#averagedmodel" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.swa_utils.AveragedModel.to">
-<span class="sig-name descname"><span class="pre">to</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L1226"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.AveragedModel.to" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">to</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/module.py#L1226"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.AveragedModel.to" title="Permalink to this definition">¶</a></dt>
 <dd><p>Move and/or cast the parameters and buffers.</p>
 <p>This can be called as</p>
 <dl class="py function">
 <dt class="sig sig-object py">
-<span class="sig-name descname"><span class="pre">to</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">non_blocking</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L1226"><span class="viewcode-link"><span class="pre">[source]</span></span></a></dt>
+<span class="sig-name descname"><span class="pre">to</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">non_blocking</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/module.py#L1226"><span class="viewcode-link"><span class="pre">[source]</span></span></a></dt>
 <dd></dd></dl>
 
 <dl class="py function">
 <dt class="sig sig-object py">
-<span class="sig-name descname"><span class="pre">to</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dtype</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">non_blocking</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L1226"><span class="viewcode-link"><span class="pre">[source]</span></span></a></dt>
+<span class="sig-name descname"><span class="pre">to</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dtype</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">non_blocking</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/module.py#L1226"><span class="viewcode-link"><span class="pre">[source]</span></span></a></dt>
 <dd></dd></dl>
 
 <dl class="py function">
 <dt class="sig sig-object py">
-<span class="sig-name descname"><span class="pre">to</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">non_blocking</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L1226"><span class="viewcode-link"><span class="pre">[source]</span></span></a></dt>
+<span class="sig-name descname"><span class="pre">to</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">non_blocking</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/module.py#L1226"><span class="viewcode-link"><span class="pre">[source]</span></span></a></dt>
 <dd></dd></dl>
 
 <dl class="py function">
 <dt class="sig sig-object py">
-<span class="sig-name descname"><span class="pre">to</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">memory_format</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.channels_last</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L1226"><span class="viewcode-link"><span class="pre">[source]</span></span></a></dt>
+<span class="sig-name descname"><span class="pre">to</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">memory_format</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.channels_last</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/module.py#L1226"><span class="viewcode-link"><span class="pre">[source]</span></span></a></dt>
 <dd></dd></dl>
 
 <p>Its signature is similar to <a class="reference internal" href="torch.Tensor.to.html#torch.Tensor.to" title="torch.Tensor.to"><code class="xref py py-meth docutils literal notranslate"><span class="pre">torch.Tensor.to()</span></code></a>, but only accepts
@@ -1971,7 +1971,7 @@ <h1>AveragedModel<a class="headerlink" href="#averagedmodel" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.swa_utils.AveragedModel.to_empty">
-<span class="sig-name descname"><span class="pre">to_empty</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">recurse</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L1191"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.AveragedModel.to_empty" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">to_empty</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">recurse</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/module.py#L1191"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.AveragedModel.to_empty" title="Permalink to this definition">¶</a></dt>
 <dd><p>Move the parameters and buffers to the specified device without copying storage.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -1993,7 +1993,7 @@ <h1>AveragedModel<a class="headerlink" href="#averagedmodel" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.swa_utils.AveragedModel.train">
-<span class="sig-name descname"><span class="pre">train</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L2834"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.AveragedModel.train" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">train</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/module.py#L2834"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.AveragedModel.train" title="Permalink to this definition">¶</a></dt>
 <dd><p>Set the module in training mode.</p>
 <p>This has an effect only on certain modules. See the documentation of
 particular modules for details of their behaviors in training/evaluation
@@ -2015,7 +2015,7 @@ <h1>AveragedModel<a class="headerlink" href="#averagedmodel" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.swa_utils.AveragedModel.type">
-<span class="sig-name descname"><span class="pre">type</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dst_type</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L1133"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.AveragedModel.type" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">type</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dst_type</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/module.py#L1133"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.AveragedModel.type" title="Permalink to this definition">¶</a></dt>
 <dd><p>Casts all parameters and buffers to <code class="xref py py-attr docutils literal notranslate"><span class="pre">dst_type</span></code>.</p>
 <div class="admonition note">
 <p class="admonition-title">Note</p>
@@ -2036,7 +2036,7 @@ <h1>AveragedModel<a class="headerlink" href="#averagedmodel" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.swa_utils.AveragedModel.update_parameters">
-<span class="sig-name descname"><span class="pre">update_parameters</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/swa_utils.html#AveragedModel.update_parameters"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/swa_utils.py#L245"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.AveragedModel.update_parameters" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">update_parameters</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/swa_utils.html#AveragedModel.update_parameters"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/swa_utils.py#L245"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.AveragedModel.update_parameters" title="Permalink to this definition">¶</a></dt>
 <dd><p>Update model parameters.</p>
 <dl class="field-list simple">
 </dl>
@@ -2044,7 +2044,7 @@ <h1>AveragedModel<a class="headerlink" href="#averagedmodel" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.swa_utils.AveragedModel.xpu">
-<span class="sig-name descname"><span class="pre">xpu</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L1084"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.AveragedModel.xpu" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">xpu</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/module.py#L1084"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.AveragedModel.xpu" title="Permalink to this definition">¶</a></dt>
 <dd><p>Move all model parameters and buffers to the XPU.</p>
 <p>This also makes associated parameters and buffers different objects. So
 it should be called before constructing optimizer if the module will
@@ -2069,7 +2069,7 @@ <h1>AveragedModel<a class="headerlink" href="#averagedmodel" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.swa_utils.AveragedModel.zero_grad">
-<span class="sig-name descname"><span class="pre">zero_grad</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">set_to_none</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/modules/module.py#L2897"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.AveragedModel.zero_grad" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">zero_grad</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">set_to_none</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/modules/module.py#L2897"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.AveragedModel.zero_grad" title="Permalink to this definition">¶</a></dt>
 <dd><p>Reset gradients of all model parameters.</p>
 <p>See similar function under <a class="reference internal" href="../optim.html#torch.optim.Optimizer" title="torch.optim.Optimizer"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.optim.Optimizer</span></code></a> for more context.</p>
 <dl class="field-list simple">
diff --git a/main/generated/torch.optim.swa_utils.SWALR.html b/main/generated/torch.optim.swa_utils.SWALR.html
index a1004d81d3e..08fea36f1ac 100644
--- a/main/generated/torch.optim.swa_utils.SWALR.html
+++ b/main/generated/torch.optim.swa_utils.SWALR.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>SWALR<a class="headerlink" href="#swalr" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.optim.swa_utils.SWALR">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.optim.swa_utils.</span></span><span class="sig-name descname"><span class="pre">SWALR</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">optimizer</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">swa_lr</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">anneal_epochs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">anneal_strategy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'cos'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">last_epoch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-1</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/swa_utils.html#SWALR"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/swa_utils.py#L368"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.SWALR" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.optim.swa_utils.</span></span><span class="sig-name descname"><span class="pre">SWALR</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">optimizer</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">swa_lr</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">anneal_epochs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">anneal_strategy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'cos'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">last_epoch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-1</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/swa_utils.html#SWALR"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/swa_utils.py#L368"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.SWALR" title="Permalink to this definition">¶</a></dt>
 <dd><p>Anneals the learning rate in each parameter group to a fixed value.</p>
 <p>This learning rate scheduler is meant to be used with Stochastic Weight
 Averaging (SWA) method (see <cite>torch.optim.swa_utils.AveragedModel</cite>).</p>
@@ -655,7 +655,7 @@ <h1>SWALR<a class="headerlink" href="#swalr" title="Permalink to this heading">
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.swa_utils.SWALR.get_last_lr">
-<span class="sig-name descname"><span class="pre">get_last_lr</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L172"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.SWALR.get_last_lr" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">get_last_lr</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/lr_scheduler.py#L172"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.SWALR.get_last_lr" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return last computed learning rate by current scheduler.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -666,13 +666,13 @@ <h1>SWALR<a class="headerlink" href="#swalr" title="Permalink to this heading">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.swa_utils.SWALR.get_lr">
-<span class="sig-name descname"><span class="pre">get_lr</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/swa_utils.html#SWALR.get_lr"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/swa_utils.py#L453"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.SWALR.get_lr" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">get_lr</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/optim/swa_utils.html#SWALR.get_lr"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/swa_utils.py#L453"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.SWALR.get_lr" title="Permalink to this definition">¶</a></dt>
 <dd><p>Get learning rate.</p>
 </dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.swa_utils.SWALR.load_state_dict">
-<span class="sig-name descname"><span class="pre">load_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state_dict</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L163"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.SWALR.load_state_dict" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">load_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state_dict</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/lr_scheduler.py#L163"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.SWALR.load_state_dict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Load the scheduler’s state.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -684,7 +684,7 @@ <h1>SWALR<a class="headerlink" href="#swalr" title="Permalink to this heading">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.swa_utils.SWALR.print_lr">
-<span class="sig-name descname"><span class="pre">print_lr</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">is_verbose</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lr</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">epoch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L180"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.SWALR.print_lr" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">print_lr</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">is_verbose</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lr</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">epoch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/lr_scheduler.py#L180"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.SWALR.print_lr" title="Permalink to this definition">¶</a></dt>
 <dd><p>Display the current learning rate.</p>
 <div class="deprecated">
 <p><span class="versionmodified deprecated">Deprecated since version 2.4: </span><code class="docutils literal notranslate"><span class="pre">print_lr()</span></code> is deprecated. Please use <code class="docutils literal notranslate"><span class="pre">get_last_lr()</span></code> to access the
@@ -696,7 +696,7 @@ <h1>SWALR<a class="headerlink" href="#swalr" title="Permalink to this heading">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.swa_utils.SWALR.state_dict">
-<span class="sig-name descname"><span class="pre">state_dict</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L153"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.SWALR.state_dict" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">state_dict</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/lr_scheduler.py#L153"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.SWALR.state_dict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the state of the scheduler as a <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.13)"><code class="xref py py-class docutils literal notranslate"><span class="pre">dict</span></code></a>.</p>
 <p>It contains an entry for every variable in self.__dict__ which
 is not the optimizer.</p>
@@ -704,7 +704,7 @@ <h1>SWALR<a class="headerlink" href="#swalr" title="Permalink to this heading">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.optim.swa_utils.SWALR.step">
-<span class="sig-name descname"><span class="pre">step</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">epoch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/lr_scheduler.py#L208"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.SWALR.step" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">step</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">epoch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/lr_scheduler.py#L208"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.SWALR.step" title="Permalink to this definition">¶</a></dt>
 <dd><p>Perform a step.</p>
 <dl class="field-list simple">
 </dl>
diff --git a/main/generated/torch.orgqr.html b/main/generated/torch.orgqr.html
index 4c7a04bdb01..4f9b0fc817b 100644
--- a/main/generated/torch.orgqr.html
+++ b/main/generated/torch.orgqr.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.ormqr.html b/main/generated/torch.ormqr.html
index dfe9124f3a1..ac0fe26e8b0 100644
--- a/main/generated/torch.ormqr.html
+++ b/main/generated/torch.ormqr.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.outer.html b/main/generated/torch.outer.html
index 9bea851afb3..c56186eeba7 100644
--- a/main/generated/torch.outer.html
+++ b/main/generated/torch.outer.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.pca_lowrank.html b/main/generated/torch.pca_lowrank.html
index 0333a7f9984..d83f78525c7 100644
--- a/main/generated/torch.pca_lowrank.html
+++ b/main/generated/torch.pca_lowrank.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.pca_lowrank<a class="headerlink" href="#torch-pca-lowrank" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.pca_lowrank">
-<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">pca_lowrank</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">A</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">q</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">center</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">niter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/_lowrank.html#pca_lowrank"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/_lowrank.py#L184"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.pca_lowrank" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">pca_lowrank</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">A</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">q</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">center</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">niter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/_lowrank.html#pca_lowrank"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/_lowrank.py#L184"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.pca_lowrank" title="Permalink to this definition">¶</a></dt>
 <dd><p>Performs linear Principal Component Analysis (PCA) on a low-rank
 matrix, batches of such matrices, or sparse matrix.</p>
 <p>This function returns a namedtuple <code class="docutils literal notranslate"><span class="pre">(U,</span> <span class="pre">S,</span> <span class="pre">V)</span></code> which is the
diff --git a/main/generated/torch.permute.html b/main/generated/torch.permute.html
index a885e97dd19..f559eb996f6 100644
--- a/main/generated/torch.permute.html
+++ b/main/generated/torch.permute.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.pinverse.html b/main/generated/torch.pinverse.html
index 765acdd4941..53e8f51ecd2 100644
--- a/main/generated/torch.pinverse.html
+++ b/main/generated/torch.pinverse.html
@@ -300,7 +300,7 @@
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     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.poisson.html b/main/generated/torch.poisson.html
index 66279d5d2f9..5cd51eb936c 100644
--- a/main/generated/torch.poisson.html
+++ b/main/generated/torch.poisson.html
@@ -300,7 +300,7 @@
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     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
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     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.polar.html b/main/generated/torch.polar.html
index 8080972b568..fc6ac2a0a55 100644
--- a/main/generated/torch.polar.html
+++ b/main/generated/torch.polar.html
@@ -300,7 +300,7 @@
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     <div id="searchBox">
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diff --git a/main/generated/torch.polygamma.html b/main/generated/torch.polygamma.html
index 92710f506e6..2ea20039bc8 100644
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@@ -300,7 +300,7 @@
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-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
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     <div id="searchBox">
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diff --git a/main/generated/torch.positive.html b/main/generated/torch.positive.html
index 7a50354d43e..cdb0282a3fd 100644
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+++ b/main/generated/torch.positive.html
@@ -300,7 +300,7 @@
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-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
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diff --git a/main/generated/torch.pow.html b/main/generated/torch.pow.html
index 77e599a0d81..f6a5790867b 100644
--- a/main/generated/torch.pow.html
+++ b/main/generated/torch.pow.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
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-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
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     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.prod.html b/main/generated/torch.prod.html
index 5ab3c4b953a..824d98594c9 100644
--- a/main/generated/torch.prod.html
+++ b/main/generated/torch.prod.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
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diff --git a/main/generated/torch.promote_types.html b/main/generated/torch.promote_types.html
index bc00e42a967..a73e3b8d239 100644
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+++ b/main/generated/torch.promote_types.html
@@ -300,7 +300,7 @@
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-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
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diff --git a/main/generated/torch.qr.html b/main/generated/torch.qr.html
index e9bb1d553a0..83cb1c84cf1 100644
--- a/main/generated/torch.qr.html
+++ b/main/generated/torch.qr.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
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-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
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diff --git a/main/generated/torch.quantile.html b/main/generated/torch.quantile.html
index 0612f686d78..329064c2fde 100644
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@@ -300,7 +300,7 @@
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diff --git a/main/generated/torch.quantize_per_channel.html b/main/generated/torch.quantize_per_channel.html
index 6bc93e1670c..d4b0e11c361 100644
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@@ -300,7 +300,7 @@
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index 7729c5e586c..d43c6877abf 100644
--- a/main/generated/torch.quantize_per_tensor.html
+++ b/main/generated/torch.quantize_per_tensor.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.quantized_batch_norm.html b/main/generated/torch.quantized_batch_norm.html
index 03901f30abc..512001331ba 100644
--- a/main/generated/torch.quantized_batch_norm.html
+++ b/main/generated/torch.quantized_batch_norm.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.quantized_max_pool1d.html b/main/generated/torch.quantized_max_pool1d.html
index 008d1cede77..4b9e993ce51 100644
--- a/main/generated/torch.quantized_max_pool1d.html
+++ b/main/generated/torch.quantized_max_pool1d.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.quantized_max_pool2d.html b/main/generated/torch.quantized_max_pool2d.html
index 10d9061597b..0c919e6fd57 100644
--- a/main/generated/torch.quantized_max_pool2d.html
+++ b/main/generated/torch.quantized_max_pool2d.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.quasirandom.SobolEngine.html b/main/generated/torch.quasirandom.SobolEngine.html
index c36e3f5818c..ad8fa755479 100644
--- a/main/generated/torch.quasirandom.SobolEngine.html
+++ b/main/generated/torch.quasirandom.SobolEngine.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>SobolEngine<a class="headerlink" href="#sobolengine" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.quasirandom.SobolEngine">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.quasirandom.</span></span><span class="sig-name descname"><span class="pre">SobolEngine</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dimension</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scramble</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">seed</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/quasirandom.html#SobolEngine"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/quasirandom.py#L7"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.quasirandom.SobolEngine" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.quasirandom.</span></span><span class="sig-name descname"><span class="pre">SobolEngine</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dimension</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scramble</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">seed</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/quasirandom.html#SobolEngine"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/quasirandom.py#L7"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.quasirandom.SobolEngine" title="Permalink to this definition">¶</a></dt>
 <dd><p>The <a class="reference internal" href="#torch.quasirandom.SobolEngine" title="torch.quasirandom.SobolEngine"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.quasirandom.SobolEngine</span></code></a> is an engine for generating
 (scrambled) Sobol sequences. Sobol sequences are an example of low
 discrepancy quasi-random sequences.</p>
@@ -654,7 +654,7 @@ <h1>SobolEngine<a class="headerlink" href="#sobolengine" title="Permalink to thi
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.quasirandom.SobolEngine.draw">
-<span class="sig-name descname"><span class="pre">draw</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/quasirandom.html#SobolEngine.draw"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/quasirandom.py#L78"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.quasirandom.SobolEngine.draw" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">draw</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/quasirandom.html#SobolEngine.draw"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/quasirandom.py#L78"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.quasirandom.SobolEngine.draw" title="Permalink to this definition">¶</a></dt>
 <dd><p>Function to draw a sequence of <code class="xref py py-attr docutils literal notranslate"><span class="pre">n</span></code> points from a Sobol sequence.
 Note that the samples are dependent on the previous samples. The size
 of the result is <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mo stretchy="false">(</mo><mi>n</mi><mo separator="true">,</mo><mi>d</mi><mi>i</mi><mi>m</mi><mi>e</mi><mi>n</mi><mi>s</mi><mi>i</mi><mi>o</mi><mi>n</mi><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">(n, dimension)</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mopen">(</span><span class="mord mathnormal">n</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord mathnormal">d</span><span class="mord mathnormal">im</span><span class="mord mathnormal">e</span><span class="mord mathnormal">n</span><span class="mord mathnormal">s</span><span class="mord mathnormal">i</span><span class="mord mathnormal">o</span><span class="mord mathnormal">n</span><span class="mclose">)</span></span></span></span></span>.</p>
@@ -677,7 +677,7 @@ <h1>SobolEngine<a class="headerlink" href="#sobolengine" title="Permalink to thi
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.quasirandom.SobolEngine.draw_base2">
-<span class="sig-name descname"><span class="pre">draw_base2</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">m</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/quasirandom.html#SobolEngine.draw_base2"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/quasirandom.py#L131"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.quasirandom.SobolEngine.draw_base2" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">draw_base2</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">m</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/quasirandom.html#SobolEngine.draw_base2"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/quasirandom.py#L131"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.quasirandom.SobolEngine.draw_base2" title="Permalink to this definition">¶</a></dt>
 <dd><p>Function to draw a sequence of <code class="xref py py-attr docutils literal notranslate"><span class="pre">2**m</span></code> points from a Sobol sequence.
 Note that the samples are dependent on the previous samples. The size
 of the result is <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mo stretchy="false">(</mo><mn>2</mn><mo>∗</mo><mo>∗</mo><mi>m</mi><mo separator="true">,</mo><mi>d</mi><mi>i</mi><mi>m</mi><mi>e</mi><mi>n</mi><mi>s</mi><mi>i</mi><mi>o</mi><mi>n</mi><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">(2**m, dimension)</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mopen">(</span><span class="mord">2</span><span class="mspace" style="margin-right:0.2222em;"></span><span class="mbin">∗</span><span class="mspace" style="margin-right:0.2222em;"></span></span><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mord">∗</span><span class="mord mathnormal">m</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord mathnormal">d</span><span class="mord mathnormal">im</span><span class="mord mathnormal">e</span><span class="mord mathnormal">n</span><span class="mord mathnormal">s</span><span class="mord mathnormal">i</span><span class="mord mathnormal">o</span><span class="mord mathnormal">n</span><span class="mclose">)</span></span></span></span></span>.</p>
@@ -699,7 +699,7 @@ <h1>SobolEngine<a class="headerlink" href="#sobolengine" title="Permalink to thi
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.quasirandom.SobolEngine.fast_forward">
-<span class="sig-name descname"><span class="pre">fast_forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/quasirandom.html#SobolEngine.fast_forward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/quasirandom.py#L169"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.quasirandom.SobolEngine.fast_forward" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">fast_forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">n</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/quasirandom.html#SobolEngine.fast_forward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/quasirandom.py#L169"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.quasirandom.SobolEngine.fast_forward" title="Permalink to this definition">¶</a></dt>
 <dd><p>Function to fast-forward the state of the <code class="docutils literal notranslate"><span class="pre">SobolEngine</span></code> by
 <code class="xref py py-attr docutils literal notranslate"><span class="pre">n</span></code> steps. This is equivalent to drawing <code class="xref py py-attr docutils literal notranslate"><span class="pre">n</span></code> samples
 without using the samples.</p>
@@ -712,7 +712,7 @@ <h1>SobolEngine<a class="headerlink" href="#sobolengine" title="Permalink to thi
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.quasirandom.SobolEngine.reset">
-<span class="sig-name descname"><span class="pre">reset</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/quasirandom.html#SobolEngine.reset"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/quasirandom.py#L161"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.quasirandom.SobolEngine.reset" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">reset</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/quasirandom.html#SobolEngine.reset"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/quasirandom.py#L161"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.quasirandom.SobolEngine.reset" title="Permalink to this definition">¶</a></dt>
 <dd><p>Function to reset the <code class="docutils literal notranslate"><span class="pre">SobolEngine</span></code> to base state.</p>
 </dd></dl>
 
diff --git a/main/generated/torch.rad2deg.html b/main/generated/torch.rad2deg.html
index e8fb1593d92..bf3a4abf293 100644
--- a/main/generated/torch.rad2deg.html
+++ b/main/generated/torch.rad2deg.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.rand.html b/main/generated/torch.rand.html
index d6764af9104..2c541b000f5 100644
--- a/main/generated/torch.rand.html
+++ b/main/generated/torch.rand.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.rand_like.html b/main/generated/torch.rand_like.html
index 006637cbb53..6daa2c10433 100644
--- a/main/generated/torch.rand_like.html
+++ b/main/generated/torch.rand_like.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.randint.html b/main/generated/torch.randint.html
index d3cc6a47248..80b0f569a94 100644
--- a/main/generated/torch.randint.html
+++ b/main/generated/torch.randint.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.randint_like.html b/main/generated/torch.randint_like.html
index e16aa51c161..c2ce68a0b06 100644
--- a/main/generated/torch.randint_like.html
+++ b/main/generated/torch.randint_like.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.randn.html b/main/generated/torch.randn.html
index 639b32288b2..59636bb5c12 100644
--- a/main/generated/torch.randn.html
+++ b/main/generated/torch.randn.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.randn_like.html b/main/generated/torch.randn_like.html
index 20c465ebb24..31d5d0f4454 100644
--- a/main/generated/torch.randn_like.html
+++ b/main/generated/torch.randn_like.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
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diff --git a/main/generated/torch.randperm.html b/main/generated/torch.randperm.html
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index 15f8db79d6c..5d52f5f16af 100644
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diff --git a/main/generated/torch.ravel.html b/main/generated/torch.ravel.html
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diff --git a/main/generated/torch.real.html b/main/generated/torch.real.html
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diff --git a/main/generated/torch.result_type.html b/main/generated/torch.result_type.html
index 9aa59267a57..9205462d277 100644
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diff --git a/main/generated/torch.roll.html b/main/generated/torch.roll.html
index 345a832dc4a..2444e6eb7bb 100644
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diff --git a/main/generated/torch.rot90.html b/main/generated/torch.rot90.html
index 8c0e0424e23..7353995a6ad 100644
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diff --git a/main/generated/torch.round.html b/main/generated/torch.round.html
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diff --git a/main/generated/torch.save.html b/main/generated/torch.save.html
index 6e53735e902..70d21e33ab6 100644
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@@ -612,7 +612,7 @@
 <h1>torch.save<a class="headerlink" href="#torch-save" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.save">
-<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">save</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">obj</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">f</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pickle_module</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">pickle</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pickle_protocol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_use_new_zipfile_serialization</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/serialization.html#save"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/serialization.py#L882"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.save" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">save</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">obj</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">f</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pickle_module</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">pickle</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pickle_protocol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_use_new_zipfile_serialization</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/serialization.html#save"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/serialization.py#L882"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.save" title="Permalink to this definition">¶</a></dt>
 <dd><p>Saves an object to a disk file.</p>
 <p>See also: <a class="reference internal" href="../notes/serialization.html#saving-loading-tensors"><span class="std std-ref">Saving and loading tensors</span></a></p>
 <dl class="field-list simple">
diff --git a/main/generated/torch.scatter.html b/main/generated/torch.scatter.html
index 836aed89c97..9ac4441e6b8 100644
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diff --git a/main/generated/torch.scatter_add.html b/main/generated/torch.scatter_add.html
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diff --git a/main/generated/torch.seed.html b/main/generated/torch.seed.html
index 11374984fc1..e5b1f04219c 100644
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@@ -612,7 +612,7 @@
 <h1>torch.seed<a class="headerlink" href="#torch-seed" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.seed">
-<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">seed</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/random.html#seed"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/random.py#L63"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.seed" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">seed</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/random.html#seed"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/random.py#L63"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.seed" title="Permalink to this definition">¶</a></dt>
 <dd><p>Sets the seed for generating random numbers to a non-deterministic
 random number on all devices. Returns a 64 bit number used to seed the RNG.</p>
 <dl class="field-list simple">
diff --git a/main/generated/torch.select.html b/main/generated/torch.select.html
index 22efee91bc9..fd07f937a89 100644
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index 288aee31773..898c6b4b2e0 100644
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diff --git a/main/generated/torch.set_default_device.html b/main/generated/torch.set_default_device.html
index 6bd0fd57627..c921ffcdd79 100644
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     <div class="version">
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     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.set_default_device<a class="headerlink" href="#torch-set-default-device" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.set_default_device">
-<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">set_default_device</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch.html#set_default_device"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/__init__.py#L1140"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.set_default_device" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">set_default_device</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch.html#set_default_device"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/__init__.py#L1140"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.set_default_device" title="Permalink to this definition">¶</a></dt>
 <dd><p>Sets the default <code class="docutils literal notranslate"><span class="pre">torch.Tensor</span></code> to be allocated on <code class="docutils literal notranslate"><span class="pre">device</span></code>.  This
 does not affect factory function calls which are called with an explicit
 <code class="docutils literal notranslate"><span class="pre">device</span></code> argument.  Factory calls will be performed as if they
diff --git a/main/generated/torch.set_default_dtype.html b/main/generated/torch.set_default_dtype.html
index fbbbbe31c46..f2e22d25f5e 100644
--- a/main/generated/torch.set_default_dtype.html
+++ b/main/generated/torch.set_default_dtype.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
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     </div>
     <div id="searchBox">
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@@ -612,7 +612,7 @@
 <h1>torch.set_default_dtype<a class="headerlink" href="#torch-set-default-dtype" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.set_default_dtype">
-<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">set_default_dtype</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">d</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">/</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch.html#set_default_dtype"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/__init__.py#L1235"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.set_default_dtype" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">set_default_dtype</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">d</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">/</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch.html#set_default_dtype"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/__init__.py#L1235"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.set_default_dtype" title="Permalink to this definition">¶</a></dt>
 <dd><p>Sets the default floating point dtype to <code class="xref py py-attr docutils literal notranslate"><span class="pre">d</span></code>. Supports floating point dtype
 as inputs. Other dtypes will cause torch to raise an exception.</p>
 <p>When PyTorch is initialized its default floating point dtype is torch.float32,
diff --git a/main/generated/torch.set_default_tensor_type.html b/main/generated/torch.set_default_tensor_type.html
index 9cc309e4702..49e2b8400f5 100644
--- a/main/generated/torch.set_default_tensor_type.html
+++ b/main/generated/torch.set_default_tensor_type.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
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     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.set_default_tensor_type<a class="headerlink" href="#torch-set-default-tensor-type" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.set_default_tensor_type">
-<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">set_default_tensor_type</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">t</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">/</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch.html#set_default_tensor_type"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/__init__.py#L1204"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.set_default_tensor_type" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">set_default_tensor_type</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">t</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">/</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch.html#set_default_tensor_type"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/__init__.py#L1204"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.set_default_tensor_type" title="Permalink to this definition">¶</a></dt>
 <dd><div class="admonition warning">
 <p class="admonition-title">Warning</p>
 <p>This function is deprecated as of PyTorch 2.1, please use <a class="reference internal" href="torch.set_default_dtype.html#torch.set_default_dtype" title="torch.set_default_dtype"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.set_default_dtype()</span></code></a> and
diff --git a/main/generated/torch.set_deterministic_debug_mode.html b/main/generated/torch.set_deterministic_debug_mode.html
index 653ad5b61a3..d26cd2641ce 100644
--- a/main/generated/torch.set_deterministic_debug_mode.html
+++ b/main/generated/torch.set_deterministic_debug_mode.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
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     </div>
     <div id="searchBox">
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@@ -612,7 +612,7 @@
 <h1>torch.set_deterministic_debug_mode<a class="headerlink" href="#torch-set-deterministic-debug-mode" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.set_deterministic_debug_mode">
-<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">set_deterministic_debug_mode</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">debug_mode</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch.html#set_deterministic_debug_mode"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/__init__.py#L1445"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.set_deterministic_debug_mode" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">set_deterministic_debug_mode</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">debug_mode</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch.html#set_deterministic_debug_mode"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/__init__.py#L1445"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.set_deterministic_debug_mode" title="Permalink to this definition">¶</a></dt>
 <dd><p>Sets the debug mode for deterministic operations.</p>
 <div class="admonition note">
 <p class="admonition-title">Note</p>
diff --git a/main/generated/torch.set_float32_matmul_precision.html b/main/generated/torch.set_float32_matmul_precision.html
index 9d962318fff..6737b32d35d 100644
--- a/main/generated/torch.set_float32_matmul_precision.html
+++ b/main/generated/torch.set_float32_matmul_precision.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.set_float32_matmul_precision<a class="headerlink" href="#torch-set-float32-matmul-precision" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.set_float32_matmul_precision">
-<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">set_float32_matmul_precision</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">precision</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch.html#set_float32_matmul_precision"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/__init__.py#L1511"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.set_float32_matmul_precision" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">set_float32_matmul_precision</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">precision</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch.html#set_float32_matmul_precision"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/__init__.py#L1511"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.set_float32_matmul_precision" title="Permalink to this definition">¶</a></dt>
 <dd><p>Sets the internal precision of float32 matrix multiplications.</p>
 <p>Running float32 matrix multiplications in lower precision may significantly increase
 performance, and in some programs the loss of precision has a negligible impact.</p>
diff --git a/main/generated/torch.set_flush_denormal.html b/main/generated/torch.set_flush_denormal.html
index 22c1fe81990..86843735cd6 100644
--- a/main/generated/torch.set_flush_denormal.html
+++ b/main/generated/torch.set_flush_denormal.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.set_num_interop_threads.html b/main/generated/torch.set_num_interop_threads.html
index eb3c458bcb7..d599b3b6844 100644
--- a/main/generated/torch.set_num_interop_threads.html
+++ b/main/generated/torch.set_num_interop_threads.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.set_num_threads.html b/main/generated/torch.set_num_threads.html
index 00d4e04b725..960f51cdb51 100644
--- a/main/generated/torch.set_num_threads.html
+++ b/main/generated/torch.set_num_threads.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.set_printoptions.html b/main/generated/torch.set_printoptions.html
index 18f0265a66e..85eeb4f83a7 100644
--- a/main/generated/torch.set_printoptions.html
+++ b/main/generated/torch.set_printoptions.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.set_printoptions<a class="headerlink" href="#torch-set-printoptions" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.set_printoptions">
-<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">set_printoptions</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">precision</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">threshold</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">edgeitems</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">linewidth</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">profile</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sci_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/_tensor_str.html#set_printoptions"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/_tensor_str.py#L25"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.set_printoptions" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">set_printoptions</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">precision</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">threshold</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">edgeitems</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">linewidth</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">profile</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sci_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/_tensor_str.html#set_printoptions"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/_tensor_str.py#L25"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.set_printoptions" title="Permalink to this definition">¶</a></dt>
 <dd><p>Set options for printing. Items shamelessly taken from NumPy</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.set_rng_state.html b/main/generated/torch.set_rng_state.html
index 55372ab0efc..9802c3c03ec 100644
--- a/main/generated/torch.set_rng_state.html
+++ b/main/generated/torch.set_rng_state.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
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     </div>
     <div id="searchBox">
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@@ -612,7 +612,7 @@
 <h1>torch.set_rng_state<a class="headerlink" href="#torch-set-rng-state" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.set_rng_state">
-<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">set_rng_state</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">new_state</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/random.html#set_rng_state"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/random.py#L10"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.set_rng_state" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">set_rng_state</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">new_state</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/random.html#set_rng_state"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/random.py#L10"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.set_rng_state" title="Permalink to this definition">¶</a></dt>
 <dd><p>Sets the random number generator state.</p>
 <div class="admonition note">
 <p class="admonition-title">Note</p>
diff --git a/main/generated/torch.set_warn_always.html b/main/generated/torch.set_warn_always.html
index 3457c9b19d1..6a4dc4f3db9 100644
--- a/main/generated/torch.set_warn_always.html
+++ b/main/generated/torch.set_warn_always.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.set_warn_always<a class="headerlink" href="#torch-set-warn-always" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.set_warn_always">
-<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">set_warn_always</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">b</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">/</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch.html#set_warn_always"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/__init__.py#L1577"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.set_warn_always" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">set_warn_always</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">b</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">/</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch.html#set_warn_always"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/__init__.py#L1577"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.set_warn_always" title="Permalink to this definition">¶</a></dt>
 <dd><p>When this flag is False (default) then some PyTorch warnings may only
 appear once per process. This helps avoid excessive warning information.
 Setting it to True causes these warnings to always appear, which may be
diff --git a/main/generated/torch.sgn.html b/main/generated/torch.sgn.html
index 2a8d34a769c..5ca64c293f5 100644
--- a/main/generated/torch.sgn.html
+++ b/main/generated/torch.sgn.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.sigmoid.html b/main/generated/torch.sigmoid.html
index b8828f93fe4..0054ddd42f4 100644
--- a/main/generated/torch.sigmoid.html
+++ b/main/generated/torch.sigmoid.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.sign.html b/main/generated/torch.sign.html
index a15ac2b4372..1c85c6290cd 100644
--- a/main/generated/torch.sign.html
+++ b/main/generated/torch.sign.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.signal.windows.bartlett.html b/main/generated/torch.signal.windows.bartlett.html
index 17e54071bd4..58bc3d1937d 100644
--- a/main/generated/torch.signal.windows.bartlett.html
+++ b/main/generated/torch.signal.windows.bartlett.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.signal.windows.bartlett<a class="headerlink" href="#torch-signal-windows-bartlett" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.signal.windows.bartlett">
-<span class="sig-prename descclassname"><span class="pre">torch.signal.windows.</span></span><span class="sig-name descname"><span class="pre">bartlett</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">M</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sym</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">layout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.strided</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">requires_grad</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/signal/windows/windows.html#bartlett"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/signal/windows/windows.py#L596"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.signal.windows.bartlett" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.signal.windows.</span></span><span class="sig-name descname"><span class="pre">bartlett</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">M</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sym</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">layout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.strided</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">requires_grad</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/signal/windows/windows.html#bartlett"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/signal/windows/windows.py#L596"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.signal.windows.bartlett" title="Permalink to this definition">¶</a></dt>
 <dd><p>Computes the Bartlett window.</p>
 <p>The Bartlett window is defined as follows:</p>
 <div class="math">
diff --git a/main/generated/torch.signal.windows.blackman.html b/main/generated/torch.signal.windows.blackman.html
index 4e9b861a510..d37bab556be 100644
--- a/main/generated/torch.signal.windows.blackman.html
+++ b/main/generated/torch.signal.windows.blackman.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.signal.windows.blackman<a class="headerlink" href="#torch-signal-windows-blackman" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.signal.windows.blackman">
-<span class="sig-prename descclassname"><span class="pre">torch.signal.windows.</span></span><span class="sig-name descname"><span class="pre">blackman</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">M</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sym</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">layout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.strided</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">requires_grad</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/signal/windows/windows.html#blackman"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/signal/windows/windows.py#L535"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.signal.windows.blackman" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.signal.windows.</span></span><span class="sig-name descname"><span class="pre">blackman</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">M</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sym</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">layout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.strided</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">requires_grad</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/signal/windows/windows.html#blackman"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/signal/windows/windows.py#L535"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.signal.windows.blackman" title="Permalink to this definition">¶</a></dt>
 <dd><p>Computes the Blackman window.</p>
 <p>The Blackman window is defined as follows:</p>
 <div class="math">
diff --git a/main/generated/torch.signal.windows.cosine.html b/main/generated/torch.signal.windows.cosine.html
index 094ca77fdd7..b642e1a759b 100644
--- a/main/generated/torch.signal.windows.cosine.html
+++ b/main/generated/torch.signal.windows.cosine.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.signal.windows.cosine<a class="headerlink" href="#torch-signal-windows-cosine" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.signal.windows.cosine">
-<span class="sig-prename descclassname"><span class="pre">torch.signal.windows.</span></span><span class="sig-name descname"><span class="pre">cosine</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">M</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sym</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">layout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.strided</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">requires_grad</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/signal/windows/windows.html#cosine"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/signal/windows/windows.py#L180"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.signal.windows.cosine" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.signal.windows.</span></span><span class="sig-name descname"><span class="pre">cosine</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">M</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sym</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">layout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.strided</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">requires_grad</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/signal/windows/windows.html#cosine"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/signal/windows/windows.py#L180"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.signal.windows.cosine" title="Permalink to this definition">¶</a></dt>
 <dd><p>Computes a window with a simple cosine waveform, following the same implementation as SciPy.
 This window is also known as the sine window.</p>
 <p>The cosine window is defined as follows:</p>
diff --git a/main/generated/torch.signal.windows.exponential.html b/main/generated/torch.signal.windows.exponential.html
index cdf85aff2b3..4b0ec2f4d12 100644
--- a/main/generated/torch.signal.windows.exponential.html
+++ b/main/generated/torch.signal.windows.exponential.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.signal.windows.exponential<a class="headerlink" href="#torch-signal-windows-exponential" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.signal.windows.exponential">
-<span class="sig-prename descclassname"><span class="pre">torch.signal.windows.</span></span><span class="sig-name descname"><span class="pre">exponential</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">M</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">center</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tau</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sym</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">layout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.strided</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">requires_grad</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/signal/windows/windows.html#exponential"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/signal/windows/windows.py#L89"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.signal.windows.exponential" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.signal.windows.</span></span><span class="sig-name descname"><span class="pre">exponential</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">M</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">center</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tau</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sym</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">layout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.strided</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">requires_grad</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/signal/windows/windows.html#exponential"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/signal/windows/windows.py#L89"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.signal.windows.exponential" title="Permalink to this definition">¶</a></dt>
 <dd><p>Computes a window with an exponential waveform.
 Also known as Poisson window.</p>
 <p>The exponential window is defined as follows:</p>
diff --git a/main/generated/torch.signal.windows.gaussian.html b/main/generated/torch.signal.windows.gaussian.html
index 0e1ad64fcdb..0faf901128f 100644
--- a/main/generated/torch.signal.windows.gaussian.html
+++ b/main/generated/torch.signal.windows.gaussian.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.signal.windows.gaussian<a class="headerlink" href="#torch-signal-windows-gaussian" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.signal.windows.gaussian">
-<span class="sig-prename descclassname"><span class="pre">torch.signal.windows.</span></span><span class="sig-name descname"><span class="pre">gaussian</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">M</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">std</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sym</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">layout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.strided</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">requires_grad</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/signal/windows/windows.html#gaussian"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/signal/windows/windows.py#L256"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.signal.windows.gaussian" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.signal.windows.</span></span><span class="sig-name descname"><span class="pre">gaussian</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">M</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">std</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sym</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">layout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.strided</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">requires_grad</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/signal/windows/windows.html#gaussian"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/signal/windows/windows.py#L256"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.signal.windows.gaussian" title="Permalink to this definition">¶</a></dt>
 <dd><p>Computes a window with a gaussian waveform.</p>
 <p>The gaussian window is defined as follows:</p>
 <div class="math">
diff --git a/main/generated/torch.signal.windows.general_cosine.html b/main/generated/torch.signal.windows.general_cosine.html
index f598e7f7ba9..c7e9308d639 100644
--- a/main/generated/torch.signal.windows.general_cosine.html
+++ b/main/generated/torch.signal.windows.general_cosine.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.signal.windows.general_cosine<a class="headerlink" href="#torch-signal-windows-general-cosine" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.signal.windows.general_cosine">
-<span class="sig-prename descclassname"><span class="pre">torch.signal.windows.</span></span><span class="sig-name descname"><span class="pre">general_cosine</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">M</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">a</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sym</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">layout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.strided</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">requires_grad</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/signal/windows/windows.html#general_cosine"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/signal/windows/windows.py#L674"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.signal.windows.general_cosine" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.signal.windows.</span></span><span class="sig-name descname"><span class="pre">general_cosine</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">M</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">a</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sym</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">layout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.strided</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">requires_grad</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/signal/windows/windows.html#general_cosine"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/signal/windows/windows.py#L674"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.signal.windows.general_cosine" title="Permalink to this definition">¶</a></dt>
 <dd><p>Computes the general cosine window.</p>
 <p>The general cosine window is defined as follows:</p>
 <div class="math">
diff --git a/main/generated/torch.signal.windows.general_hamming.html b/main/generated/torch.signal.windows.general_hamming.html
index d235572ea76..fa38ffacac0 100644
--- a/main/generated/torch.signal.windows.general_hamming.html
+++ b/main/generated/torch.signal.windows.general_hamming.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.signal.windows.general_hamming<a class="headerlink" href="#torch-signal-windows-general-hamming" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.signal.windows.general_hamming">
-<span class="sig-prename descclassname"><span class="pre">torch.signal.windows.</span></span><span class="sig-name descname"><span class="pre">general_hamming</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">M</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.54</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sym</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">layout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.strided</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">requires_grad</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/signal/windows/windows.html#general_hamming"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/signal/windows/windows.py#L769"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.signal.windows.general_hamming" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.signal.windows.</span></span><span class="sig-name descname"><span class="pre">general_hamming</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">M</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.54</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sym</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">layout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.strided</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">requires_grad</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/signal/windows/windows.html#general_hamming"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/signal/windows/windows.py#L769"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.signal.windows.general_hamming" title="Permalink to this definition">¶</a></dt>
 <dd><p>Computes the general Hamming window.</p>
 <p>The general Hamming window is defined as follows:</p>
 <div class="math">
diff --git a/main/generated/torch.signal.windows.hamming.html b/main/generated/torch.signal.windows.hamming.html
index f7ef2a49d1f..6444163c642 100644
--- a/main/generated/torch.signal.windows.hamming.html
+++ b/main/generated/torch.signal.windows.hamming.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.signal.windows.hamming<a class="headerlink" href="#torch-signal-windows-hamming" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.signal.windows.hamming">
-<span class="sig-prename descclassname"><span class="pre">torch.signal.windows.</span></span><span class="sig-name descname"><span class="pre">hamming</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">M</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sym</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">layout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.strided</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">requires_grad</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/signal/windows/windows.html#hamming"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/signal/windows/windows.py#L421"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.signal.windows.hamming" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.signal.windows.</span></span><span class="sig-name descname"><span class="pre">hamming</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">M</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sym</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">layout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.strided</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">requires_grad</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/signal/windows/windows.html#hamming"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/signal/windows/windows.py#L421"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.signal.windows.hamming" title="Permalink to this definition">¶</a></dt>
 <dd><p>Computes the Hamming window.</p>
 <p>The Hamming window is defined as follows:</p>
 <div class="math">
diff --git a/main/generated/torch.signal.windows.hann.html b/main/generated/torch.signal.windows.hann.html
index c040d627f18..fc4c53d0146 100644
--- a/main/generated/torch.signal.windows.hann.html
+++ b/main/generated/torch.signal.windows.hann.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.signal.windows.hann<a class="headerlink" href="#torch-signal-windows-hann" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.signal.windows.hann">
-<span class="sig-prename descclassname"><span class="pre">torch.signal.windows.</span></span><span class="sig-name descname"><span class="pre">hann</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">M</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sym</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">layout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.strided</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">requires_grad</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/signal/windows/windows.html#hann"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/signal/windows/windows.py#L478"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.signal.windows.hann" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.signal.windows.</span></span><span class="sig-name descname"><span class="pre">hann</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">M</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sym</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">layout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.strided</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">requires_grad</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/signal/windows/windows.html#hann"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/signal/windows/windows.py#L478"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.signal.windows.hann" title="Permalink to this definition">¶</a></dt>
 <dd><p>Computes the Hann window.</p>
 <p>The Hann window is defined as follows:</p>
 <div class="math">
diff --git a/main/generated/torch.signal.windows.kaiser.html b/main/generated/torch.signal.windows.kaiser.html
index 94851f4a4a3..2551f917613 100644
--- a/main/generated/torch.signal.windows.kaiser.html
+++ b/main/generated/torch.signal.windows.kaiser.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.signal.windows.kaiser<a class="headerlink" href="#torch-signal-windows-kaiser" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.signal.windows.kaiser">
-<span class="sig-prename descclassname"><span class="pre">torch.signal.windows.</span></span><span class="sig-name descname"><span class="pre">kaiser</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">M</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">beta</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">12.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sym</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">layout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.strided</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">requires_grad</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/signal/windows/windows.html#kaiser"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/signal/windows/windows.py#L334"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.signal.windows.kaiser" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.signal.windows.</span></span><span class="sig-name descname"><span class="pre">kaiser</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">M</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">beta</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">12.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sym</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">layout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.strided</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">requires_grad</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/signal/windows/windows.html#kaiser"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/signal/windows/windows.py#L334"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.signal.windows.kaiser" title="Permalink to this definition">¶</a></dt>
 <dd><p>Computes the Kaiser window.</p>
 <p>The Kaiser window is defined as follows:</p>
 <div class="math">
diff --git a/main/generated/torch.signal.windows.nuttall.html b/main/generated/torch.signal.windows.nuttall.html
index 5fdc52d95d6..e7b29b32afb 100644
--- a/main/generated/torch.signal.windows.nuttall.html
+++ b/main/generated/torch.signal.windows.nuttall.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.signal.windows.nuttall<a class="headerlink" href="#torch-signal-windows-nuttall" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.signal.windows.nuttall">
-<span class="sig-prename descclassname"><span class="pre">torch.signal.windows.</span></span><span class="sig-name descname"><span class="pre">nuttall</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">M</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sym</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">layout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.strided</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">requires_grad</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/signal/windows/windows.html#nuttall"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/signal/windows/windows.py#L827"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.signal.windows.nuttall" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.signal.windows.</span></span><span class="sig-name descname"><span class="pre">nuttall</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">M</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sym</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">layout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.strided</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">requires_grad</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/signal/windows/windows.html#nuttall"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/signal/windows/windows.py#L827"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.signal.windows.nuttall" title="Permalink to this definition">¶</a></dt>
 <dd><p>Computes the minimum 4-term Blackman-Harris window according to Nuttall.</p>
 <div class="math">
 <span class="katex-display"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML" display="block"><semantics><mrow><msub><mi>w</mi><mi>n</mi></msub><mo>=</mo><mn>1</mn><mo>−</mo><mn>0.36358</mn><mi>cos</mi><mo>⁡</mo><mrow><mo stretchy="false">(</mo><msub><mi>z</mi><mi>n</mi></msub><mo stretchy="false">)</mo></mrow><mo>+</mo><mn>0.48917</mn><mi>cos</mi><mo>⁡</mo><mrow><mo stretchy="false">(</mo><mn>2</mn><msub><mi>z</mi><mi>n</mi></msub><mo stretchy="false">)</mo></mrow><mo>−</mo><mn>0.13659</mn><mi>cos</mi><mo>⁡</mo><mrow><mo stretchy="false">(</mo><mn>3</mn><msub><mi>z</mi><mi>n</mi></msub><mo stretchy="false">)</mo></mrow><mo>+</mo><mn>0.01064</mn><mi>cos</mi><mo>⁡</mo><mrow><mo stretchy="false">(</mo><mn>4</mn><msub><mi>z</mi><mi>n</mi></msub><mo stretchy="false">)</mo></mrow></mrow><annotation encoding="application/x-tex">w_n = 1 - 0.36358 \cos{(z_n)} + 0.48917 \cos{(2z_n)} - 0.13659 \cos{(3z_n)} + 0.01064 \cos{(4z_n)}
diff --git a/main/generated/torch.signbit.html b/main/generated/torch.signbit.html
index 9424436007e..2ccec47dc83 100644
--- a/main/generated/torch.signbit.html
+++ b/main/generated/torch.signbit.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.sin.html b/main/generated/torch.sin.html
index 229ea4490b6..8aa19788b11 100644
--- a/main/generated/torch.sin.html
+++ b/main/generated/torch.sin.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.sinc.html b/main/generated/torch.sinc.html
index 994a9a49974..cb7635eca95 100644
--- a/main/generated/torch.sinc.html
+++ b/main/generated/torch.sinc.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.sinh.html b/main/generated/torch.sinh.html
index 5392de309d9..4fa9fbbb5b6 100644
--- a/main/generated/torch.sinh.html
+++ b/main/generated/torch.sinh.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.slice_scatter.html b/main/generated/torch.slice_scatter.html
index 74f6b3cffd7..ca4f9cda9ec 100644
--- a/main/generated/torch.slice_scatter.html
+++ b/main/generated/torch.slice_scatter.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.slogdet.html b/main/generated/torch.slogdet.html
index 76595a26639..b604d9c097b 100644
--- a/main/generated/torch.slogdet.html
+++ b/main/generated/torch.slogdet.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.smm.html b/main/generated/torch.smm.html
index b2672fa7f64..e9a82b4889c 100644
--- a/main/generated/torch.smm.html
+++ b/main/generated/torch.smm.html
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diff --git a/main/generated/torch.softmax.html b/main/generated/torch.softmax.html
index 04cd197ef1c..085900c7e21 100644
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+++ b/main/generated/torch.softmax.html
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diff --git a/main/generated/torch.sort.html b/main/generated/torch.sort.html
index 94f7a1d7537..6f20441a3dd 100644
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diff --git a/main/generated/torch.sparse.addmm.html b/main/generated/torch.sparse.addmm.html
index 49f21f0ad5e..cc1e180b841 100644
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diff --git a/main/generated/torch.sparse.as_sparse_gradcheck.html b/main/generated/torch.sparse.as_sparse_gradcheck.html
index a6fb2d0b9be..b5a49e7b788 100644
--- a/main/generated/torch.sparse.as_sparse_gradcheck.html
+++ b/main/generated/torch.sparse.as_sparse_gradcheck.html
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@@ -612,7 +612,7 @@
 <h1>torch.sparse.as_sparse_gradcheck<a class="headerlink" href="#torch-sparse-as-sparse-gradcheck" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.sparse.as_sparse_gradcheck">
-<span class="sig-prename descclassname"><span class="pre">torch.sparse.</span></span><span class="sig-name descname"><span class="pre">as_sparse_gradcheck</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">gradcheck</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/sparse.html#as_sparse_gradcheck"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/sparse/__init__.py#L549"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.sparse.as_sparse_gradcheck" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.sparse.</span></span><span class="sig-name descname"><span class="pre">as_sparse_gradcheck</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">gradcheck</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/sparse.html#as_sparse_gradcheck"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/sparse/__init__.py#L549"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.sparse.as_sparse_gradcheck" title="Permalink to this definition">¶</a></dt>
 <dd><p>Decorate function, to extend gradcheck for sparse tensors.</p>
 <p>Decorator for torch.autograd.gradcheck or its functools.partial
 variants that extends the gradcheck function with support to input
diff --git a/main/generated/torch.sparse.check_sparse_tensor_invariants.html b/main/generated/torch.sparse.check_sparse_tensor_invariants.html
index d9929813cce..b139ec54856 100644
--- a/main/generated/torch.sparse.check_sparse_tensor_invariants.html
+++ b/main/generated/torch.sparse.check_sparse_tensor_invariants.html
@@ -300,7 +300,7 @@
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@@ -612,7 +612,7 @@
 <h1>check_sparse_tensor_invariants<a class="headerlink" href="#check-sparse-tensor-invariants" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.sparse.check_sparse_tensor_invariants">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.sparse.</span></span><span class="sig-name descname"><span class="pre">check_sparse_tensor_invariants</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">enable</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/sparse.html#check_sparse_tensor_invariants"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/sparse/__init__.py#L437"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.sparse.check_sparse_tensor_invariants" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.sparse.</span></span><span class="sig-name descname"><span class="pre">check_sparse_tensor_invariants</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">enable</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/sparse.html#check_sparse_tensor_invariants"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/sparse/__init__.py#L437"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.sparse.check_sparse_tensor_invariants" title="Permalink to this definition">¶</a></dt>
 <dd><p>A tool to control checking sparse tensor invariants.</p>
 <p>The following options exists to manage sparsr tensor invariants
 checking in sparse tensor construction:</p>
@@ -655,14 +655,14 @@ <h1>check_sparse_tensor_invariants<a class="headerlink" href="#check-sparse-tens
 </ol>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.sparse.check_sparse_tensor_invariants.disable">
-<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">disable</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/sparse.html#check_sparse_tensor_invariants.disable"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/sparse/__init__.py#L513"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.sparse.check_sparse_tensor_invariants.disable" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">disable</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/sparse.html#check_sparse_tensor_invariants.disable"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/sparse/__init__.py#L513"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.sparse.check_sparse_tensor_invariants.disable" title="Permalink to this definition">¶</a></dt>
 <dd><p>Disable sparse tensor invariants checking in sparse tensor constructors.</p>
 <p>See <a class="reference internal" href="#torch.sparse.check_sparse_tensor_invariants.enable" title="torch.sparse.check_sparse_tensor_invariants.enable"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.sparse.check_sparse_tensor_invariants.enable()</span></code></a> for more information.</p>
 </dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.sparse.check_sparse_tensor_invariants.enable">
-<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">enable</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/sparse.html#check_sparse_tensor_invariants.enable"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/sparse/__init__.py#L493"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.sparse.check_sparse_tensor_invariants.enable" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">enable</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/sparse.html#check_sparse_tensor_invariants.enable"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/sparse/__init__.py#L493"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.sparse.check_sparse_tensor_invariants.enable" title="Permalink to this definition">¶</a></dt>
 <dd><p>Enable sparse tensor invariants checking in sparse tensor constructors.</p>
 <div class="admonition note">
 <p class="admonition-title">Note</p>
@@ -681,7 +681,7 @@ <h1>check_sparse_tensor_invariants<a class="headerlink" href="#check-sparse-tens
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.sparse.check_sparse_tensor_invariants.is_enabled">
-<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">is_enabled</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/sparse.html#check_sparse_tensor_invariants.is_enabled"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/sparse/__init__.py#L481"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.sparse.check_sparse_tensor_invariants.is_enabled" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">is_enabled</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/sparse.html#check_sparse_tensor_invariants.is_enabled"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/sparse/__init__.py#L481"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.sparse.check_sparse_tensor_invariants.is_enabled" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return True if the sparse tensor invariants checking is enabled.</p>
 <div class="admonition note">
 <p class="admonition-title">Note</p>
diff --git a/main/generated/torch.sparse.log_softmax.html b/main/generated/torch.sparse.log_softmax.html
index 0117a05e347..393ca58556c 100644
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+++ b/main/generated/torch.sparse.log_softmax.html
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diff --git a/main/generated/torch.sparse.mm.html b/main/generated/torch.sparse.mm.html
index 09390c3ec7b..af8768eca5b 100644
--- a/main/generated/torch.sparse.mm.html
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     <div class="version">
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diff --git a/main/generated/torch.sparse.sampled_addmm.html b/main/generated/torch.sparse.sampled_addmm.html
index cce71034f1d..1eb0f72ecb2 100644
--- a/main/generated/torch.sparse.sampled_addmm.html
+++ b/main/generated/torch.sparse.sampled_addmm.html
@@ -300,7 +300,7 @@
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     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
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diff --git a/main/generated/torch.sparse.softmax.html b/main/generated/torch.sparse.softmax.html
index b48fb0bc70e..0ed028c55f3 100644
--- a/main/generated/torch.sparse.softmax.html
+++ b/main/generated/torch.sparse.softmax.html
@@ -300,7 +300,7 @@
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     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
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diff --git a/main/generated/torch.sparse.spdiags.html b/main/generated/torch.sparse.spdiags.html
index cc2adf8dc0a..b8c51ddb155 100644
--- a/main/generated/torch.sparse.spdiags.html
+++ b/main/generated/torch.sparse.spdiags.html
@@ -300,7 +300,7 @@
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     <div class="version">
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diff --git a/main/generated/torch.sparse.spsolve.html b/main/generated/torch.sparse.spsolve.html
index 8d091c7ec37..ba79804b5f3 100644
--- a/main/generated/torch.sparse.spsolve.html
+++ b/main/generated/torch.sparse.spsolve.html
@@ -300,7 +300,7 @@
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     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
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diff --git a/main/generated/torch.sparse.sum.html b/main/generated/torch.sparse.sum.html
index 14949839018..f25c5de3ce7 100644
--- a/main/generated/torch.sparse.sum.html
+++ b/main/generated/torch.sparse.sum.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
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@@ -612,7 +612,7 @@
 <h1>torch.sparse.sum<a class="headerlink" href="#torch-sparse-sum" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.sparse.sum">
-<span class="sig-prename descclassname"><span class="pre">torch.sparse.</span></span><span class="sig-name descname"><span class="pre">sum</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/sparse.html#sum"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/sparse/__init__.py#L201"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.sparse.sum" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.sparse.</span></span><span class="sig-name descname"><span class="pre">sum</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/sparse.html#sum"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/sparse/__init__.py#L201"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.sparse.sum" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the sum of each row of the given sparse tensor.</p>
 <p>Returns the sum of each row of the sparse tensor <code class="xref py py-attr docutils literal notranslate"><span class="pre">input</span></code> in the given
 dimensions <code class="xref py py-attr docutils literal notranslate"><span class="pre">dim</span></code>. If <code class="xref py py-attr docutils literal notranslate"><span class="pre">dim</span></code> is a list of dimensions,
diff --git a/main/generated/torch.sparse_bsc_tensor.html b/main/generated/torch.sparse_bsc_tensor.html
index 3cf96bdd344..1ea94138367 100644
--- a/main/generated/torch.sparse_bsc_tensor.html
+++ b/main/generated/torch.sparse_bsc_tensor.html
@@ -300,7 +300,7 @@
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diff --git a/main/generated/torch.sparse_bsr_tensor.html b/main/generated/torch.sparse_bsr_tensor.html
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diff --git a/main/generated/torch.sparse_compressed_tensor.html b/main/generated/torch.sparse_compressed_tensor.html
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diff --git a/main/generated/torch.sparse_coo_tensor.html b/main/generated/torch.sparse_coo_tensor.html
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diff --git a/main/generated/torch.split.html b/main/generated/torch.split.html
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@@ -612,7 +612,7 @@
 <h1>torch.split<a class="headerlink" href="#torch-split" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.split">
-<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">split</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">split_size_or_sections</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/functional.html#split"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/functional.py#L156"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.split" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">split</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">split_size_or_sections</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/functional.html#split"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/functional.py#L156"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.split" title="Permalink to this definition">¶</a></dt>
 <dd><p>Splits the tensor into chunks. Each chunk is a view of the original tensor.</p>
 <p>If <code class="xref py py-attr docutils literal notranslate"><span class="pre">split_size_or_sections</span></code> is an integer type, then <a class="reference internal" href="torch.tensor.html#torch.tensor" title="torch.tensor"><code class="xref py py-attr docutils literal notranslate"><span class="pre">tensor</span></code></a> will
 be split into equally sized chunks (if possible). Last chunk will be smaller if
diff --git a/main/generated/torch.sqrt.html b/main/generated/torch.sqrt.html
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diff --git a/main/generated/torch.square.html b/main/generated/torch.square.html
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diff --git a/main/generated/torch.squeeze.html b/main/generated/torch.squeeze.html
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diff --git a/main/generated/torch.sspaddmm.html b/main/generated/torch.sspaddmm.html
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diff --git a/main/generated/torch.stack.html b/main/generated/torch.stack.html
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+++ b/main/generated/torch.stack.html
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diff --git a/main/generated/torch.std.html b/main/generated/torch.std.html
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diff --git a/main/generated/torch.stft.html b/main/generated/torch.stft.html
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@@ -612,7 +612,7 @@
 <h1>torch.stft<a class="headerlink" href="#torch-stft" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.stft">
-<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">stft</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_fft</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">hop_length</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">win_length</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">window</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">center</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pad_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'reflect'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">normalized</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">onesided</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_complex</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/functional.html#stft"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/functional.py#L543"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.stft" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">stft</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_fft</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">hop_length</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">win_length</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">window</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">center</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pad_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'reflect'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">normalized</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">onesided</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_complex</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/functional.html#stft"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/functional.py#L543"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.stft" title="Permalink to this definition">¶</a></dt>
 <dd><p>Short-time Fourier transform (STFT).</p>
 <div class="admonition warning">
 <p class="admonition-title">Warning</p>
diff --git a/main/generated/torch.sub.html b/main/generated/torch.sub.html
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diff --git a/main/generated/torch.subtract.html b/main/generated/torch.subtract.html
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index bbf545e6079..71d49d9b019 100644
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@@ -612,7 +612,7 @@
 <h1>torch.svd_lowrank<a class="headerlink" href="#torch-svd-lowrank" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.svd_lowrank">
-<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">svd_lowrank</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">A</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">q</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">6</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">niter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">M</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/_lowrank.html#svd_lowrank"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/_lowrank.py#L86"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.svd_lowrank" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">svd_lowrank</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">A</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">q</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">6</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">niter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">M</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/_lowrank.html#svd_lowrank"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/_lowrank.py#L86"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.svd_lowrank" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the singular value decomposition <code class="docutils literal notranslate"><span class="pre">(U,</span> <span class="pre">S,</span> <span class="pre">V)</span></code> of a matrix,
 batches of matrices, or a sparse matrix <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>A</mi></mrow><annotation encoding="application/x-tex">A</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.6833em;"></span><span class="mord mathnormal">A</span></span></span></span></span> such that
 <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>A</mi><mo>≈</mo><mi>U</mi><mi mathvariant="normal">diag</mi><mo>⁡</mo><mo stretchy="false">(</mo><mi>S</mi><mo stretchy="false">)</mo><msup><mi>V</mi><mtext>H</mtext></msup></mrow><annotation encoding="application/x-tex">A \approx U \operatorname{diag}(S) V^{\text{H}}</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.6833em;"></span><span class="mord mathnormal">A</span><span class="mspace" style="margin-right:0.2778em;"></span><span class="mrel">≈</span><span class="mspace" style="margin-right:0.2778em;"></span></span><span class="base"><span class="strut" style="height:1.0913em;vertical-align:-0.25em;"></span><span class="mord mathnormal" style="margin-right:0.10903em;">U</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mop"><span class="mord mathrm" style="margin-right:0.01389em;">diag</span></span><span class="mopen">(</span><span class="mord mathnormal" style="margin-right:0.05764em;">S</span><span class="mclose">)</span><span class="mord"><span class="mord mathnormal" style="margin-right:0.22222em;">V</span><span class="msupsub"><span class="vlist-t"><span class="vlist-r"><span class="vlist" style="height:0.8413em;"><span style="top:-3.063em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mord text mtight"><span class="mord mtight">H</span></span></span></span></span></span></span></span></span></span></span></span></span></span>. In case <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>M</mi></mrow><annotation encoding="application/x-tex">M</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.6833em;"></span><span class="mord mathnormal" style="margin-right:0.10903em;">M</span></span></span></span></span> is given, then
diff --git a/main/generated/torch.swapaxes.html b/main/generated/torch.swapaxes.html
index 54ac2fa4db3..46dbc272fc3 100644
--- a/main/generated/torch.swapaxes.html
+++ b/main/generated/torch.swapaxes.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.swapdims.html b/main/generated/torch.swapdims.html
index 16169e6d674..437ecbcf3a4 100644
--- a/main/generated/torch.swapdims.html
+++ b/main/generated/torch.swapdims.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.sym_float.html b/main/generated/torch.sym_float.html
index 11c53141461..8f3cd4b0f50 100644
--- a/main/generated/torch.sym_float.html
+++ b/main/generated/torch.sym_float.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.sym_float<a class="headerlink" href="#torch-sym-float" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.sym_float">
-<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">sym_float</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">a</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch.html#sym_float"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/__init__.py#L805"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.sym_float" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">sym_float</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">a</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch.html#sym_float"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/__init__.py#L805"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.sym_float" title="Permalink to this definition">¶</a></dt>
 <dd><p>SymInt-aware utility for float casting.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.sym_fresh_size.html b/main/generated/torch.sym_fresh_size.html
index 1c5b66eb555..95c6ebc7a7f 100644
--- a/main/generated/torch.sym_fresh_size.html
+++ b/main/generated/torch.sym_fresh_size.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.sym_fresh_size<a class="headerlink" href="#torch-sym-fresh-size" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.sym_fresh_size">
-<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">sym_fresh_size</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">expr</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch.html#sym_fresh_size"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/__init__.py#L972"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.sym_fresh_size" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">sym_fresh_size</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">expr</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch.html#sym_fresh_size"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/__init__.py#L972"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.sym_fresh_size" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 </div>
diff --git a/main/generated/torch.sym_int.html b/main/generated/torch.sym_int.html
index a4500679409..b89b8d35b15 100644
--- a/main/generated/torch.sym_int.html
+++ b/main/generated/torch.sym_int.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.sym_int<a class="headerlink" href="#torch-sym-int" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.sym_int">
-<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">sym_int</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">a</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch.html#sym_int"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/__init__.py#L820"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.sym_int" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">sym_int</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">a</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch.html#sym_int"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/__init__.py#L820"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.sym_int" title="Permalink to this definition">¶</a></dt>
 <dd><p>SymInt-aware utility for int casting.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.sym_ite.html b/main/generated/torch.sym_ite.html
index f0031000b36..a36b5cfe612 100644
--- a/main/generated/torch.sym_ite.html
+++ b/main/generated/torch.sym_ite.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.sym_ite<a class="headerlink" href="#torch-sym-ite" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.sym_ite">
-<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">sym_ite</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">b</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">t</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">f</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch.html#sym_ite"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/__init__.py#L962"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.sym_ite" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">sym_ite</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">b</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">t</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">f</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch.html#sym_ite"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/__init__.py#L962"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.sym_ite" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 </div>
diff --git a/main/generated/torch.sym_max.html b/main/generated/torch.sym_max.html
index e775dce560c..8eaa700891c 100644
--- a/main/generated/torch.sym_max.html
+++ b/main/generated/torch.sym_max.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.sym_max<a class="headerlink" href="#torch-sym-max" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.sym_max">
-<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">sym_max</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">a</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">b</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch.html#sym_max"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/__init__.py#L835"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.sym_max" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">sym_max</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">a</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">b</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch.html#sym_max"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/__init__.py#L835"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.sym_max" title="Permalink to this definition">¶</a></dt>
 <dd><p>SymInt-aware utility for max which avoids branching on a &lt; b.
 Unlike builtins.max(), this only works for int/float, and it always
 promotes to float if any argument is float (unlike builtins.max, which
diff --git a/main/generated/torch.sym_min.html b/main/generated/torch.sym_min.html
index d87ebae50ef..be2aa5b89e8 100644
--- a/main/generated/torch.sym_min.html
+++ b/main/generated/torch.sym_min.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.sym_min<a class="headerlink" href="#torch-sym-min" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.sym_min">
-<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">sym_min</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">a</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">b</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch.html#sym_min"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/__init__.py#L880"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.sym_min" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">sym_min</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">a</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">b</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch.html#sym_min"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/__init__.py#L880"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.sym_min" title="Permalink to this definition">¶</a></dt>
 <dd><p>SymInt-aware utility for min().</p>
 </dd></dl>
 
diff --git a/main/generated/torch.sym_not.html b/main/generated/torch.sym_not.html
index 7c7f5d2d1a0..552ec6861ba 100644
--- a/main/generated/torch.sym_not.html
+++ b/main/generated/torch.sym_not.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
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     <div id="searchBox">
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@@ -612,7 +612,7 @@
 <h1>torch.sym_not<a class="headerlink" href="#torch-sym-not" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.sym_not">
-<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">sym_not</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">a</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch.html#sym_not"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/__init__.py#L788"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.sym_not" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">sym_not</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">a</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch.html#sym_not"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/__init__.py#L788"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.sym_not" title="Permalink to this definition">¶</a></dt>
 <dd><p>SymInt-aware utility for logical negation.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.sym_sum.html b/main/generated/torch.sym_sum.html
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@@ -612,7 +612,7 @@
 <h1>torch.sym_sum<a class="headerlink" href="#torch-sym-sum" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.sym_sum">
-<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">sym_sum</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">args</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch.html#sym_sum"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/__init__.py#L899"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.sym_sum" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">sym_sum</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">args</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch.html#sym_sum"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/__init__.py#L899"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.sym_sum" title="Permalink to this definition">¶</a></dt>
 <dd><p>N-ary add which is faster to compute for long lists than iterated binary
 addition.  Only does something special for integers.</p>
 </dd></dl>
diff --git a/main/generated/torch.t.html b/main/generated/torch.t.html
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@@ -612,7 +612,7 @@
 <h1>torch.tensordot<a class="headerlink" href="#torch-tensordot" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.tensordot">
-<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">tensordot</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">a</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">b</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dims</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/functional.html#tensordot"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/functional.py#L1264"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.tensordot" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">tensordot</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">a</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">b</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dims</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/functional.html#tensordot"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/functional.py#L1264"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.tensordot" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns a contraction of a and b over multiple dimensions.</p>
 <p><a class="reference internal" href="#torch.tensordot" title="torch.tensordot"><code class="xref py py-attr docutils literal notranslate"><span class="pre">tensordot</span></code></a> implements a generalized matrix product.</p>
 <dl class="field-list simple">
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-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
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diff --git a/main/generated/torch.unique.html b/main/generated/torch.unique.html
index f4cb49c538e..31eedad5663 100644
--- a/main/generated/torch.unique.html
+++ b/main/generated/torch.unique.html
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-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
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@@ -612,7 +612,7 @@
 <h1>torch.unique<a class="headerlink" href="#torch-unique" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.unique">
-<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">unique</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sorted</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_inverse</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_counts</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.13)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><span class="pre">Tensor</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><span class="pre">Tensor</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><span class="pre">Tensor</span></a><span class="p"><span class="pre">]</span></span></span></span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/_jit_internal.py#L613"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.unique" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">unique</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sorted</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_inverse</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_counts</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.13)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><span class="pre">Tensor</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><span class="pre">Tensor</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><span class="pre">Tensor</span></a><span class="p"><span class="pre">]</span></span></span></span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/_jit_internal.py#L613"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.unique" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns the unique elements of the input tensor.</p>
 <div class="admonition note">
 <p class="admonition-title">Note</p>
diff --git a/main/generated/torch.unique_consecutive.html b/main/generated/torch.unique_consecutive.html
index 20bf7c6d862..989a8ddbba7 100644
--- a/main/generated/torch.unique_consecutive.html
+++ b/main/generated/torch.unique_consecutive.html
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           <div class="pytorch-left-menu-search">
             
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-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
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     <div id="searchBox">
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@@ -612,7 +612,7 @@
 <h1>torch.unique_consecutive<a class="headerlink" href="#torch-unique-consecutive" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.unique_consecutive">
-<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">unique_consecutive</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/_jit_internal.py#L613"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.unique_consecutive" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">unique_consecutive</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/_jit_internal.py#L613"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.unique_consecutive" title="Permalink to this definition">¶</a></dt>
 <dd><p>Eliminates all but the first element from every consecutive group of equivalent elements.</p>
 <div class="admonition note">
 <p class="admonition-title">Note</p>
diff --git a/main/generated/torch.unravel_index.html b/main/generated/torch.unravel_index.html
index 8f6d97e8386..f7d38d51a51 100644
--- a/main/generated/torch.unravel_index.html
+++ b/main/generated/torch.unravel_index.html
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-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.unravel_index<a class="headerlink" href="#torch-unravel-index" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.unravel_index">
-<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">unravel_index</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">indices</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shape</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/functional.html#unravel_index"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/functional.py#L1897"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.unravel_index" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">unravel_index</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">indices</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shape</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/functional.html#unravel_index"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/functional.py#L1897"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.unravel_index" title="Permalink to this definition">¶</a></dt>
 <dd><p>Converts a tensor of flat indices into a tuple of coordinate tensors that
 index into an arbitrary tensor of the specified shape.</p>
 <dl class="field-list simple">
diff --git a/main/generated/torch.unsqueeze.html b/main/generated/torch.unsqueeze.html
index 404adf6c195..0f7fb7faece 100644
--- a/main/generated/torch.unsqueeze.html
+++ b/main/generated/torch.unsqueeze.html
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     <div id="searchBox">
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diff --git a/main/generated/torch.use_deterministic_algorithms.html b/main/generated/torch.use_deterministic_algorithms.html
index 58cbbe27982..2d672845ccd 100644
--- a/main/generated/torch.use_deterministic_algorithms.html
+++ b/main/generated/torch.use_deterministic_algorithms.html
@@ -300,7 +300,7 @@
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     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.use_deterministic_algorithms<a class="headerlink" href="#torch-use-deterministic-algorithms" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.use_deterministic_algorithms">
-<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">use_deterministic_algorithms</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mode</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">warn_only</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch.html#use_deterministic_algorithms"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/__init__.py#L1288"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.use_deterministic_algorithms" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">use_deterministic_algorithms</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mode</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">warn_only</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch.html#use_deterministic_algorithms"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/__init__.py#L1288"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.use_deterministic_algorithms" title="Permalink to this definition">¶</a></dt>
 <dd><p>Sets whether PyTorch operations must use “deterministic”
 algorithms. That is, algorithms which, given the same input, and when
 run on the same software and hardware, always produce the same output.
diff --git a/main/generated/torch.utils.generate_methods_for_privateuse1_backend.html b/main/generated/torch.utils.generate_methods_for_privateuse1_backend.html
index 9ebf4de53c4..8894374ea65 100644
--- a/main/generated/torch.utils.generate_methods_for_privateuse1_backend.html
+++ b/main/generated/torch.utils.generate_methods_for_privateuse1_backend.html
@@ -300,7 +300,7 @@
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     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.utils.generate_methods_for_privateuse1_backend<a class="headerlink" href="#torch-utils-generate-methods-for-privateuse1-backend" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.utils.generate_methods_for_privateuse1_backend">
-<span class="sig-prename descclassname"><span class="pre">torch.utils.</span></span><span class="sig-name descname"><span class="pre">generate_methods_for_privateuse1_backend</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">for_tensor</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">for_module</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">for_packed_sequence</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">for_storage</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">unsupported_dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/utils/backend_registration.html#generate_methods_for_privateuse1_backend"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/utils/backend_registration.py#L295"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.generate_methods_for_privateuse1_backend" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.utils.</span></span><span class="sig-name descname"><span class="pre">generate_methods_for_privateuse1_backend</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">for_tensor</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">for_module</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">for_packed_sequence</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">for_storage</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">unsupported_dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/utils/backend_registration.html#generate_methods_for_privateuse1_backend"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/utils/backend_registration.py#L295"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.generate_methods_for_privateuse1_backend" title="Permalink to this definition">¶</a></dt>
 <dd><p>Automatically generate attributes and methods for the custom backend after rename privateuse1 backend.</p>
 <p>In the default scenario, storage-related methods will not be generated automatically.</p>
 <p>When you implement kernels for various torch operations, and register them to the PrivateUse1 dispatch key.
diff --git a/main/generated/torch.utils.get_cpp_backtrace.html b/main/generated/torch.utils.get_cpp_backtrace.html
index d94b9957c88..d1cd5f31bb8 100644
--- a/main/generated/torch.utils.get_cpp_backtrace.html
+++ b/main/generated/torch.utils.get_cpp_backtrace.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.utils.get_cpp_backtrace<a class="headerlink" href="#torch-utils-get-cpp-backtrace" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.utils.get_cpp_backtrace">
-<span class="sig-prename descclassname"><span class="pre">torch.utils.</span></span><span class="sig-name descname"><span class="pre">get_cpp_backtrace</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">frames_to_skip</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">maximum_number_of_frames</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">64</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/utils/cpp_backtrace.html#get_cpp_backtrace"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/utils/cpp_backtrace.py#L4"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.get_cpp_backtrace" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.utils.</span></span><span class="sig-name descname"><span class="pre">get_cpp_backtrace</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">frames_to_skip</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">maximum_number_of_frames</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">64</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/utils/cpp_backtrace.html#get_cpp_backtrace"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/utils/cpp_backtrace.py#L4"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.get_cpp_backtrace" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return a string containing the C++ stack trace of the current thread.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.utils.rename_privateuse1_backend.html b/main/generated/torch.utils.rename_privateuse1_backend.html
index 72c356093dc..da8c72d1d36 100644
--- a/main/generated/torch.utils.rename_privateuse1_backend.html
+++ b/main/generated/torch.utils.rename_privateuse1_backend.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.utils.rename_privateuse1_backend<a class="headerlink" href="#torch-utils-rename-privateuse1-backend" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.utils.rename_privateuse1_backend">
-<span class="sig-prename descclassname"><span class="pre">torch.utils.</span></span><span class="sig-name descname"><span class="pre">rename_privateuse1_backend</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">backend_name</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/utils/backend_registration.html#rename_privateuse1_backend"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/utils/backend_registration.py#L18"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.rename_privateuse1_backend" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.utils.</span></span><span class="sig-name descname"><span class="pre">rename_privateuse1_backend</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">backend_name</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/utils/backend_registration.html#rename_privateuse1_backend"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/utils/backend_registration.py#L18"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.rename_privateuse1_backend" title="Permalink to this definition">¶</a></dt>
 <dd><p>Rename the privateuse1 backend device to make it more convenient to use as a device name within PyTorch APIs.</p>
 <p>The steps are:</p>
 <ol class="arabic simple">
diff --git a/main/generated/torch.utils.set_module.html b/main/generated/torch.utils.set_module.html
index 2baf1121f9e..88af573b10a 100644
--- a/main/generated/torch.utils.set_module.html
+++ b/main/generated/torch.utils.set_module.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.utils.set_module<a class="headerlink" href="#torch-utils-set-module" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.utils.set_module">
-<span class="sig-prename descclassname"><span class="pre">torch.utils.</span></span><span class="sig-name descname"><span class="pre">set_module</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">obj</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mod</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/utils.html#set_module"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/utils/__init__.py#L23"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.set_module" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.utils.</span></span><span class="sig-name descname"><span class="pre">set_module</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">obj</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mod</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/utils.html#set_module"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/utils/__init__.py#L23"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.set_module" title="Permalink to this definition">¶</a></dt>
 <dd><p>Set the module attribute on a python object for a given object for nicer printing</p>
 </dd></dl>
 
diff --git a/main/generated/torch.utils.swap_tensors.html b/main/generated/torch.utils.swap_tensors.html
index a42411d475f..9d137e4808d 100644
--- a/main/generated/torch.utils.swap_tensors.html
+++ b/main/generated/torch.utils.swap_tensors.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.utils.swap_tensors<a class="headerlink" href="#torch-utils-swap-tensors" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.utils.swap_tensors">
-<span class="sig-prename descclassname"><span class="pre">torch.utils.</span></span><span class="sig-name descname"><span class="pre">swap_tensors</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">t1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">t2</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/utils.html#swap_tensors"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/utils/__init__.py#L41"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.swap_tensors" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.utils.</span></span><span class="sig-name descname"><span class="pre">swap_tensors</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">t1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">t2</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/utils.html#swap_tensors"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/utils/__init__.py#L41"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.swap_tensors" title="Permalink to this definition">¶</a></dt>
 <dd><p>This function swaps the content of the two Tensor objects.
 At a high level, this will make t1 have the content of t2 while preserving
 its identity.</p>
diff --git a/main/generated/torch.vander.html b/main/generated/torch.vander.html
index 419f0740519..f46557d48da 100644
--- a/main/generated/torch.vander.html
+++ b/main/generated/torch.vander.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.var.html b/main/generated/torch.var.html
index 06cece7d8a4..6ead9eb1e0c 100644
--- a/main/generated/torch.var.html
+++ b/main/generated/torch.var.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.var_mean.html b/main/generated/torch.var_mean.html
index 5cedaffd916..8ece422c957 100644
--- a/main/generated/torch.var_mean.html
+++ b/main/generated/torch.var_mean.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.vdot.html b/main/generated/torch.vdot.html
index 01a6899ca50..25b9a13d56c 100644
--- a/main/generated/torch.vdot.html
+++ b/main/generated/torch.vdot.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.view_as_complex.html b/main/generated/torch.view_as_complex.html
index 1abafea8aff..135d09498f9 100644
--- a/main/generated/torch.view_as_complex.html
+++ b/main/generated/torch.view_as_complex.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.view_as_real.html b/main/generated/torch.view_as_real.html
index c312678a689..7483f1ca524 100644
--- a/main/generated/torch.view_as_real.html
+++ b/main/generated/torch.view_as_real.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.vmap.html b/main/generated/torch.vmap.html
index 53d20924316..3cf538e8eac 100644
--- a/main/generated/torch.vmap.html
+++ b/main/generated/torch.vmap.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.vmap<a class="headerlink" href="#torch-vmap" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.vmap">
-<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">vmap</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">func</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">in_dims</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out_dims</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">randomness</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'error'</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">chunk_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/_functorch/apis.py#L30"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.vmap" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">vmap</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">func</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">in_dims</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out_dims</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">randomness</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'error'</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">chunk_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/_functorch/apis.py#L30"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.vmap" title="Permalink to this definition">¶</a></dt>
 <dd><p>vmap is the vectorizing map; <code class="docutils literal notranslate"><span class="pre">vmap(func)</span></code> returns a new function that
 maps <code class="docutils literal notranslate"><span class="pre">func</span></code> over some dimension of the inputs. Semantically, vmap
 pushes the map into PyTorch operations called by <code class="docutils literal notranslate"><span class="pre">func</span></code>, effectively
diff --git a/main/generated/torch.vsplit.html b/main/generated/torch.vsplit.html
index bf92e1069f7..5cb21c263dd 100644
--- a/main/generated/torch.vsplit.html
+++ b/main/generated/torch.vsplit.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.vstack.html b/main/generated/torch.vstack.html
index 596ec71adcc..1b0e63a9bc2 100644
--- a/main/generated/torch.vstack.html
+++ b/main/generated/torch.vstack.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.where.html b/main/generated/torch.where.html
index 9baf7fec8a5..2a93f04b8e8 100644
--- a/main/generated/torch.where.html
+++ b/main/generated/torch.where.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.xlogy.html b/main/generated/torch.xlogy.html
index 3a412cabe93..753dde1cd81 100644
--- a/main/generated/torch.xlogy.html
+++ b/main/generated/torch.xlogy.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
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diff --git a/main/generated/torch.xpu.Event.html b/main/generated/torch.xpu.Event.html
index 2f2779e2f83..b5e47167ca5 100644
--- a/main/generated/torch.xpu.Event.html
+++ b/main/generated/torch.xpu.Event.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>Event<a class="headerlink" href="#event" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.xpu.Event">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.xpu.</span></span><span class="sig-name descname"><span class="pre">Event</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">enable_timing</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/xpu/streams.html#Event"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/xpu/streams.py#L103"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.xpu.Event" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.xpu.</span></span><span class="sig-name descname"><span class="pre">Event</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">enable_timing</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/xpu/streams.html#Event"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/xpu/streams.py#L103"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.xpu.Event" title="Permalink to this definition">¶</a></dt>
 <dd><p>Wrapper around a XPU event.</p>
 <p>XPU events are synchronization markers that can be used to monitor the
 device’s progress, and to synchronize XPU streams.</p>
@@ -627,7 +627,7 @@ <h1>Event<a class="headerlink" href="#event" title="Permalink to this heading">
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.xpu.Event.elapsed_time">
-<span class="sig-name descname"><span class="pre">elapsed_time</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">end_event</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/xpu/streams.html#Event.elapsed_time"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/xpu/streams.py#L149"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.xpu.Event.elapsed_time" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">elapsed_time</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">end_event</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/xpu/streams.html#Event.elapsed_time"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/xpu/streams.py#L149"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.xpu.Event.elapsed_time" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the time elapsed.</p>
 <p>Time reported in milliseconds after the event was recorded and
 before the end_event was recorded.</p>
@@ -635,7 +635,7 @@ <h1>Event<a class="headerlink" href="#event" title="Permalink to this heading">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.xpu.Event.query">
-<span class="sig-name descname"><span class="pre">query</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/xpu/streams.html#Event.query"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/xpu/streams.py#L140"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.xpu.Event.query" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">query</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/xpu/streams.html#Event.query"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/xpu/streams.py#L140"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.xpu.Event.query" title="Permalink to this definition">¶</a></dt>
 <dd><p>Check if all work currently captured by event has completed.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Returns</dt>
@@ -650,7 +650,7 @@ <h1>Event<a class="headerlink" href="#event" title="Permalink to this heading">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.xpu.Event.record">
-<span class="sig-name descname"><span class="pre">record</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">stream</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/xpu/streams.html#Event.record"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/xpu/streams.py#L121"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.xpu.Event.record" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">record</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">stream</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/xpu/streams.html#Event.record"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/xpu/streams.py#L121"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.xpu.Event.record" title="Permalink to this definition">¶</a></dt>
 <dd><p>Record the event in a given stream.</p>
 <p>Uses <code class="docutils literal notranslate"><span class="pre">torch.xpu.current_stream()</span></code> if no stream is specified. The
 stream’s device must match the event’s device.</p>
@@ -660,7 +660,7 @@ <h1>Event<a class="headerlink" href="#event" title="Permalink to this heading">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.xpu.Event.synchronize">
-<span class="sig-name descname"><span class="pre">synchronize</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/xpu/streams.html#Event.synchronize"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/xpu/streams.py#L157"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.xpu.Event.synchronize" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">synchronize</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/xpu/streams.html#Event.synchronize"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/xpu/streams.py#L157"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.xpu.Event.synchronize" title="Permalink to this definition">¶</a></dt>
 <dd><p>Wait for the event to complete.</p>
 <p>Waits until the completion of all work currently captured in this event.
 This prevents the CPU thread from proceeding until the event completes.</p>
@@ -670,7 +670,7 @@ <h1>Event<a class="headerlink" href="#event" title="Permalink to this heading">
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.xpu.Event.wait">
-<span class="sig-name descname"><span class="pre">wait</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">stream</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/xpu/streams.html#Event.wait"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/xpu/streams.py#L131"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.xpu.Event.wait" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">wait</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">stream</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/xpu/streams.html#Event.wait"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/xpu/streams.py#L131"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.xpu.Event.wait" title="Permalink to this definition">¶</a></dt>
 <dd><p>Make all future work submitted to the given stream wait for this event.</p>
 <p>Use <code class="docutils literal notranslate"><span class="pre">torch.xpu.current_stream()</span></code> if no stream is specified.</p>
 <dl class="field-list simple">
diff --git a/main/generated/torch.xpu.Stream.html b/main/generated/torch.xpu.Stream.html
index 7479a98909e..52aa4e13c54 100644
--- a/main/generated/torch.xpu.Stream.html
+++ b/main/generated/torch.xpu.Stream.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>Stream<a class="headerlink" href="#stream" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.xpu.Stream">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.xpu.</span></span><span class="sig-name descname"><span class="pre">Stream</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">priority</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/xpu/streams.html#Stream"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/xpu/streams.py#L14"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.xpu.Stream" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.xpu.</span></span><span class="sig-name descname"><span class="pre">Stream</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">priority</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/xpu/streams.html#Stream"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/xpu/streams.py#L14"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.xpu.Stream" title="Permalink to this definition">¶</a></dt>
 <dd><p>Wrapper around a XPU stream.</p>
 <p>A XPU stream is a linear sequence of execution that belongs to a specific
 device, independent from other streams. It supports with statement as a
@@ -634,7 +634,7 @@ <h1>Stream<a class="headerlink" href="#stream" title="Permalink to this heading"
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.xpu.Stream.query">
-<span class="sig-name descname"><span class="pre">query</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/xpu/streams.html#Stream.query"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/xpu/streams.py#L75"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.xpu.Stream.query" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">query</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/xpu/streams.html#Stream.query"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/xpu/streams.py#L75"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.xpu.Stream.query" title="Permalink to this definition">¶</a></dt>
 <dd><p>Check if all the work submitted has been completed.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Returns</dt>
@@ -648,7 +648,7 @@ <h1>Stream<a class="headerlink" href="#stream" title="Permalink to this heading"
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.xpu.Stream.record_event">
-<span class="sig-name descname"><span class="pre">record_event</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">event</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/xpu/streams.html#Stream.record_event"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/xpu/streams.py#L60"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.xpu.Stream.record_event" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">record_event</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">event</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/xpu/streams.html#Stream.record_event"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/xpu/streams.py#L60"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.xpu.Stream.record_event" title="Permalink to this definition">¶</a></dt>
 <dd><p>Record an event.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -663,7 +663,7 @@ <h1>Stream<a class="headerlink" href="#stream" title="Permalink to this heading"
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.xpu.Stream.synchronize">
-<span class="sig-name descname"><span class="pre">synchronize</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/xpu/streams.html#Stream.synchronize"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/xpu/streams.py#L83"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.xpu.Stream.synchronize" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">synchronize</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/xpu/streams.html#Stream.synchronize"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/xpu/streams.py#L83"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.xpu.Stream.synchronize" title="Permalink to this definition">¶</a></dt>
 <dd><p>Wait for all the kernels in this stream to complete.</p>
 <dl class="field-list simple">
 </dl>
@@ -671,7 +671,7 @@ <h1>Stream<a class="headerlink" href="#stream" title="Permalink to this heading"
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.xpu.Stream.wait_event">
-<span class="sig-name descname"><span class="pre">wait_event</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">event</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/xpu/streams.html#Stream.wait_event"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/xpu/streams.py#L41"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.xpu.Stream.wait_event" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">wait_event</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">event</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/xpu/streams.html#Stream.wait_event"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/xpu/streams.py#L41"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.xpu.Stream.wait_event" title="Permalink to this definition">¶</a></dt>
 <dd><p>Make all future work submitted to the stream wait for an event.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -682,7 +682,7 @@ <h1>Stream<a class="headerlink" href="#stream" title="Permalink to this heading"
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.xpu.Stream.wait_stream">
-<span class="sig-name descname"><span class="pre">wait_stream</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">stream</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/xpu/streams.html#Stream.wait_stream"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/xpu/streams.py#L49"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.xpu.Stream.wait_stream" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">wait_stream</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">stream</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/xpu/streams.html#Stream.wait_stream"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/xpu/streams.py#L49"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.xpu.Stream.wait_stream" title="Permalink to this definition">¶</a></dt>
 <dd><p>Synchronize with another stream.</p>
 <p>All future work submitted to this stream will wait until all kernels
 submitted to a given stream at the time of call complete.</p>
diff --git a/main/generated/torch.xpu.StreamContext.html b/main/generated/torch.xpu.StreamContext.html
index cf9bf2d9b57..82fba065a06 100644
--- a/main/generated/torch.xpu.StreamContext.html
+++ b/main/generated/torch.xpu.StreamContext.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>StreamContext<a class="headerlink" href="#streamcontext" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.xpu.StreamContext">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.xpu.</span></span><span class="sig-name descname"><span class="pre">StreamContext</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">stream</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/xpu.html#StreamContext"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/xpu/__init__.py#L279"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.xpu.StreamContext" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.xpu.</span></span><span class="sig-name descname"><span class="pre">StreamContext</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">stream</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/xpu.html#StreamContext"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/xpu/__init__.py#L279"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.xpu.StreamContext" title="Permalink to this definition">¶</a></dt>
 <dd><p>Context-manager that selects a given stream.</p>
 <p>All XPU kernels queued within its context will be enqueued on a selected
 stream.</p>
diff --git a/main/generated/torch.xpu.current_device.html b/main/generated/torch.xpu.current_device.html
index 32f37cb370a..298de0a557b 100644
--- a/main/generated/torch.xpu.current_device.html
+++ b/main/generated/torch.xpu.current_device.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.xpu.current_device<a class="headerlink" href="#torch-xpu-current-device" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.xpu.current_device">
-<span class="sig-prename descclassname"><span class="pre">torch.xpu.</span></span><span class="sig-name descname"><span class="pre">current_device</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/xpu.html#current_device"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/xpu/__init__.py#L260"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.xpu.current_device" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.xpu.</span></span><span class="sig-name descname"><span class="pre">current_device</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/xpu.html#current_device"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/xpu/__init__.py#L260"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.xpu.current_device" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the index of a currently selected device.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
diff --git a/main/generated/torch.xpu.current_stream.html b/main/generated/torch.xpu.current_stream.html
index d1d3e952c02..3056db3d65e 100644
--- a/main/generated/torch.xpu.current_stream.html
+++ b/main/generated/torch.xpu.current_stream.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.xpu.current_stream<a class="headerlink" href="#torch-xpu-current-stream" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.xpu.current_stream">
-<span class="sig-prename descclassname"><span class="pre">torch.xpu.</span></span><span class="sig-name descname"><span class="pre">current_stream</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/xpu.html#current_stream"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/xpu/__init__.py#L363"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.xpu.current_stream" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.xpu.</span></span><span class="sig-name descname"><span class="pre">current_stream</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/xpu.html#current_stream"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/xpu/__init__.py#L363"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.xpu.current_stream" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the currently selected <a class="reference internal" href="torch.xpu.Stream.html#torch.xpu.Stream" title="torch.xpu.Stream"><code class="xref py py-class docutils literal notranslate"><span class="pre">Stream</span></code></a> for a given device.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.xpu.device.html b/main/generated/torch.xpu.device.html
index 2998cba73bf..06339e6d747 100644
--- a/main/generated/torch.xpu.device.html
+++ b/main/generated/torch.xpu.device.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>device<a class="headerlink" href="#device" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.xpu.device">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.xpu.</span></span><span class="sig-name descname"><span class="pre">device</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/xpu.html#device"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/xpu/__init__.py#L155"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.xpu.device" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.xpu.</span></span><span class="sig-name descname"><span class="pre">device</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/xpu.html#device"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/xpu/__init__.py#L155"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.xpu.device" title="Permalink to this definition">¶</a></dt>
 <dd><p>Context-manager that changes the selected device.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.xpu.device_count.html b/main/generated/torch.xpu.device_count.html
index efbaccec266..9e2fac93112 100644
--- a/main/generated/torch.xpu.device_count.html
+++ b/main/generated/torch.xpu.device_count.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.xpu.device_count<a class="headerlink" href="#torch-xpu-device-count" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.xpu.device_count">
-<span class="sig-prename descclassname"><span class="pre">torch.xpu.</span></span><span class="sig-name descname"><span class="pre">device_count</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/xpu.html#device_count"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/xpu/__init__.py#L55"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.xpu.device_count" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.xpu.</span></span><span class="sig-name descname"><span class="pre">device_count</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/xpu.html#device_count"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/xpu/__init__.py#L55"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.xpu.device_count" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the number of XPU device available.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
diff --git a/main/generated/torch.xpu.device_of.html b/main/generated/torch.xpu.device_of.html
index b4a5a33daa7..45cdcf826aa 100644
--- a/main/generated/torch.xpu.device_of.html
+++ b/main/generated/torch.xpu.device_of.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>device_of<a class="headerlink" href="#device-of" title="Permalink to this heading">¶</a></h1>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.xpu.device_of">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.xpu.</span></span><span class="sig-name descname"><span class="pre">device_of</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">obj</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/xpu.html#device_of"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/xpu/__init__.py#L175"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.xpu.device_of" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.xpu.</span></span><span class="sig-name descname"><span class="pre">device_of</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">obj</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/xpu.html#device_of"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/xpu/__init__.py#L175"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.xpu.device_of" title="Permalink to this definition">¶</a></dt>
 <dd><p>Context-manager that changes the current device to that of given object.</p>
 <p>You can use both tensors and storages as arguments. If a given object is
 not allocated on a XPU, this is a no-op.</p>
diff --git a/main/generated/torch.xpu.empty_cache.html b/main/generated/torch.xpu.empty_cache.html
index 7cac1376a52..13d6196bd71 100644
--- a/main/generated/torch.xpu.empty_cache.html
+++ b/main/generated/torch.xpu.empty_cache.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.xpu.empty_cache<a class="headerlink" href="#torch-xpu-empty-cache" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.xpu.empty_cache">
-<span class="sig-prename descclassname"><span class="pre">torch.xpu.</span></span><span class="sig-name descname"><span class="pre">empty_cache</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/xpu/memory.html#empty_cache"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/xpu/memory.py#L13"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.xpu.empty_cache" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.xpu.</span></span><span class="sig-name descname"><span class="pre">empty_cache</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/xpu/memory.html#empty_cache"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/xpu/memory.py#L13"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.xpu.empty_cache" title="Permalink to this definition">¶</a></dt>
 <dd><p>Release all unoccupied cached memory currently held by the caching
 allocator so that those can be used in other XPU application.</p>
 <div class="admonition note">
diff --git a/main/generated/torch.xpu.get_arch_list.html b/main/generated/torch.xpu.get_arch_list.html
index b1e2c904812..d52acf2e000 100644
--- a/main/generated/torch.xpu.get_arch_list.html
+++ b/main/generated/torch.xpu.get_arch_list.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.xpu.get_arch_list<a class="headerlink" href="#torch-xpu-get-arch-list" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.xpu.get_arch_list">
-<span class="sig-prename descclassname"><span class="pre">torch.xpu.</span></span><span class="sig-name descname"><span class="pre">get_arch_list</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/xpu.html#get_arch_list"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/xpu/__init__.py#L421"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.xpu.get_arch_list" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.xpu.</span></span><span class="sig-name descname"><span class="pre">get_arch_list</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/xpu.html#get_arch_list"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/xpu/__init__.py#L421"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.xpu.get_arch_list" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return list XPU architectures this library was compiled for.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
diff --git a/main/generated/torch.xpu.get_device_capability.html b/main/generated/torch.xpu.get_device_capability.html
index 74b98161ffe..4b59eaf4ae7 100644
--- a/main/generated/torch.xpu.get_device_capability.html
+++ b/main/generated/torch.xpu.get_device_capability.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.xpu.get_device_capability<a class="headerlink" href="#torch-xpu-get-device-capability" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.xpu.get_device_capability">
-<span class="sig-prename descclassname"><span class="pre">torch.xpu.</span></span><span class="sig-name descname"><span class="pre">get_device_capability</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/xpu.html#get_device_capability"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/xpu/__init__.py#L218"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.xpu.get_device_capability" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.xpu.</span></span><span class="sig-name descname"><span class="pre">get_device_capability</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/xpu.html#get_device_capability"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/xpu/__init__.py#L218"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.xpu.get_device_capability" title="Permalink to this definition">¶</a></dt>
 <dd><p>Get the xpu capability of a device.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.xpu.get_device_name.html b/main/generated/torch.xpu.get_device_name.html
index 939de46e95a..e2a4a0f72ec 100644
--- a/main/generated/torch.xpu.get_device_name.html
+++ b/main/generated/torch.xpu.get_device_name.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.xpu.get_device_name<a class="headerlink" href="#torch-xpu-get-device-name" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.xpu.get_device_name">
-<span class="sig-prename descclassname"><span class="pre">torch.xpu.</span></span><span class="sig-name descname"><span class="pre">get_device_name</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/xpu.html#get_device_name"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/xpu/__init__.py#L203"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.xpu.get_device_name" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.xpu.</span></span><span class="sig-name descname"><span class="pre">get_device_name</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/xpu.html#get_device_name"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/xpu/__init__.py#L203"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.xpu.get_device_name" title="Permalink to this definition">¶</a></dt>
 <dd><p>Get the name of a device.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.xpu.get_device_properties.html b/main/generated/torch.xpu.get_device_properties.html
index 6debeb28a03..9dbb3bb36da 100644
--- a/main/generated/torch.xpu.get_device_properties.html
+++ b/main/generated/torch.xpu.get_device_properties.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.xpu.get_device_properties<a class="headerlink" href="#torch-xpu-get-device-properties" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.xpu.get_device_properties">
-<span class="sig-prename descclassname"><span class="pre">torch.xpu.</span></span><span class="sig-name descname"><span class="pre">get_device_properties</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/xpu.html#get_device_properties"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/xpu/__init__.py#L245"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.xpu.get_device_properties" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.xpu.</span></span><span class="sig-name descname"><span class="pre">get_device_properties</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/xpu.html#get_device_properties"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/xpu/__init__.py#L245"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.xpu.get_device_properties" title="Permalink to this definition">¶</a></dt>
 <dd><p>Get the properties of a device.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.xpu.get_gencode_flags.html b/main/generated/torch.xpu.get_gencode_flags.html
index 5dd170a4361..8e73e50a9a0 100644
--- a/main/generated/torch.xpu.get_gencode_flags.html
+++ b/main/generated/torch.xpu.get_gencode_flags.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.xpu.get_gencode_flags<a class="headerlink" href="#torch-xpu-get-gencode-flags" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.xpu.get_gencode_flags">
-<span class="sig-prename descclassname"><span class="pre">torch.xpu.</span></span><span class="sig-name descname"><span class="pre">get_gencode_flags</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/xpu.html#get_gencode_flags"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/xpu/__init__.py#L431"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.xpu.get_gencode_flags" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.xpu.</span></span><span class="sig-name descname"><span class="pre">get_gencode_flags</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/xpu.html#get_gencode_flags"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/xpu/__init__.py#L431"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.xpu.get_gencode_flags" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return XPU AOT(ahead-of-time) build flags this library was compiled with.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
diff --git a/main/generated/torch.xpu.get_rng_state.html b/main/generated/torch.xpu.get_rng_state.html
index 340653a5b1a..2eff627f094 100644
--- a/main/generated/torch.xpu.get_rng_state.html
+++ b/main/generated/torch.xpu.get_rng_state.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.xpu.get_rng_state<a class="headerlink" href="#torch-xpu-get-rng-state" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.xpu.get_rng_state">
-<span class="sig-prename descclassname"><span class="pre">torch.xpu.</span></span><span class="sig-name descname"><span class="pre">get_rng_state</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'xpu'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/xpu/random.html#get_rng_state"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/xpu/random.py#L11"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.xpu.get_rng_state" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.xpu.</span></span><span class="sig-name descname"><span class="pre">get_rng_state</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'xpu'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/xpu/random.html#get_rng_state"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/xpu/random.py#L11"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.xpu.get_rng_state" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the random number generator state of the specified GPU as a ByteTensor.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.xpu.get_rng_state_all.html b/main/generated/torch.xpu.get_rng_state_all.html
index 6a344bbe9c3..43f0447c06a 100644
--- a/main/generated/torch.xpu.get_rng_state_all.html
+++ b/main/generated/torch.xpu.get_rng_state_all.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.xpu.get_rng_state_all<a class="headerlink" href="#torch-xpu-get-rng-state-all" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.xpu.get_rng_state_all">
-<span class="sig-prename descclassname"><span class="pre">torch.xpu.</span></span><span class="sig-name descname"><span class="pre">get_rng_state_all</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/xpu/random.html#get_rng_state_all"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/xpu/random.py#L33"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.xpu.get_rng_state_all" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.xpu.</span></span><span class="sig-name descname"><span class="pre">get_rng_state_all</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/xpu/random.html#get_rng_state_all"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/xpu/random.py#L33"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.xpu.get_rng_state_all" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return a list of ByteTensor representing the random number states of all devices.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
diff --git a/main/generated/torch.xpu.get_stream_from_external.html b/main/generated/torch.xpu.get_stream_from_external.html
index 26e296b9250..893017c17f2 100644
--- a/main/generated/torch.xpu.get_stream_from_external.html
+++ b/main/generated/torch.xpu.get_stream_from_external.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.xpu.get_stream_from_external<a class="headerlink" href="#torch-xpu-get-stream-from-external" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.xpu.get_stream_from_external">
-<span class="sig-prename descclassname"><span class="pre">torch.xpu.</span></span><span class="sig-name descname"><span class="pre">get_stream_from_external</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">data_ptr</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/xpu.html#get_stream_from_external"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/xpu/__init__.py#L381"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.xpu.get_stream_from_external" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.xpu.</span></span><span class="sig-name descname"><span class="pre">get_stream_from_external</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">data_ptr</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/xpu.html#get_stream_from_external"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/xpu/__init__.py#L381"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.xpu.get_stream_from_external" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return a <a class="reference internal" href="torch.xpu.Stream.html#torch.xpu.Stream" title="torch.xpu.Stream"><code class="xref py py-class docutils literal notranslate"><span class="pre">Stream</span></code></a> from an external SYCL queue.</p>
 <p>This function is used to wrap SYCL queue created in other libraries in order
 to facilitate data exchange and multi-library interactions.</p>
diff --git a/main/generated/torch.xpu.init.html b/main/generated/torch.xpu.init.html
index 99e6b725a7b..e95ea1fa36a 100644
--- a/main/generated/torch.xpu.init.html
+++ b/main/generated/torch.xpu.init.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.xpu.init<a class="headerlink" href="#torch-xpu-init" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.xpu.init">
-<span class="sig-prename descclassname"><span class="pre">torch.xpu.</span></span><span class="sig-name descname"><span class="pre">init</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/xpu.html#init"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/xpu/__init__.py#L93"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.xpu.init" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.xpu.</span></span><span class="sig-name descname"><span class="pre">init</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/xpu.html#init"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/xpu/__init__.py#L93"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.xpu.init" title="Permalink to this definition">¶</a></dt>
 <dd><p>Initialize PyTorch’s XPU state.
 This is a Python API about lazy initialization that avoids initializing
 XPU until the first time it is accessed. Does nothing if the XPU state is
diff --git a/main/generated/torch.xpu.initial_seed.html b/main/generated/torch.xpu.initial_seed.html
index 4bed9634e63..0df34795d92 100644
--- a/main/generated/torch.xpu.initial_seed.html
+++ b/main/generated/torch.xpu.initial_seed.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.xpu.initial_seed<a class="headerlink" href="#torch-xpu-initial-seed" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.xpu.initial_seed">
-<span class="sig-prename descclassname"><span class="pre">torch.xpu.</span></span><span class="sig-name descname"><span class="pre">initial_seed</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/xpu/random.html#initial_seed"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/xpu/random.py#L155"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.xpu.initial_seed" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.xpu.</span></span><span class="sig-name descname"><span class="pre">initial_seed</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/xpu/random.html#initial_seed"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/xpu/random.py#L155"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.xpu.initial_seed" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the current random seed of the current GPU.</p>
 <div class="admonition warning">
 <p class="admonition-title">Warning</p>
diff --git a/main/generated/torch.xpu.is_available.html b/main/generated/torch.xpu.is_available.html
index ff58d392e43..a939aa30744 100644
--- a/main/generated/torch.xpu.is_available.html
+++ b/main/generated/torch.xpu.is_available.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
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@@ -612,7 +612,7 @@
 <h1>torch.xpu.is_available<a class="headerlink" href="#torch-xpu-is-available" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.xpu.is_available">
-<span class="sig-prename descclassname"><span class="pre">torch.xpu.</span></span><span class="sig-name descname"><span class="pre">is_available</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/xpu.html#is_available"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/xpu/__init__.py#L63"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.xpu.is_available" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.xpu.</span></span><span class="sig-name descname"><span class="pre">is_available</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/xpu.html#is_available"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/xpu/__init__.py#L63"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.xpu.is_available" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return a bool indicating if XPU is currently available.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
diff --git a/main/generated/torch.xpu.is_initialized.html b/main/generated/torch.xpu.is_initialized.html
index 16faa67c645..88159f2fec1 100644
--- a/main/generated/torch.xpu.is_initialized.html
+++ b/main/generated/torch.xpu.is_initialized.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.xpu.is_initialized<a class="headerlink" href="#torch-xpu-is-initialized" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.xpu.is_initialized">
-<span class="sig-prename descclassname"><span class="pre">torch.xpu.</span></span><span class="sig-name descname"><span class="pre">is_initialized</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/xpu.html#is_initialized"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/xpu/__init__.py#L74"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.xpu.is_initialized" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.xpu.</span></span><span class="sig-name descname"><span class="pre">is_initialized</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/xpu.html#is_initialized"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/xpu/__init__.py#L74"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.xpu.is_initialized" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return whether PyTorch’s XPU state has been initialized.</p>
 </dd></dl>
 
diff --git a/main/generated/torch.xpu.manual_seed.html b/main/generated/torch.xpu.manual_seed.html
index d35084e7a1e..f57dcbcde8b 100644
--- a/main/generated/torch.xpu.manual_seed.html
+++ b/main/generated/torch.xpu.manual_seed.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.xpu.manual_seed<a class="headerlink" href="#torch-xpu-manual-seed" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.xpu.manual_seed">
-<span class="sig-prename descclassname"><span class="pre">torch.xpu.</span></span><span class="sig-name descname"><span class="pre">manual_seed</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">seed</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/xpu/random.html#manual_seed"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/xpu/random.py#L76"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.xpu.manual_seed" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.xpu.</span></span><span class="sig-name descname"><span class="pre">manual_seed</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">seed</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/xpu/random.html#manual_seed"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/xpu/random.py#L76"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.xpu.manual_seed" title="Permalink to this definition">¶</a></dt>
 <dd><p>Set the seed for generating random numbers for the current GPU.</p>
 <p>It’s safe to call this function if XPU is not available; in that case, it is silently ignored.</p>
 <dl class="field-list simple">
diff --git a/main/generated/torch.xpu.manual_seed_all.html b/main/generated/torch.xpu.manual_seed_all.html
index 10a6931c361..4bedff9c7eb 100644
--- a/main/generated/torch.xpu.manual_seed_all.html
+++ b/main/generated/torch.xpu.manual_seed_all.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.xpu.manual_seed_all<a class="headerlink" href="#torch-xpu-manual-seed-all" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.xpu.manual_seed_all">
-<span class="sig-prename descclassname"><span class="pre">torch.xpu.</span></span><span class="sig-name descname"><span class="pre">manual_seed_all</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">seed</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/xpu/random.html#manual_seed_all"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/xpu/random.py#L98"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.xpu.manual_seed_all" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.xpu.</span></span><span class="sig-name descname"><span class="pre">manual_seed_all</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">seed</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/xpu/random.html#manual_seed_all"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/xpu/random.py#L98"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.xpu.manual_seed_all" title="Permalink to this definition">¶</a></dt>
 <dd><p>Set the seed for generating random numbers on all GPUs.</p>
 <p>It’s safe to call this function if XPU is not available; in that case, it is silently ignored.</p>
 <dl class="field-list simple">
diff --git a/main/generated/torch.xpu.max_memory_allocated.html b/main/generated/torch.xpu.max_memory_allocated.html
index 41e47e1d7d9..250e234ac9b 100644
--- a/main/generated/torch.xpu.max_memory_allocated.html
+++ b/main/generated/torch.xpu.max_memory_allocated.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.xpu.max_memory_allocated<a class="headerlink" href="#torch-xpu-max-memory-allocated" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.xpu.max_memory_allocated">
-<span class="sig-prename descclassname"><span class="pre">torch.xpu.</span></span><span class="sig-name descname"><span class="pre">max_memory_allocated</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/xpu/memory.html#max_memory_allocated"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/xpu/memory.py#L136"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.xpu.max_memory_allocated" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.xpu.</span></span><span class="sig-name descname"><span class="pre">max_memory_allocated</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/xpu/memory.html#max_memory_allocated"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/xpu/memory.py#L136"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.xpu.max_memory_allocated" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the maximum GPU memory occupied by tensors in bytes for a given device.</p>
 <p>By default, this returns the peak allocated memory since the beginning of
 this program. <a class="reference internal" href="torch.xpu.reset_peak_memory_stats.html#torch.xpu.reset_peak_memory_stats" title="torch.xpu.reset_peak_memory_stats"><code class="xref py py-func docutils literal notranslate"><span class="pre">reset_peak_memory_stats()</span></code></a> can be used to
diff --git a/main/generated/torch.xpu.max_memory_reserved.html b/main/generated/torch.xpu.max_memory_reserved.html
index 6ffb86deec5..b78a5729c79 100644
--- a/main/generated/torch.xpu.max_memory_reserved.html
+++ b/main/generated/torch.xpu.max_memory_reserved.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.xpu.max_memory_reserved<a class="headerlink" href="#torch-xpu-max-memory-reserved" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.xpu.max_memory_reserved">
-<span class="sig-prename descclassname"><span class="pre">torch.xpu.</span></span><span class="sig-name descname"><span class="pre">max_memory_reserved</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/xpu/memory.html#max_memory_reserved"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/xpu/memory.py#L164"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.xpu.max_memory_reserved" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.xpu.</span></span><span class="sig-name descname"><span class="pre">max_memory_reserved</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/xpu/memory.html#max_memory_reserved"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/xpu/memory.py#L164"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.xpu.max_memory_reserved" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the maximum GPU memory managed by the caching allocator in bytes for a given device.</p>
 <p>By default, this returns the peak cached memory since the beginning of this
 program. <a class="reference internal" href="torch.xpu.reset_peak_memory_stats.html#torch.xpu.reset_peak_memory_stats" title="torch.xpu.reset_peak_memory_stats"><code class="xref py py-func docutils literal notranslate"><span class="pre">reset_peak_memory_stats()</span></code></a> can be used to reset
diff --git a/main/generated/torch.xpu.mem_get_info.html b/main/generated/torch.xpu.mem_get_info.html
index 263e6c7733c..96fc1301386 100644
--- a/main/generated/torch.xpu.mem_get_info.html
+++ b/main/generated/torch.xpu.mem_get_info.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.xpu.mem_get_info<a class="headerlink" href="#torch-xpu-mem-get-info" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.xpu.mem_get_info">
-<span class="sig-prename descclassname"><span class="pre">torch.xpu.</span></span><span class="sig-name descname"><span class="pre">mem_get_info</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/xpu/memory.html#mem_get_info"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/xpu/memory.py#L181"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.xpu.mem_get_info" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.xpu.</span></span><span class="sig-name descname"><span class="pre">mem_get_info</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/xpu/memory.html#mem_get_info"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/xpu/memory.py#L181"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.xpu.mem_get_info" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the global free and total GPU memory for a given device.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.xpu.memory_allocated.html b/main/generated/torch.xpu.memory_allocated.html
index f76c067e1b1..6d54224212c 100644
--- a/main/generated/torch.xpu.memory_allocated.html
+++ b/main/generated/torch.xpu.memory_allocated.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.xpu.memory_allocated<a class="headerlink" href="#torch-xpu-memory-allocated" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.xpu.memory_allocated">
-<span class="sig-prename descclassname"><span class="pre">torch.xpu.</span></span><span class="sig-name descname"><span class="pre">memory_allocated</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/xpu/memory.html#memory_allocated"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/xpu/memory.py#L120"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.xpu.memory_allocated" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.xpu.</span></span><span class="sig-name descname"><span class="pre">memory_allocated</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/xpu/memory.html#memory_allocated"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/xpu/memory.py#L120"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.xpu.memory_allocated" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the current GPU memory occupied by tensors in bytes for a given device.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.xpu.memory_reserved.html b/main/generated/torch.xpu.memory_reserved.html
index 798430bf156..93439be4dc0 100644
--- a/main/generated/torch.xpu.memory_reserved.html
+++ b/main/generated/torch.xpu.memory_reserved.html
@@ -300,7 +300,7 @@
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     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
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@@ -612,7 +612,7 @@
 <h1>torch.xpu.memory_reserved<a class="headerlink" href="#torch-xpu-memory-reserved" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.xpu.memory_reserved">
-<span class="sig-prename descclassname"><span class="pre">torch.xpu.</span></span><span class="sig-name descname"><span class="pre">memory_reserved</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/xpu/memory.html#memory_reserved"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/xpu/memory.py#L153"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.xpu.memory_reserved" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.xpu.</span></span><span class="sig-name descname"><span class="pre">memory_reserved</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/xpu/memory.html#memory_reserved"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/xpu/memory.py#L153"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.xpu.memory_reserved" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the current GPU memory managed by the caching allocator in bytes for a given device.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.xpu.memory_stats.html b/main/generated/torch.xpu.memory_stats.html
index a5cd35808d6..c691f8b00d4 100644
--- a/main/generated/torch.xpu.memory_stats.html
+++ b/main/generated/torch.xpu.memory_stats.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
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     <div id="searchBox">
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@@ -612,7 +612,7 @@
 <h1>torch.xpu.memory_stats<a class="headerlink" href="#torch-xpu-memory-stats" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.xpu.memory_stats">
-<span class="sig-prename descclassname"><span class="pre">torch.xpu.</span></span><span class="sig-name descname"><span class="pre">memory_stats</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/xpu/memory.html#memory_stats"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/xpu/memory.py#L64"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.xpu.memory_stats" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.xpu.</span></span><span class="sig-name descname"><span class="pre">memory_stats</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/xpu/memory.html#memory_stats"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/xpu/memory.py#L64"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.xpu.memory_stats" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return a dictionary of XPU memory allocator statistics for a given device.</p>
 <p>The return value of this function is a dictionary of statistics, each of
 which is a non-negative integer.</p>
diff --git a/main/generated/torch.xpu.memory_stats_as_nested_dict.html b/main/generated/torch.xpu.memory_stats_as_nested_dict.html
index 786bdde282f..a040e535a9b 100644
--- a/main/generated/torch.xpu.memory_stats_as_nested_dict.html
+++ b/main/generated/torch.xpu.memory_stats_as_nested_dict.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.xpu.memory_stats_as_nested_dict<a class="headerlink" href="#torch-xpu-memory-stats-as-nested-dict" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.xpu.memory_stats_as_nested_dict">
-<span class="sig-prename descclassname"><span class="pre">torch.xpu.</span></span><span class="sig-name descname"><span class="pre">memory_stats_as_nested_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/xpu/memory.html#memory_stats_as_nested_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/xpu/memory.py#L56"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.xpu.memory_stats_as_nested_dict" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.xpu.</span></span><span class="sig-name descname"><span class="pre">memory_stats_as_nested_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/xpu/memory.html#memory_stats_as_nested_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/xpu/memory.py#L56"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.xpu.memory_stats_as_nested_dict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the result of <a class="reference internal" href="torch.xpu.memory_stats.html#torch.xpu.memory_stats" title="torch.xpu.memory_stats"><code class="xref py py-func docutils literal notranslate"><span class="pre">memory_stats()</span></code></a> as a nested dictionary.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
diff --git a/main/generated/torch.xpu.reset_accumulated_memory_stats.html b/main/generated/torch.xpu.reset_accumulated_memory_stats.html
index ef6a2f9a918..7016170113e 100644
--- a/main/generated/torch.xpu.reset_accumulated_memory_stats.html
+++ b/main/generated/torch.xpu.reset_accumulated_memory_stats.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.xpu.reset_accumulated_memory_stats<a class="headerlink" href="#torch-xpu-reset-accumulated-memory-stats" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.xpu.reset_accumulated_memory_stats">
-<span class="sig-prename descclassname"><span class="pre">torch.xpu.</span></span><span class="sig-name descname"><span class="pre">reset_accumulated_memory_stats</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/xpu/memory.html#reset_accumulated_memory_stats"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/xpu/memory.py#L41"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.xpu.reset_accumulated_memory_stats" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.xpu.</span></span><span class="sig-name descname"><span class="pre">reset_accumulated_memory_stats</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/xpu/memory.html#reset_accumulated_memory_stats"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/xpu/memory.py#L41"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.xpu.reset_accumulated_memory_stats" title="Permalink to this definition">¶</a></dt>
 <dd><p>Reset the “accumulated” (historical) stats tracked by the XPU memory allocator.</p>
 <p>See <a class="reference internal" href="torch.xpu.memory_stats.html#torch.xpu.memory_stats" title="torch.xpu.memory_stats"><code class="xref py py-func docutils literal notranslate"><span class="pre">memory_stats()</span></code></a> for details. Accumulated stats correspond to
 the <cite>“allocated”</cite> and <cite>“freed”</cite> keys in each individual stat dict.</p>
diff --git a/main/generated/torch.xpu.reset_peak_memory_stats.html b/main/generated/torch.xpu.reset_peak_memory_stats.html
index 2efa8df1273..d93654ecc4a 100644
--- a/main/generated/torch.xpu.reset_peak_memory_stats.html
+++ b/main/generated/torch.xpu.reset_peak_memory_stats.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
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@@ -612,7 +612,7 @@
 <h1>torch.xpu.reset_peak_memory_stats<a class="headerlink" href="#torch-xpu-reset-peak-memory-stats" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.xpu.reset_peak_memory_stats">
-<span class="sig-prename descclassname"><span class="pre">torch.xpu.</span></span><span class="sig-name descname"><span class="pre">reset_peak_memory_stats</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/xpu/memory.html#reset_peak_memory_stats"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/xpu/memory.py#L26"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.xpu.reset_peak_memory_stats" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.xpu.</span></span><span class="sig-name descname"><span class="pre">reset_peak_memory_stats</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/xpu/memory.html#reset_peak_memory_stats"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/xpu/memory.py#L26"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.xpu.reset_peak_memory_stats" title="Permalink to this definition">¶</a></dt>
 <dd><p>Reset the “peak” stats tracked by the XPU memory allocator.</p>
 <p>See <a class="reference internal" href="torch.xpu.memory_stats.html#torch.xpu.memory_stats" title="torch.xpu.memory_stats"><code class="xref py py-func docutils literal notranslate"><span class="pre">memory_stats()</span></code></a> for details. Peak stats correspond to the
 <cite>“peak”</cite> key in each individual stat dict.</p>
diff --git a/main/generated/torch.xpu.seed.html b/main/generated/torch.xpu.seed.html
index 8f2dded5f92..0bf56ca7ba9 100644
--- a/main/generated/torch.xpu.seed.html
+++ b/main/generated/torch.xpu.seed.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.xpu.seed<a class="headerlink" href="#torch-xpu-seed" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.xpu.seed">
-<span class="sig-prename descclassname"><span class="pre">torch.xpu.</span></span><span class="sig-name descname"><span class="pre">seed</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/xpu/random.html#seed"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/xpu/random.py#L116"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.xpu.seed" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.xpu.</span></span><span class="sig-name descname"><span class="pre">seed</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/xpu/random.html#seed"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/xpu/random.py#L116"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.xpu.seed" title="Permalink to this definition">¶</a></dt>
 <dd><p>Set the seed for generating random numbers to a random number for the current GPU.</p>
 <p>It’s safe to call this function if XPU is not available; in that case, it is silently ignored.</p>
 <div class="admonition warning">
diff --git a/main/generated/torch.xpu.seed_all.html b/main/generated/torch.xpu.seed_all.html
index 083cff8bb56..2e42be0ba33 100644
--- a/main/generated/torch.xpu.seed_all.html
+++ b/main/generated/torch.xpu.seed_all.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
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@@ -612,7 +612,7 @@
 <h1>torch.xpu.seed_all<a class="headerlink" href="#torch-xpu-seed-all" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.xpu.seed_all">
-<span class="sig-prename descclassname"><span class="pre">torch.xpu.</span></span><span class="sig-name descname"><span class="pre">seed_all</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/xpu/random.html#seed_all"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/xpu/random.py#L134"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.xpu.seed_all" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.xpu.</span></span><span class="sig-name descname"><span class="pre">seed_all</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/xpu/random.html#seed_all"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/xpu/random.py#L134"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.xpu.seed_all" title="Permalink to this definition">¶</a></dt>
 <dd><p>Set the seed for generating random numbers to a random number on all GPUs.</p>
 <p>It’s safe to call this function if XPU is not available; in that case, it is silently ignored.</p>
 <dl class="field-list simple">
diff --git a/main/generated/torch.xpu.set_device.html b/main/generated/torch.xpu.set_device.html
index aaa7cb89606..b8d2df1278b 100644
--- a/main/generated/torch.xpu.set_device.html
+++ b/main/generated/torch.xpu.set_device.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.xpu.set_device<a class="headerlink" href="#torch-xpu-set-device" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.xpu.set_device">
-<span class="sig-prename descclassname"><span class="pre">torch.xpu.</span></span><span class="sig-name descname"><span class="pre">set_device</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/xpu.html#set_device"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/xpu/__init__.py#L190"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.xpu.set_device" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.xpu.</span></span><span class="sig-name descname"><span class="pre">set_device</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/xpu.html#set_device"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/xpu/__init__.py#L190"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.xpu.set_device" title="Permalink to this definition">¶</a></dt>
 <dd><p>Set the current device.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.xpu.set_rng_state.html b/main/generated/torch.xpu.set_rng_state.html
index 7268ee408a7..1060c8f3910 100644
--- a/main/generated/torch.xpu.set_rng_state.html
+++ b/main/generated/torch.xpu.set_rng_state.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.xpu.set_rng_state<a class="headerlink" href="#torch-xpu-set-rng-state" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.xpu.set_rng_state">
-<span class="sig-prename descclassname"><span class="pre">torch.xpu.</span></span><span class="sig-name descname"><span class="pre">set_rng_state</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">new_state</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'xpu'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/xpu/random.html#set_rng_state"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/xpu/random.py#L39"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.xpu.set_rng_state" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.xpu.</span></span><span class="sig-name descname"><span class="pre">set_rng_state</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">new_state</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'xpu'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/xpu/random.html#set_rng_state"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/xpu/random.py#L39"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.xpu.set_rng_state" title="Permalink to this definition">¶</a></dt>
 <dd><p>Set the random number generator state of the specified GPU.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.xpu.set_rng_state_all.html b/main/generated/torch.xpu.set_rng_state_all.html
index 37cfbc4f12d..a3658c5dcac 100644
--- a/main/generated/torch.xpu.set_rng_state_all.html
+++ b/main/generated/torch.xpu.set_rng_state_all.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.xpu.set_rng_state_all<a class="headerlink" href="#torch-xpu-set-rng-state-all" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.xpu.set_rng_state_all">
-<span class="sig-prename descclassname"><span class="pre">torch.xpu.</span></span><span class="sig-name descname"><span class="pre">set_rng_state_all</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">new_states</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/xpu/random.html#set_rng_state_all"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/xpu/random.py#L66"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.xpu.set_rng_state_all" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.xpu.</span></span><span class="sig-name descname"><span class="pre">set_rng_state_all</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">new_states</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/xpu/random.html#set_rng_state_all"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/xpu/random.py#L66"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.xpu.set_rng_state_all" title="Permalink to this definition">¶</a></dt>
 <dd><p>Set the random number generator state of all devices.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.xpu.set_stream.html b/main/generated/torch.xpu.set_stream.html
index fdeef226f0f..7acb56fa2bc 100644
--- a/main/generated/torch.xpu.set_stream.html
+++ b/main/generated/torch.xpu.set_stream.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.xpu.set_stream<a class="headerlink" href="#torch-xpu-set-stream" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.xpu.set_stream">
-<span class="sig-prename descclassname"><span class="pre">torch.xpu.</span></span><span class="sig-name descname"><span class="pre">set_stream</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">stream</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/xpu.html#set_stream"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/xpu/__init__.py#L344"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.xpu.set_stream" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.xpu.</span></span><span class="sig-name descname"><span class="pre">set_stream</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">stream</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/xpu.html#set_stream"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/xpu/__init__.py#L344"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.xpu.set_stream" title="Permalink to this definition">¶</a></dt>
 <dd><dl class="simple">
 <dt>Set the current stream.This is a wrapper API to set the stream.</dt><dd><p>Usage of this function is discouraged in favor of the <code class="docutils literal notranslate"><span class="pre">stream</span></code>
 context manager.</p>
diff --git a/main/generated/torch.xpu.stream.html b/main/generated/torch.xpu.stream.html
index a356e368c2d..48ec3cf6b0a 100644
--- a/main/generated/torch.xpu.stream.html
+++ b/main/generated/torch.xpu.stream.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.xpu.stream<a class="headerlink" href="#torch-xpu-stream" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.xpu.stream">
-<span class="sig-prename descclassname"><span class="pre">torch.xpu.</span></span><span class="sig-name descname"><span class="pre">stream</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">stream</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/xpu.html#stream"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/xpu/__init__.py#L321"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.xpu.stream" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.xpu.</span></span><span class="sig-name descname"><span class="pre">stream</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">stream</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/xpu.html#stream"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/xpu/__init__.py#L321"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.xpu.stream" title="Permalink to this definition">¶</a></dt>
 <dd><p>Wrap around the Context-manager StreamContext that selects a given stream.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.xpu.synchronize.html b/main/generated/torch.xpu.synchronize.html
index adb0c0302cd..03b55163cb7 100644
--- a/main/generated/torch.xpu.synchronize.html
+++ b/main/generated/torch.xpu.synchronize.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <h1>torch.xpu.synchronize<a class="headerlink" href="#torch-xpu-synchronize" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.xpu.synchronize">
-<span class="sig-prename descclassname"><span class="pre">torch.xpu.</span></span><span class="sig-name descname"><span class="pre">synchronize</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/xpu.html#synchronize"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/xpu/__init__.py#L408"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.xpu.synchronize" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.xpu.</span></span><span class="sig-name descname"><span class="pre">synchronize</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/xpu.html#synchronize"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/xpu/__init__.py#L408"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.xpu.synchronize" title="Permalink to this definition">¶</a></dt>
 <dd><p>Wait for all kernels in all streams on a XPU device to complete.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/generated/torch.zeros.html b/main/generated/torch.zeros.html
index 6c5c0a565b5..c699cdff2d1 100644
--- a/main/generated/torch.zeros.html
+++ b/main/generated/torch.zeros.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/generated/torch.zeros_like.html b/main/generated/torch.zeros_like.html
index 7fe264b26cc..32fede156af 100644
--- a/main/generated/torch.zeros_like.html
+++ b/main/generated/torch.zeros_like.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/genindex.html b/main/genindex.html
index 4d42b1381ef..1caf9ede58f 100644
--- a/main/genindex.html
+++ b/main/genindex.html
@@ -298,7 +298,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/hub.html b/main/hub.html
index 6947e7ea9d3..21b9760826e 100644
--- a/main/hub.html
+++ b/main/hub.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -683,7 +683,7 @@ <h2>Loading models from Hub<a class="headerlink" href="#loading-models-from-hub"
 <a class="reference internal" href="#torch.hub.load" title="torch.hub.load"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.hub.load()</span></code></a>.</p>
 <span class="target" id="module-torch.hub"></span><dl class="py function">
 <dt class="sig sig-object py" id="torch.hub.list">
-<span class="sig-prename descclassname"><span class="pre">torch.hub.</span></span><span class="sig-name descname"><span class="pre">list</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">github</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">force_reload</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">skip_validation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">trust_repo</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/hub.html#list"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/hub.py#L422"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.hub.list" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.hub.</span></span><span class="sig-name descname"><span class="pre">list</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">github</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">force_reload</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">skip_validation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">trust_repo</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/hub.html#list"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/hub.py#L422"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.hub.list" title="Permalink to this definition">¶</a></dt>
 <dd><p>List all callable entrypoints available in the repo specified by <code class="docutils literal notranslate"><span class="pre">github</span></code>.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -736,7 +736,7 @@ <h2>Loading models from Hub<a class="headerlink" href="#loading-models-from-hub"
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.hub.help">
-<span class="sig-prename descclassname"><span class="pre">torch.hub.</span></span><span class="sig-name descname"><span class="pre">help</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">github</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">force_reload</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">skip_validation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">trust_repo</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/hub.html#help"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/hub.py#L494"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.hub.help" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.hub.</span></span><span class="sig-name descname"><span class="pre">help</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">github</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">force_reload</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">skip_validation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">trust_repo</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/hub.html#help"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/hub.py#L494"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.hub.help" title="Permalink to this definition">¶</a></dt>
 <dd><p>Show the docstring of entrypoint <code class="docutils literal notranslate"><span class="pre">model</span></code>.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -781,7 +781,7 @@ <h2>Loading models from Hub<a class="headerlink" href="#loading-models-from-hub"
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.hub.load">
-<span class="sig-prename descclassname"><span class="pre">torch.hub.</span></span><span class="sig-name descname"><span class="pre">load</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">repo_or_dir</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">model</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">source</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'github'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">trust_repo</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">force_reload</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">skip_validation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/hub.html#load"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/hub.py#L549"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.hub.load" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.hub.</span></span><span class="sig-name descname"><span class="pre">load</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">repo_or_dir</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">model</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">source</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'github'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">trust_repo</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">force_reload</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">skip_validation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/hub.html#load"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/hub.py#L549"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.hub.load" title="Permalink to this definition">¶</a></dt>
 <dd><p>Load a model from a github repo or a local directory.</p>
 <p>Note: Loading a model is the typical use case, but this can also be used to
 for loading other objects such as tokenizers, loss functions, etc.</p>
@@ -855,7 +855,7 @@ <h2>Loading models from Hub<a class="headerlink" href="#loading-models-from-hub"
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.hub.download_url_to_file">
-<span class="sig-prename descclassname"><span class="pre">torch.hub.</span></span><span class="sig-name descname"><span class="pre">download_url_to_file</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">url</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dst</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">hash_prefix</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">progress</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/hub.html#download_url_to_file"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/hub.py#L681"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.hub.download_url_to_file" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.hub.</span></span><span class="sig-name descname"><span class="pre">download_url_to_file</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">url</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dst</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">hash_prefix</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">progress</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/hub.html#download_url_to_file"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/hub.py#L681"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.hub.download_url_to_file" title="Permalink to this definition">¶</a></dt>
 <dd><p>Download object at the given URL to a local path.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -880,7 +880,7 @@ <h2>Loading models from Hub<a class="headerlink" href="#loading-models-from-hub"
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.hub.load_state_dict_from_url">
-<span class="sig-prename descclassname"><span class="pre">torch.hub.</span></span><span class="sig-name descname"><span class="pre">load_state_dict_from_url</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">url</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">model_dir</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">map_location</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">progress</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">check_hash</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">file_name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weights_only</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/hub.html#load_state_dict_from_url"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/hub.py#L803"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.hub.load_state_dict_from_url" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.hub.</span></span><span class="sig-name descname"><span class="pre">load_state_dict_from_url</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">url</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">model_dir</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">map_location</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">progress</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">check_hash</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">file_name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weights_only</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/hub.html#load_state_dict_from_url"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/hub.py#L803"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.hub.load_state_dict_from_url" title="Permalink to this definition">¶</a></dt>
 <dd><p>Loads the Torch serialized object at the given URL.</p>
 <p>If downloaded file is a zip file, it will be automatically
 decompressed.</p>
@@ -943,7 +943,7 @@ <h3>Where are my downloaded models saved?<a class="headerlink" href="#where-are-
 </ul>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.hub.get_dir">
-<span class="sig-prename descclassname"><span class="pre">torch.hub.</span></span><span class="sig-name descname"><span class="pre">get_dir</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/hub.html#get_dir"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/hub.py#L392"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.hub.get_dir" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.hub.</span></span><span class="sig-name descname"><span class="pre">get_dir</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/hub.html#get_dir"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/hub.py#L392"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.hub.get_dir" title="Permalink to this definition">¶</a></dt>
 <dd><p>Get the Torch Hub cache directory used for storing downloaded models &amp; weights.</p>
 <p>If <a class="reference internal" href="#torch.hub.set_dir" title="torch.hub.set_dir"><code class="xref py py-func docutils literal notranslate"><span class="pre">set_dir()</span></code></a> is not called, default path is <code class="docutils literal notranslate"><span class="pre">$TORCH_HOME/hub</span></code> where
 environment variable <code class="docutils literal notranslate"><span class="pre">$TORCH_HOME</span></code> defaults to <code class="docutils literal notranslate"><span class="pre">$XDG_CACHE_HOME/torch</span></code>.
@@ -959,7 +959,7 @@ <h3>Where are my downloaded models saved?<a class="headerlink" href="#where-are-
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.hub.set_dir">
-<span class="sig-prename descclassname"><span class="pre">torch.hub.</span></span><span class="sig-name descname"><span class="pre">set_dir</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">d</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/hub.html#set_dir"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/hub.py#L411"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.hub.set_dir" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.hub.</span></span><span class="sig-name descname"><span class="pre">set_dir</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">d</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/hub.html#set_dir"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/hub.py#L411"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.hub.set_dir" title="Permalink to this definition">¶</a></dt>
 <dd><p>Optionally set the Torch Hub directory used to save downloaded models &amp; weights.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/index.html b/main/index.html
index 2e908f5a81b..6d0f222dcae 100644
--- a/main/index.html
+++ b/main/index.html
@@ -299,7 +299,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/jit.html b/main/jit.html
index 7ee9ec906ea..91e8e115c19 100644
--- a/main/jit.html
+++ b/main/jit.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -1271,7 +1271,7 @@ <h4>Modules<a class="headerlink" href="#modules" title="Permalink to this headin
 <code class="docutils literal notranslate"><span class="pre">&#64;torch.jit.export</span></code> methods.</p>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.jit.export">
-<span class="sig-prename descclassname"><span class="pre">torch.jit.</span></span><span class="sig-name descname"><span class="pre">export</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">fn</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/_jit_internal.html#export"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/_jit_internal.py#L669"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.export" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.jit.</span></span><span class="sig-name descname"><span class="pre">export</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">fn</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/_jit_internal.html#export"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/_jit_internal.py#L669"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.export" title="Permalink to this definition">¶</a></dt>
 <dd><p>This decorator indicates that a method on an <code class="docutils literal notranslate"><span class="pre">nn.Module</span></code> is used as an entry point into a
 <a class="reference internal" href="generated/torch.jit.ScriptModule.html#torch.jit.ScriptModule" title="torch.jit.ScriptModule"><code class="xref py py-class docutils literal notranslate"><span class="pre">ScriptModule</span></code></a> and should be compiled.</p>
 <p><code class="docutils literal notranslate"><span class="pre">forward</span></code> implicitly is assumed to be an entry point, so it does not need this decorator.
diff --git a/main/jit_builtin_functions.html b/main/jit_builtin_functions.html
index 1cccc8cca51..9031ae92ed9 100644
--- a/main/jit_builtin_functions.html
+++ b/main/jit_builtin_functions.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/jit_language_reference.html b/main/jit_language_reference.html
index 2eb4f2fee76..6253cdad13b 100644
--- a/main/jit_language_reference.html
+++ b/main/jit_language_reference.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -1363,7 +1363,7 @@ <h3>Functions<a class="headerlink" href="#functions" title="Permalink to this he
 check the correctness of the model as you go.</p>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.jit.is_scripting">
-<span class="sig-prename descclassname"><span class="pre">torch.jit.</span></span><span class="sig-name descname"><span class="pre">is_scripting</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/_jit_internal.html#is_scripting"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/_jit_internal.py#L102"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.is_scripting" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.jit.</span></span><span class="sig-name descname"><span class="pre">is_scripting</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/_jit_internal.html#is_scripting"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/_jit_internal.py#L102"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.is_scripting" title="Permalink to this definition">¶</a></dt>
 <dd><p>Function that returns True when in compilation and False otherwise. This
 is useful especially with the &#64;unused decorator to leave code in your
 model that is not yet TorchScript compatible.
@@ -1390,7 +1390,7 @@ <h3>Functions<a class="headerlink" href="#functions" title="Permalink to this he
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.jit.is_tracing">
-<span class="sig-prename descclassname"><span class="pre">torch.jit.</span></span><span class="sig-name descname"><span class="pre">is_tracing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/jit/_trace.html#is_tracing"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/jit/_trace.py#L1324"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.is_tracing" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.jit.</span></span><span class="sig-name descname"><span class="pre">is_tracing</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/jit/_trace.html#is_tracing"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/jit/_trace.py#L1324"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.jit.is_tracing" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return a boolean value.</p>
 <p>Returns <code class="docutils literal notranslate"><span class="pre">True</span></code> in tracing (if a function is called during the
 tracing of code with <code class="docutils literal notranslate"><span class="pre">torch.jit.trace</span></code>) and <code class="docutils literal notranslate"><span class="pre">False</span></code> otherwise.</p>
diff --git a/main/jit_language_reference_v2.html b/main/jit_language_reference_v2.html
index 2fc5e4da13a..a2ea3da3248 100644
--- a/main/jit_language_reference_v2.html
+++ b/main/jit_language_reference_v2.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/jit_python_reference.html b/main/jit_python_reference.html
index 9226f4a19ad..b66af8d7a73 100644
--- a/main/jit_python_reference.html
+++ b/main/jit_python_reference.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/jit_unsupported.html b/main/jit_unsupported.html
index 88165a78cf5..c94a2cd9433 100644
--- a/main/jit_unsupported.html
+++ b/main/jit_unsupported.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/jit_utils.html b/main/jit_utils.html
index 939a5bc3184..b85f3e0263a 100644
--- a/main/jit_utils.html
+++ b/main/jit_utils.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/library.html b/main/library.html
index 8f75ad27a6b..391c674f797 100644
--- a/main/library.html
+++ b/main/library.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -623,7 +623,7 @@ <h2>Testing custom ops<a class="headerlink" href="#testing-custom-ops" title="Pe
 mathematically correct.</p>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.library.opcheck">
-<span class="sig-prename descclassname"><span class="pre">torch.library.</span></span><span class="sig-name descname"><span class="pre">opcheck</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">op</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kwargs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">test_utils</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">('test_schema',</span> <span class="pre">'test_autograd_registration',</span> <span class="pre">'test_faketensor',</span> <span class="pre">'test_aot_dispatch_dynamic')</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">raise_exception</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/library.html#opcheck"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/library.py#L1445"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.library.opcheck" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.library.</span></span><span class="sig-name descname"><span class="pre">opcheck</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">op</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kwargs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">test_utils</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">('test_schema',</span> <span class="pre">'test_autograd_registration',</span> <span class="pre">'test_faketensor',</span> <span class="pre">'test_aot_dispatch_dynamic')</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">raise_exception</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/library.html#opcheck"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/library.py#L1445"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.library.opcheck" title="Permalink to this definition">¶</a></dt>
 <dd><p>Given an operator and some sample arguments, tests if the operator is
 registered correctly.</p>
 <p>That is, when you use the torch.library/TORCH_LIBRARY APIs to create a
@@ -733,7 +733,7 @@ <h2>Creating new custom ops in Python<a class="headerlink" href="#creating-new-c
 <p>Use <a class="reference internal" href="#torch.library.custom_op" title="torch.library.custom_op"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.library.custom_op()</span></code></a> to create new custom ops.</p>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.library.custom_op">
-<span class="sig-prename descclassname"><span class="pre">torch.library.</span></span><span class="sig-name descname"><span class="pre">custom_op</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fn</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">/</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mutates_args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device_types</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">schema</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/_library/custom_ops.py#L48"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.library.custom_op" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.library.</span></span><span class="sig-name descname"><span class="pre">custom_op</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fn</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">/</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mutates_args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device_types</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">schema</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/_library/custom_ops.py#L48"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.library.custom_op" title="Permalink to this definition">¶</a></dt>
 <dd><p>Wraps a function into custom operator.</p>
 <p>Reasons why you may want to create a custom op include:
 - Wrapping a third-party library or custom kernel to work with PyTorch
@@ -831,7 +831,7 @@ <h2>Creating new custom ops in Python<a class="headerlink" href="#creating-new-c
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.library.triton_op">
-<span class="sig-prename descclassname"><span class="pre">torch.library.</span></span><span class="sig-name descname"><span class="pre">triton_op</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fn</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">/</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mutates_args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">schema</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/_library/triton.py#L12"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.library.triton_op" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.library.</span></span><span class="sig-name descname"><span class="pre">triton_op</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fn</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">/</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mutates_args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">schema</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/_library/triton.py#L12"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.library.triton_op" title="Permalink to this definition">¶</a></dt>
 <dd><p>Create a custom operator whose implementation is backed by 1+ triton kernels.</p>
 <p>This is a more structured way of using triton kernels with PyTorch.
 Prefer using triton kernels with no <code class="docutils literal notranslate"><span class="pre">torch.library</span></code> custom operator wrappers
@@ -925,7 +925,7 @@ <h2>Creating new custom ops in Python<a class="headerlink" href="#creating-new-c
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.library.wrap_triton">
-<span class="sig-prename descclassname"><span class="pre">torch.library.</span></span><span class="sig-name descname"><span class="pre">wrap_triton</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">triton_kernel</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">/</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/_library/triton.py#L200"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.library.wrap_triton" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.library.</span></span><span class="sig-name descname"><span class="pre">wrap_triton</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">triton_kernel</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">/</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/_library/triton.py#L200"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.library.wrap_triton" title="Permalink to this definition">¶</a></dt>
 <dd><p>Allows capture of a triton kernel into a graph via make_fx or
 non-strict <code class="docutils literal notranslate"><span class="pre">torch.export</span></code>.</p>
 <p>These technologies perform Dispatcher-based tracing (via
@@ -998,7 +998,7 @@ <h2>Extending custom ops (created from Python or C++)<a class="headerlink" href=
 via PyTorch’s C++ operator registration APIs).</p>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.library.register_kernel">
-<span class="sig-prename descclassname"><span class="pre">torch.library.</span></span><span class="sig-name descname"><span class="pre">register_kernel</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">op</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device_types</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">func</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">/</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lib</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/library.html#register_kernel"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/library.py#L763"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.library.register_kernel" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.library.</span></span><span class="sig-name descname"><span class="pre">register_kernel</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">op</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device_types</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">func</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">/</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lib</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/library.html#register_kernel"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/library.py#L763"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.library.register_kernel" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register an implementation for a device type for this operator.</p>
 <p>Some valid device_types are: “cpu”, “cuda”, “xla”, “mps”, “ipu”, “xpu”.
 This API may be used as a decorator.</p>
@@ -1047,7 +1047,7 @@ <h2>Extending custom ops (created from Python or C++)<a class="headerlink" href=
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.library.register_autocast">
-<span class="sig-prename descclassname"><span class="pre">torch.library.</span></span><span class="sig-name descname"><span class="pre">register_autocast</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">op</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device_type</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cast_inputs</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">/</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lib</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/library.html#register_autocast"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/library.py#L831"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.library.register_autocast" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.library.</span></span><span class="sig-name descname"><span class="pre">register_autocast</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">op</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device_type</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cast_inputs</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">/</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lib</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/library.html#register_autocast"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/library.py#L831"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.library.register_autocast" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register an autocast dispatch rule for this custom op.</p>
 <p>Valid <cite>device_type</cite> include: “cpu” and “cuda”.</p>
 <dl class="field-list simple">
@@ -1089,7 +1089,7 @@ <h2>Extending custom ops (created from Python or C++)<a class="headerlink" href=
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.library.register_autograd">
-<span class="sig-prename descclassname"><span class="pre">torch.library.</span></span><span class="sig-name descname"><span class="pre">register_autograd</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">op</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">backward</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">/</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">setup_context</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lib</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/library.html#register_autograd"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/library.py#L1033"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.library.register_autograd" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.library.</span></span><span class="sig-name descname"><span class="pre">register_autograd</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">op</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">backward</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">/</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">setup_context</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lib</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/library.html#register_autograd"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/library.py#L1033"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.library.register_autograd" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register a backward formula for this custom op.</p>
 <p>In order for an operator to work with autograd, you need to register
 a backward formula:
@@ -1173,7 +1173,7 @@ <h2>Extending custom ops (created from Python or C++)<a class="headerlink" href=
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.library.register_fake">
-<span class="sig-prename descclassname"><span class="pre">torch.library.</span></span><span class="sig-name descname"><span class="pre">register_fake</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">op</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">func</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">/</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lib</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_stacklevel</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/library.html#register_fake"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/library.py#L912"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.library.register_fake" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.library.</span></span><span class="sig-name descname"><span class="pre">register_fake</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">op</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">func</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">/</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lib</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_stacklevel</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/library.html#register_fake"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/library.py#L912"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.library.register_fake" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register a FakeTensor implementation (“fake impl”) for this operator.</p>
 <p>Also sometimes known as a “meta kernel”, “abstract impl”.</p>
 <p>An “FakeTensor implementation” specifies the behavior of this operator on
@@ -1254,7 +1254,7 @@ <h2>Extending custom ops (created from Python or C++)<a class="headerlink" href=
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.library.register_vmap">
-<span class="sig-prename descclassname"><span class="pre">torch.library.</span></span><span class="sig-name descname"><span class="pre">register_vmap</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">op</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">func</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">/</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lib</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/library.html#register_vmap"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/library.py#L1244"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.library.register_vmap" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.library.</span></span><span class="sig-name descname"><span class="pre">register_vmap</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">op</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">func</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">/</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lib</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/library.html#register_vmap"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/library.py#L1244"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.library.register_vmap" title="Permalink to this definition">¶</a></dt>
 <dd><p>Register a vmap implementation to support <a class="reference internal" href="generated/torch.vmap.html#torch.vmap" title="torch.vmap"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.vmap()</span></code></a> for this custom op.</p>
 <p>This API may be used as a decorator (see examples).</p>
 <p>In order for an operator to work with <a class="reference internal" href="generated/torch.vmap.html#torch.vmap" title="torch.vmap"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.vmap()</span></code></a>, you may need to register a
@@ -1332,14 +1332,14 @@ <h2>Extending custom ops (created from Python or C++)<a class="headerlink" href=
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.library.impl_abstract">
-<span class="sig-prename descclassname"><span class="pre">torch.library.</span></span><span class="sig-name descname"><span class="pre">impl_abstract</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">qualname</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">func</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lib</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_stacklevel</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/library.html#impl_abstract"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/library.py#L744"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.library.impl_abstract" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.library.</span></span><span class="sig-name descname"><span class="pre">impl_abstract</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">qualname</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">func</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lib</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_stacklevel</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/library.html#impl_abstract"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/library.py#L744"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.library.impl_abstract" title="Permalink to this definition">¶</a></dt>
 <dd><p>This API was renamed to <a class="reference internal" href="#torch.library.register_fake" title="torch.library.register_fake"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.library.register_fake()</span></code></a> in PyTorch 2.4.
 Please use that instead.</p>
 </dd></dl>
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.library.get_ctx">
-<span class="sig-prename descclassname"><span class="pre">torch.library.</span></span><span class="sig-name descname"><span class="pre">get_ctx</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/library.html#get_ctx"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/library.py#L1428"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.library.get_ctx" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.library.</span></span><span class="sig-name descname"><span class="pre">get_ctx</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/library.html#get_ctx"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/library.py#L1428"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.library.get_ctx" title="Permalink to this definition">¶</a></dt>
 <dd><p>get_ctx() returns the current AbstractImplCtx object.</p>
 <p>Calling <code class="docutils literal notranslate"><span class="pre">get_ctx()</span></code> is only valid inside of an fake impl
 (see <a class="reference internal" href="#torch.library.register_fake" title="torch.library.register_fake"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.library.register_fake()</span></code></a> for more usage details.</p>
@@ -1352,7 +1352,7 @@ <h2>Extending custom ops (created from Python or C++)<a class="headerlink" href=
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.library.register_torch_dispatch">
-<span class="sig-prename descclassname"><span class="pre">torch.library.</span></span><span class="sig-name descname"><span class="pre">register_torch_dispatch</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">op</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">torch_dispatch_class</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">func</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">/</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lib</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/library.html#register_torch_dispatch"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/library.py#L1163"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.library.register_torch_dispatch" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.library.</span></span><span class="sig-name descname"><span class="pre">register_torch_dispatch</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">op</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">torch_dispatch_class</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">func</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">/</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lib</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/library.html#register_torch_dispatch"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/library.py#L1163"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.library.register_torch_dispatch" title="Permalink to this definition">¶</a></dt>
 <dd><p>Registers a torch_dispatch rule for the given operator and <code class="docutils literal notranslate"><span class="pre">torch_dispatch_class</span></code>.</p>
 <p>This allows for open registration to specify the behavior between the operator
 and the <code class="docutils literal notranslate"><span class="pre">torch_dispatch_class</span></code> without needing to modify the <code class="docutils literal notranslate"><span class="pre">torch_dispatch_class</span></code>
@@ -1396,7 +1396,7 @@ <h2>Extending custom ops (created from Python or C++)<a class="headerlink" href=
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.library.infer_schema">
-<span class="sig-prename descclassname"><span class="pre">torch.library.</span></span><span class="sig-name descname"><span class="pre">infer_schema</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">prototype_function</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">/</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mutates_args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">op_name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/_library/infer_schema.py#L18"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.library.infer_schema" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.library.</span></span><span class="sig-name descname"><span class="pre">infer_schema</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">prototype_function</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">/</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mutates_args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">op_name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/_library/infer_schema.py#L18"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.library.infer_schema" title="Permalink to this definition">¶</a></dt>
 <dd><p>Parses the schema of a given function with type hints. The schema is inferred from the
 function’s type hints, and can be used to define a new operator.</p>
 <p>We make the following assumptions:</p>
@@ -1447,7 +1447,7 @@ <h2>Extending custom ops (created from Python or C++)<a class="headerlink" href=
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch._library.custom_ops.CustomOpDef">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch._library.custom_ops.</span></span><span class="sig-name descname"><span class="pre">CustomOpDef</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">namespace</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">schema</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fn</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/_library/custom_ops.html#CustomOpDef"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/_library/custom_ops.py#L176"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch._library.custom_ops.CustomOpDef" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch._library.custom_ops.</span></span><span class="sig-name descname"><span class="pre">CustomOpDef</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">namespace</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">schema</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fn</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/_library/custom_ops.html#CustomOpDef"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/_library/custom_ops.py#L176"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch._library.custom_ops.CustomOpDef" title="Permalink to this definition">¶</a></dt>
 <dd><p>CustomOpDef is a wrapper around a function that turns it into a custom op.</p>
 <p>It has various methods for registering additional behavior for this
 custom op.</p>
@@ -1457,7 +1457,7 @@ <h2>Extending custom ops (created from Python or C++)<a class="headerlink" href=
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch._library.custom_ops.CustomOpDef.set_kernel_enabled">
-<span class="sig-name descname"><span class="pre">set_kernel_enabled</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device_type</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">enabled</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/_library/custom_ops.html#CustomOpDef.set_kernel_enabled"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/_library/custom_ops.py#L215"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch._library.custom_ops.CustomOpDef.set_kernel_enabled" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">set_kernel_enabled</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device_type</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">enabled</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/_library/custom_ops.html#CustomOpDef.set_kernel_enabled"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/_library/custom_ops.py#L215"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch._library.custom_ops.CustomOpDef.set_kernel_enabled" title="Permalink to this definition">¶</a></dt>
 <dd><p>Disable or re-enable an already registered kernel for this custom operator.</p>
 <p>If the kernel is already disabled/enabled, this is a no-op.</p>
 <div class="admonition note">
@@ -1513,7 +1513,7 @@ <h2>Low-level APIs<a class="headerlink" href="#low-level-apis" title="Permalink
 <p>A tutorial that walks you through some examples on how to use this API is available on <a class="reference external" href="https://colab.research.google.com/drive/1RRhSfk7So3Cn02itzLWE9K4Fam-8U011?usp=sharing">Google Colab</a>.</p>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.library.Library">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.library.</span></span><span class="sig-name descname"><span class="pre">Library</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">ns</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kind</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dispatch_key</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/library.html#Library"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/library.py#L75"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.library.Library" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.library.</span></span><span class="sig-name descname"><span class="pre">Library</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">ns</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kind</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dispatch_key</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/library.html#Library"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/library.py#L75"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.library.Library" title="Permalink to this definition">¶</a></dt>
 <dd><p>A class to create libraries that can be used to register new operators or
 override operators in existing libraries from Python.
 A user can optionally pass in a dispatch keyname if they only want to register
@@ -1534,7 +1534,7 @@ <h2>Low-level APIs<a class="headerlink" href="#low-level-apis" title="Permalink
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.library.Library.define">
-<span class="sig-name descname"><span class="pre">define</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">schema</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alias_analysis</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tags</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">()</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/library.html#Library.define"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/library.py#L135"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.library.Library.define" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">define</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">schema</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alias_analysis</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tags</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">()</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/library.html#Library.define"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/library.py#L135"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.library.Library.define" title="Permalink to this definition">¶</a></dt>
 <dd><p>Defines a new operator and its semantics in the ns namespace.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -1563,7 +1563,7 @@ <h2>Low-level APIs<a class="headerlink" href="#low-level-apis" title="Permalink
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.library.Library.fallback">
-<span class="sig-name descname"><span class="pre">fallback</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">fn</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dispatch_key</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">with_keyset</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/library.html#Library.fallback"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/library.py#L375"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.library.Library.fallback" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">fallback</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">fn</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dispatch_key</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">with_keyset</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/library.html#Library.fallback"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/library.py#L375"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.library.Library.fallback" title="Permalink to this definition">¶</a></dt>
 <dd><p>Registers the function implementation as the fallback for the given key.</p>
 <p>This function only works for a library with global namespace (“_”).</p>
 <dl class="field-list simple">
@@ -1592,7 +1592,7 @@ <h2>Low-level APIs<a class="headerlink" href="#low-level-apis" title="Permalink
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.library.Library.impl">
-<span class="sig-name descname"><span class="pre">impl</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">op_name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fn</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dispatch_key</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">with_keyset</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/library.html#Library.impl"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/library.py#L293"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.library.Library.impl" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">impl</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">op_name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fn</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dispatch_key</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">with_keyset</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/library.html#Library.impl"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/library.py#L293"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.library.Library.impl" title="Permalink to this definition">¶</a></dt>
 <dd><p>Registers the function implementation for an operator defined in the library.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -1622,13 +1622,13 @@ <h2>Low-level APIs<a class="headerlink" href="#low-level-apis" title="Permalink
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.library.fallthrough_kernel">
-<span class="sig-prename descclassname"><span class="pre">torch.library.</span></span><span class="sig-name descname"><span class="pre">fallthrough_kernel</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/library.html#fallthrough_kernel"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/library.py#L68"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.library.fallthrough_kernel" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.library.</span></span><span class="sig-name descname"><span class="pre">fallthrough_kernel</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/library.html#fallthrough_kernel"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/library.py#L68"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.library.fallthrough_kernel" title="Permalink to this definition">¶</a></dt>
 <dd><p>A dummy function to pass to <code class="docutils literal notranslate"><span class="pre">Library.impl</span></code> in order to register a fallthrough.</p>
 </dd></dl>
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.library.define">
-<span class="sig-prename descclassname"><span class="pre">torch.library.</span></span><span class="sig-name descname"><span class="pre">define</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">qualname</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">schema</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lib</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tags</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">()</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/library.html#define"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/library.py#L467"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.library.define" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.library.</span></span><span class="sig-name descname"><span class="pre">define</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">qualname</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">schema</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lib</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tags</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">()</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/library.html#define"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/library.py#L467"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.library.define" title="Permalink to this definition">¶</a></dt>
 <dt class="sig sig-object py">
 <span class="sig-prename descclassname"><span class="pre">torch.library.</span></span><span class="sig-name descname"><span class="pre">define</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">lib</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">schema</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alias_analysis</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em><span class="sig-paren">)</span></dt>
 <dd><p>Defines a new operator.</p>
@@ -1685,7 +1685,7 @@ <h2>Low-level APIs<a class="headerlink" href="#low-level-apis" title="Permalink
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.library.impl">
-<span class="sig-prename descclassname"><span class="pre">torch.library.</span></span><span class="sig-name descname"><span class="pre">impl</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">lib</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dispatch_key</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/library.html#impl"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/library.py#L577"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.library.impl" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.library.</span></span><span class="sig-name descname"><span class="pre">impl</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">lib</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dispatch_key</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/library.html#impl"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/library.py#L577"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.library.impl" title="Permalink to this definition">¶</a></dt>
 <dt class="sig sig-object py">
 <span class="sig-prename descclassname"><span class="pre">torch.library.</span></span><span class="sig-name descname"><span class="pre">impl</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">qualname</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">types</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Sequence</span><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.13)"><span class="pre">str</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">func</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Literal</span><span class="p"><span class="pre">[</span></span><span class="pre">None</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lib</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="#torch.library.Library" title="torch.library.Library"><span class="pre">Library</span></a><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">Callable</span><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><span class="pre">Callable</span><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#object" title="(in Python v3.13)"><span class="pre">object</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.13)"><span class="pre">None</span></a><span class="p"><span class="pre">]</span></span></span></span></dt>
 <dt class="sig sig-object py">
diff --git a/main/linalg.html b/main/linalg.html
index 9048b22a3f7..c0fd63bc8f1 100644
--- a/main/linalg.html
+++ b/main/linalg.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/logging.html b/main/logging.html
index b772d8484c2..086c365fb64 100644
--- a/main/logging.html
+++ b/main/logging.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/masked.html b/main/masked.html
index ed84d02604a..6ae620f8169 100644
--- a/main/masked.html
+++ b/main/masked.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/meta.html b/main/meta.html
index 1f083d66a1c..75b743d197b 100644
--- a/main/meta.html
+++ b/main/meta.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/miscellaneous_environment_variables.html b/main/miscellaneous_environment_variables.html
index 4a27734d5b0..070555e2bff 100644
--- a/main/miscellaneous_environment_variables.html
+++ b/main/miscellaneous_environment_variables.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/mobile_optimizer.html b/main/mobile_optimizer.html
index fd067a773c5..67ed9d3e4f1 100644
--- a/main/mobile_optimizer.html
+++ b/main/mobile_optimizer.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -636,7 +636,7 @@ <h1>torch.utils.mobile_optimizer<a class="headerlink" href="#torch-utils-mobile-
 <p><code class="docutils literal notranslate"><span class="pre">optimize_for_mobile</span></code> will also invoke freeze_module pass which only preserves <code class="docutils literal notranslate"><span class="pre">forward</span></code> method. If you have other method to that needed to be preserved, add them into the preserved method list and pass into the method.</p>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.utils.mobile_optimizer.optimize_for_mobile">
-<span class="sig-prename descclassname"><span class="pre">torch.utils.mobile_optimizer.</span></span><span class="sig-name descname"><span class="pre">optimize_for_mobile</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">script_module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">optimization_blocklist</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">preserved_methods</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">backend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'CPU'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/mobile_optimizer.html#optimize_for_mobile"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/utils/mobile_optimizer.py#L15"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.mobile_optimizer.optimize_for_mobile" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.utils.mobile_optimizer.</span></span><span class="sig-name descname"><span class="pre">optimize_for_mobile</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">script_module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">optimization_blocklist</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">preserved_methods</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">backend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'CPU'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/mobile_optimizer.html#optimize_for_mobile"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/utils/mobile_optimizer.py#L15"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.mobile_optimizer.optimize_for_mobile" title="Permalink to this definition">¶</a></dt>
 <dd><p>Optimize a torch script module for mobile deployment.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/model_zoo.html b/main/model_zoo.html
index cfb5f3e76a9..38d0cbc8e03 100644
--- a/main/model_zoo.html
+++ b/main/model_zoo.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -611,7 +611,7 @@ <h1>torch.utils.model_zoo<a class="headerlink" href="#torch-utils-model-zoo" tit
 <p>Moved to <cite>torch.hub</cite>.</p>
 <span class="target" id="module-torch.utils.model_zoo"></span><dl class="py function">
 <dt class="sig sig-object py" id="torch.utils.model_zoo.load_url">
-<span class="sig-prename descclassname"><span class="pre">torch.utils.model_zoo.</span></span><span class="sig-name descname"><span class="pre">load_url</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">url</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">model_dir</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">map_location</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">progress</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">check_hash</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">file_name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weights_only</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/hub.py#L803"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.model_zoo.load_url" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.utils.model_zoo.</span></span><span class="sig-name descname"><span class="pre">load_url</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">url</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">model_dir</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">map_location</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">progress</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">check_hash</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">file_name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weights_only</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/hub.py#L803"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.model_zoo.load_url" title="Permalink to this definition">¶</a></dt>
 <dd><p>Loads the Torch serialized object at the given URL.</p>
 <p>If downloaded file is a zip file, it will be automatically
 decompressed.</p>
diff --git a/main/module_tracker.html b/main/module_tracker.html
index d3c405bf86f..f46d77fe860 100644
--- a/main/module_tracker.html
+++ b/main/module_tracker.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 It can be used within other tracking tools to be able to easily associate measured quantities to user-friendly names. This is used in particular in the FlopCounterMode today.</p>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.utils.module_tracker.ModuleTracker">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.utils.module_tracker.</span></span><span class="sig-name descname"><span class="pre">ModuleTracker</span></span><a class="reference internal" href="_modules/torch/utils/module_tracker.html#ModuleTracker"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/utils/module_tracker.py#L25"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.module_tracker.ModuleTracker" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.utils.module_tracker.</span></span><span class="sig-name descname"><span class="pre">ModuleTracker</span></span><a class="reference internal" href="_modules/torch/utils/module_tracker.html#ModuleTracker"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/utils/module_tracker.py#L25"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.module_tracker.ModuleTracker" title="Permalink to this definition">¶</a></dt>
 <dd><p><code class="docutils literal notranslate"><span class="pre">ModuleTracker</span></code> is a context manager that tracks the nn.Module hierarchy during execution
 so that other system can query which Module is currently being executed (or its backward is being
 executed).</p>
diff --git a/main/monitor.html b/main/monitor.html
index 7e6b03e9ddf..35dc1bc2a51 100644
--- a/main/monitor.html
+++ b/main/monitor.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -785,7 +785,7 @@ <h1>torch.monitor<a class="headerlink" href="#torch-monitor" title="Permalink to
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.monitor.TensorboardEventHandler">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.monitor.</span></span><span class="sig-name descname"><span class="pre">TensorboardEventHandler</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">writer</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/monitor.html#TensorboardEventHandler"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/monitor/__init__.py#L14"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.monitor.TensorboardEventHandler" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.monitor.</span></span><span class="sig-name descname"><span class="pre">TensorboardEventHandler</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">writer</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/monitor.html#TensorboardEventHandler"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/monitor/__init__.py#L14"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.monitor.TensorboardEventHandler" title="Permalink to this definition">¶</a></dt>
 <dd><p>TensorboardEventHandler is an event handler that will write known events to
 the provided SummaryWriter.</p>
 <p>This currently only supports <code class="docutils literal notranslate"><span class="pre">torch.monitor.Stat</span></code> events which are logged
@@ -801,7 +801,7 @@ <h1>torch.monitor<a class="headerlink" href="#torch-monitor" title="Permalink to
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.monitor.TensorboardEventHandler.__init__">
-<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">writer</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/monitor.html#TensorboardEventHandler.__init__"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/monitor/__init__.py#L31"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.monitor.TensorboardEventHandler.__init__" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">writer</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/monitor.html#TensorboardEventHandler.__init__"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/monitor/__init__.py#L31"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.monitor.TensorboardEventHandler.__init__" title="Permalink to this definition">¶</a></dt>
 <dd><p>Constructs the <code class="docutils literal notranslate"><span class="pre">TensorboardEventHandler</span></code>.</p>
 <dl class="field-list simple">
 </dl>
diff --git a/main/mps.html b/main/mps.html
index f4ab07221ba..4ab264f8801 100644
--- a/main/mps.html
+++ b/main/mps.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/mps_environment_variables.html b/main/mps_environment_variables.html
index 1e554d07c80..136a2659b68 100644
--- a/main/mps_environment_variables.html
+++ b/main/mps_environment_variables.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/mtia.html b/main/mtia.html
index 711a71d3c60..d869007f4f6 100644
--- a/main/mtia.html
+++ b/main/mtia.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/mtia.memory.html b/main/mtia.memory.html
index 3453d99df72..77f2837980f 100644
--- a/main/mtia.memory.html
+++ b/main/mtia.memory.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/multiprocessing.html b/main/multiprocessing.html
index 492bbd1e627..9cceed8d257 100644
--- a/main/multiprocessing.html
+++ b/main/multiprocessing.html
@@ -298,7 +298,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -629,19 +629,19 @@
 <h2>Strategy management<a class="headerlink" href="#strategy-management" title="Permalink to this heading">¶</a></h2>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.multiprocessing.get_all_sharing_strategies">
-<span class="sig-prename descclassname"><span class="pre">torch.multiprocessing.</span></span><span class="sig-name descname"><span class="pre">get_all_sharing_strategies</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/multiprocessing.html#get_all_sharing_strategies"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/multiprocessing/__init__.py#L77"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.multiprocessing.get_all_sharing_strategies" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.multiprocessing.</span></span><span class="sig-name descname"><span class="pre">get_all_sharing_strategies</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/multiprocessing.html#get_all_sharing_strategies"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/multiprocessing/__init__.py#L77"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.multiprocessing.get_all_sharing_strategies" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return a set of sharing strategies supported on a current system.</p>
 </dd></dl>
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.multiprocessing.get_sharing_strategy">
-<span class="sig-prename descclassname"><span class="pre">torch.multiprocessing.</span></span><span class="sig-name descname"><span class="pre">get_sharing_strategy</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/multiprocessing.html#get_sharing_strategy"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/multiprocessing/__init__.py#L72"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.multiprocessing.get_sharing_strategy" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.multiprocessing.</span></span><span class="sig-name descname"><span class="pre">get_sharing_strategy</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/multiprocessing.html#get_sharing_strategy"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/multiprocessing/__init__.py#L72"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.multiprocessing.get_sharing_strategy" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the current strategy for sharing CPU tensors.</p>
 </dd></dl>
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.multiprocessing.set_sharing_strategy">
-<span class="sig-prename descclassname"><span class="pre">torch.multiprocessing.</span></span><span class="sig-name descname"><span class="pre">set_sharing_strategy</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">new_strategy</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/multiprocessing.html#set_sharing_strategy"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/multiprocessing/__init__.py#L60"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.multiprocessing.set_sharing_strategy" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.multiprocessing.</span></span><span class="sig-name descname"><span class="pre">set_sharing_strategy</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">new_strategy</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/multiprocessing.html#set_sharing_strategy"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/multiprocessing/__init__.py#L60"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.multiprocessing.set_sharing_strategy" title="Permalink to this definition">¶</a></dt>
 <dd><p>Set the strategy for sharing CPU tensors.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -778,7 +778,7 @@ <h2>Spawning subprocesses<a class="headerlink" href="#spawning-subprocesses" tit
 terminate processes upon detecting an error in one of them.</p>
 <span class="target" id="module-torch.multiprocessing.spawn"></span><dl class="py function">
 <dt class="sig sig-object py" id="torch.multiprocessing.spawn.spawn">
-<span class="sig-prename descclassname"><span class="pre">torch.multiprocessing.spawn.</span></span><span class="sig-name descname"><span class="pre">spawn</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">fn</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">()</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">nprocs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">join</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">daemon</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">start_method</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'spawn'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/multiprocessing/spawn.html#spawn"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/multiprocessing/spawn.py#L300"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.multiprocessing.spawn.spawn" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.multiprocessing.spawn.</span></span><span class="sig-name descname"><span class="pre">spawn</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">fn</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">()</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">nprocs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">join</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">daemon</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">start_method</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'spawn'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/multiprocessing/spawn.html#spawn"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/multiprocessing/spawn.py#L300"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.multiprocessing.spawn.spawn" title="Permalink to this definition">¶</a></dt>
 <dd><p>Spawns <code class="docutils literal notranslate"><span class="pre">nprocs</span></code> processes that run <code class="docutils literal notranslate"><span class="pre">fn</span></code> with <code class="docutils literal notranslate"><span class="pre">args</span></code>.</p>
 <p>If one of the processes exits with a non-zero exit status, the
 remaining processes are killed and an exception is raised with the
@@ -815,11 +815,11 @@ <h2>Spawning subprocesses<a class="headerlink" href="#spawning-subprocesses" tit
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.multiprocessing.SpawnContext">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.multiprocessing.</span></span><span class="sig-name descname"><span class="pre">SpawnContext</span></span><a class="reference internal" href="_modules/torch/multiprocessing/spawn.html#SpawnContext"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/multiprocessing/spawn.py#L218"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.multiprocessing.SpawnContext" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.multiprocessing.</span></span><span class="sig-name descname"><span class="pre">SpawnContext</span></span><a class="reference internal" href="_modules/torch/multiprocessing/spawn.html#SpawnContext"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/multiprocessing/spawn.py#L218"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.multiprocessing.SpawnContext" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returned by <a class="reference internal" href="#module-torch.multiprocessing.spawn" title="torch.multiprocessing.spawn"><code class="xref py py-func docutils literal notranslate"><span class="pre">spawn()</span></code></a> when called with <code class="docutils literal notranslate"><span class="pre">join=False</span></code>.</p>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.multiprocessing.SpawnContext.join">
-<span class="sig-name descname"><span class="pre">join</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">timeout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">grace_period</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/multiprocessing/spawn.py#L120"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.multiprocessing.SpawnContext.join" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">join</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">timeout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">grace_period</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/multiprocessing/spawn.py#L120"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.multiprocessing.SpawnContext.join" title="Permalink to this definition">¶</a></dt>
 <dd><p>Join one or more processes within spawn context.</p>
 <p>Attempt to join one or more processes in this spawn context.
 If one of them exited with a non-zero exit status, this function
diff --git a/main/name_inference.html b/main/name_inference.html
index 1aa2d07d227..d02a34ff61f 100644
--- a/main/name_inference.html
+++ b/main/name_inference.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/named_tensor.html b/main/named_tensor.html
index 169f08cde73..a7c35e34f67 100644
--- a/main/named_tensor.html
+++ b/main/named_tensor.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -875,7 +875,7 @@ <h2>Named tensor API reference<a class="headerlink" href="#named-tensor-api-refe
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.Tensor.rename">
-<span class="sig-name descname"><span class="pre">rename</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">names</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">rename_map</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/_tensor.html#Tensor.rename"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/_tensor.py#L1440"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.Tensor.rename" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">rename</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">names</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">rename_map</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/_tensor.html#Tensor.rename"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/_tensor.py#L1440"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.Tensor.rename" title="Permalink to this definition">¶</a></dt>
 <dd><p>Renames dimension names of <code class="xref py py-attr docutils literal notranslate"><span class="pre">self</span></code>.</p>
 <p>There are two main usages:</p>
 <p><code class="docutils literal notranslate"><span class="pre">self.rename(**rename_map)</span></code> returns a view on tensor that has dims
@@ -908,13 +908,13 @@ <h2>Named tensor API reference<a class="headerlink" href="#named-tensor-api-refe
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.Tensor.rename_">
-<span class="sig-name descname"><span class="pre">rename_</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">names</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">rename_map</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/_tensor.html#Tensor.rename_"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/_tensor.py#L1425"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.Tensor.rename_" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">rename_</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">names</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">rename_map</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/_tensor.html#Tensor.rename_"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/_tensor.py#L1425"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.Tensor.rename_" title="Permalink to this definition">¶</a></dt>
 <dd><p>In-place version of <a class="reference internal" href="#torch.Tensor.rename" title="torch.Tensor.rename"><code class="xref py py-meth docutils literal notranslate"><span class="pre">rename()</span></code></a>.</p>
 </dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.Tensor.refine_names">
-<span class="sig-name descname"><span class="pre">refine_names</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">names</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/_tensor.html#Tensor.refine_names"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/_tensor.py#L1314"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.Tensor.refine_names" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">refine_names</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">names</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/_tensor.html#Tensor.refine_names"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/_tensor.py#L1314"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.Tensor.refine_names" title="Permalink to this definition">¶</a></dt>
 <dd><p>Refines the dimension names of <code class="xref py py-attr docutils literal notranslate"><span class="pre">self</span></code> according to <a class="reference internal" href="#torch.Tensor.names" title="torch.Tensor.names"><code class="xref py py-attr docutils literal notranslate"><span class="pre">names</span></code></a>.</p>
 <p>Refining is a special case of renaming that “lifts” unnamed dimensions.
 A <code class="docutils literal notranslate"><span class="pre">None</span></code> dim can be refined to have any name; a named dim can only be
@@ -996,7 +996,7 @@ <h2>Named tensor API reference<a class="headerlink" href="#named-tensor-api-refe
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.Tensor.align_to">
-<span class="sig-name descname"><span class="pre">align_to</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">names</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/_tensor.html#Tensor.align_to"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/_tensor.py#L1358"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.Tensor.align_to" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">align_to</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">names</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/_tensor.html#Tensor.align_to"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/_tensor.py#L1358"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.Tensor.align_to" title="Permalink to this definition">¶</a></dt>
 <dd><p>Permutes the dimensions of the <code class="xref py py-attr docutils literal notranslate"><span class="pre">self</span></code> tensor to match the order
 specified in <a class="reference internal" href="#torch.Tensor.names" title="torch.Tensor.names"><code class="xref py py-attr docutils literal notranslate"><span class="pre">names</span></code></a>, adding size-one dims for any new names.</p>
 <p>All of the dims of <code class="xref py py-attr docutils literal notranslate"><span class="pre">self</span></code> must be named in order to use this method.
diff --git a/main/nested.html b/main/nested.html
index 99e3aa4b934..dacbaa5e3e7 100644
--- a/main/nested.html
+++ b/main/nested.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -1045,7 +1045,7 @@ <h3>Data dependent operation within torch.compile<a class="headerlink" href="#da
 <span id="construction-and-conversion"></span><h2>Detailed Docs for Construction and Conversion Functions<a class="headerlink" href="#detailed-docs-for-construction-and-conversion-functions" title="Permalink to this heading">¶</a></h2>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nested.nested_tensor">
-<span class="sig-prename descclassname"><span class="pre">torch.nested.</span></span><span class="sig-name descname"><span class="pre">nested_tensor</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tensor_list</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">layout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">requires_grad</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pin_memory</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/nested.html#nested_tensor"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nested/__init__.py#L210"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nested.nested_tensor" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nested.</span></span><span class="sig-name descname"><span class="pre">nested_tensor</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tensor_list</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">layout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">requires_grad</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pin_memory</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/nested.html#nested_tensor"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nested/__init__.py#L210"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nested.nested_tensor" title="Permalink to this definition">¶</a></dt>
 <dd><p>Constructs a nested tensor with no autograd history (also known as a “leaf tensor”, see
 <a class="reference internal" href="notes/autograd.html#autograd-mechanics"><span class="std std-ref">Autograd mechanics</span></a>) from <code class="xref py py-attr docutils literal notranslate"><span class="pre">tensor_list</span></code> a list of tensors.</p>
 <dl class="field-list simple">
@@ -1085,7 +1085,7 @@ <h3>Data dependent operation within torch.compile<a class="headerlink" href="#da
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nested.nested_tensor_from_jagged">
-<span class="sig-prename descclassname"><span class="pre">torch.nested.</span></span><span class="sig-name descname"><span class="pre">nested_tensor_from_jagged</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">values</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">offsets</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lengths</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">jagged_dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_seqlen</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_seqlen</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/nested.html#nested_tensor_from_jagged"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nested/__init__.py#L359"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nested.nested_tensor_from_jagged" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nested.</span></span><span class="sig-name descname"><span class="pre">nested_tensor_from_jagged</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">values</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">offsets</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lengths</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">jagged_dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_seqlen</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_seqlen</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/nested.html#nested_tensor_from_jagged"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nested/__init__.py#L359"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nested.nested_tensor_from_jagged" title="Permalink to this definition">¶</a></dt>
 <dd><p>Constructs a jagged layout nested tensor from the given jagged components. The jagged layout
 consists of a required values buffer with the jagged dimension packed into a single dimension.
 The offsets / lengths metadata determines how this dimension is split into batch elements
@@ -1161,7 +1161,7 @@ <h3>Data dependent operation within torch.compile<a class="headerlink" href="#da
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nested.as_nested_tensor">
-<span class="sig-prename descclassname"><span class="pre">torch.nested.</span></span><span class="sig-name descname"><span class="pre">as_nested_tensor</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">ts</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">layout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/nested.html#as_nested_tensor"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nested/__init__.py#L27"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nested.as_nested_tensor" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nested.</span></span><span class="sig-name descname"><span class="pre">as_nested_tensor</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">ts</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">layout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/nested.html#as_nested_tensor"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nested/__init__.py#L27"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nested.as_nested_tensor" title="Permalink to this definition">¶</a></dt>
 <dd><p>Constructs a nested tensor preserving autograd history from a tensor or a list / tuple of
 tensors.</p>
 <p>If a nested tensor is passed, it will be returned directly unless the device / dtype / layout
@@ -1266,7 +1266,7 @@ <h3>Data dependent operation within torch.compile<a class="headerlink" href="#da
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nested.masked_select">
-<span class="sig-prename descclassname"><span class="pre">torch.nested.</span></span><span class="sig-name descname"><span class="pre">masked_select</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mask</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/nested.html#masked_select"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nested/__init__.py#L468"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nested.masked_select" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nested.</span></span><span class="sig-name descname"><span class="pre">masked_select</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mask</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/nested.html#masked_select"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nested/__init__.py#L468"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nested.masked_select" title="Permalink to this definition">¶</a></dt>
 <dd><p>Constructs a nested tensor given a strided tensor input and a strided mask, the resulting jagged layout nested tensor
 will have values retain values where the mask is equal to True. The dimensionality of the mask is preserved and is
 represented with the offsets, this is unlike <a class="reference internal" href="#torch.nested.masked_select" title="torch.nested.masked_select"><code class="xref py py-func docutils literal notranslate"><span class="pre">masked_select()</span></code></a> where the output is collapsed to a 1D tensor.</p>
@@ -1302,7 +1302,7 @@ <h3>Data dependent operation within torch.compile<a class="headerlink" href="#da
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nested.narrow">
-<span class="sig-prename descclassname"><span class="pre">torch.nested.</span></span><span class="sig-name descname"><span class="pre">narrow</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dim</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">start</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">length</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">layout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.strided</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/nested.html#narrow"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nested/__init__.py#L281"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nested.narrow" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nested.</span></span><span class="sig-name descname"><span class="pre">narrow</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dim</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">start</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">length</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">layout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.strided</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/nested.html#narrow"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nested/__init__.py#L281"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nested.narrow" title="Permalink to this definition">¶</a></dt>
 <dd><p>Constructs a nested tensor (which might be a view) from <code class="xref py py-attr docutils literal notranslate"><span class="pre">tensor</span></code>, a strided tensor. This follows
 similar semantics to torch.Tensor.narrow, where in the <code class="xref py py-attr docutils literal notranslate"><span class="pre">dim</span></code>-th dimension the new nested tensor
 shows only the elements in the interval <cite>[start, start+length)</cite>. As nested representations
diff --git a/main/nn.attention.bias.html b/main/nn.attention.bias.html
index ce9db5cd597..c2a5358dd9d 100644
--- a/main/nn.attention.bias.html
+++ b/main/nn.attention.bias.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/nn.attention.experimental.html b/main/nn.attention.experimental.html
index 4249f10d517..e4ba975e36d 100644
--- a/main/nn.attention.experimental.html
+++ b/main/nn.attention.experimental.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/nn.attention.flex_attention.html b/main/nn.attention.flex_attention.html
index bac7b319480..745a2e7cfb6 100644
--- a/main/nn.attention.flex_attention.html
+++ b/main/nn.attention.flex_attention.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -612,7 +612,7 @@
 <span id="torch-nn-attention-flex-attention"></span><h1>torch.nn.attention.flex_attention<a class="headerlink" href="#module-torch.nn.attention.flex_attention" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.attention.flex_attention.flex_attention">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.attention.flex_attention.</span></span><span class="sig-name descname"><span class="pre">flex_attention</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">query</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">key</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">value</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">score_mod</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">block_mask</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">enable_gqa</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_lse</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_options</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/nn/attention/flex_attention.html#flex_attention"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/attention/flex_attention.py#L1161"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.attention.flex_attention.flex_attention" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.attention.flex_attention.</span></span><span class="sig-name descname"><span class="pre">flex_attention</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">query</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">key</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">value</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">score_mod</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">block_mask</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scale</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">enable_gqa</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_lse</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kernel_options</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/nn/attention/flex_attention.html#flex_attention"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/attention/flex_attention.py#L1161"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.attention.flex_attention.flex_attention" title="Permalink to this definition">¶</a></dt>
 <dd><p>This function implements scaled dot product attention with an arbitrary attention score modification function.</p>
 <p>This function computes the scaled dot product attention between query, key, and value tensors with a user-defined
 attention score modification function. The attention score modification function will be applied after the attention
@@ -691,7 +691,7 @@
 <h2>BlockMask Utilities<a class="headerlink" href="#blockmask-utilities" title="Permalink to this heading">¶</a></h2>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.attention.flex_attention.create_block_mask">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.attention.flex_attention.</span></span><span class="sig-name descname"><span class="pre">create_block_mask</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mask_mod</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">B</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">H</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Q_LEN</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">KV_LEN</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'cuda'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">BLOCK_SIZE</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">128</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_compile</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/nn/attention/flex_attention.html#create_block_mask"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/attention/flex_attention.py#L824"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.attention.flex_attention.create_block_mask" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.attention.flex_attention.</span></span><span class="sig-name descname"><span class="pre">create_block_mask</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mask_mod</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">B</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">H</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Q_LEN</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">KV_LEN</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'cuda'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">BLOCK_SIZE</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">128</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_compile</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/nn/attention/flex_attention.html#create_block_mask"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/attention/flex_attention.py#L824"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.attention.flex_attention.create_block_mask" title="Permalink to this definition">¶</a></dt>
 <dd><p>This function creates a block mask tuple from a mask_mod function.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -733,7 +733,7 @@ <h2>BlockMask Utilities<a class="headerlink" href="#blockmask-utilities" title="
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.attention.flex_attention.create_mask">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.attention.flex_attention.</span></span><span class="sig-name descname"><span class="pre">create_mask</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mod_fn</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">B</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">H</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Q_LEN</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">KV_LEN</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'cuda'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/nn/attention/flex_attention.html#create_mask"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/attention/flex_attention.py#L777"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.attention.flex_attention.create_mask" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.attention.flex_attention.</span></span><span class="sig-name descname"><span class="pre">create_mask</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mod_fn</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">B</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">H</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Q_LEN</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">KV_LEN</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'cuda'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/nn/attention/flex_attention.html#create_mask"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/attention/flex_attention.py#L777"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.attention.flex_attention.create_mask" title="Permalink to this definition">¶</a></dt>
 <dd><p>This function creates a mask tensor from a mod_fn function.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -757,7 +757,7 @@ <h2>BlockMask Utilities<a class="headerlink" href="#blockmask-utilities" title="
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.attention.flex_attention.create_nested_block_mask">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.attention.flex_attention.</span></span><span class="sig-name descname"><span class="pre">create_nested_block_mask</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mask_mod</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">B</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">H</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">q_nt</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kv_nt</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">BLOCK_SIZE</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">128</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_compile</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/nn/attention/flex_attention.html#create_nested_block_mask"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/attention/flex_attention.py#L998"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.attention.flex_attention.create_nested_block_mask" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.attention.flex_attention.</span></span><span class="sig-name descname"><span class="pre">create_nested_block_mask</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mask_mod</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">B</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">H</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">q_nt</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kv_nt</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">BLOCK_SIZE</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">128</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_compile</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/nn/attention/flex_attention.html#create_nested_block_mask"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/attention/flex_attention.py#L998"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.attention.flex_attention.create_nested_block_mask" title="Permalink to this definition">¶</a></dt>
 <dd><p>This function creates a nested tensor compatible block mask tuple from a mask_mod
 function. The returned BlockMask will be on the device specified by the input nested tensor.</p>
 <dl class="field-list simple">
@@ -821,7 +821,7 @@ <h2>BlockMask Utilities<a class="headerlink" href="#blockmask-utilities" title="
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.attention.flex_attention.and_masks">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.attention.flex_attention.</span></span><span class="sig-name descname"><span class="pre">and_masks</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">mask_mods</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/nn/attention/flex_attention.html#and_masks"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/attention/flex_attention.py#L721"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.attention.flex_attention.and_masks" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.attention.flex_attention.</span></span><span class="sig-name descname"><span class="pre">and_masks</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">mask_mods</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/nn/attention/flex_attention.html#and_masks"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/attention/flex_attention.py#L721"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.attention.flex_attention.and_masks" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns a mask_mod that’s the intersection of provided mask_mods</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -832,7 +832,7 @@ <h2>BlockMask Utilities<a class="headerlink" href="#blockmask-utilities" title="
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.attention.flex_attention.or_masks">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.attention.flex_attention.</span></span><span class="sig-name descname"><span class="pre">or_masks</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">mask_mods</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/nn/attention/flex_attention.html#or_masks"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/attention/flex_attention.py#L707"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.attention.flex_attention.or_masks" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.attention.flex_attention.</span></span><span class="sig-name descname"><span class="pre">or_masks</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">mask_mods</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/nn/attention/flex_attention.html#or_masks"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/attention/flex_attention.py#L707"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.attention.flex_attention.or_masks" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns a mask_mod that’s the union of provided mask_mods</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -843,7 +843,7 @@ <h2>BlockMask Utilities<a class="headerlink" href="#blockmask-utilities" title="
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.attention.flex_attention.noop_mask">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.attention.flex_attention.</span></span><span class="sig-name descname"><span class="pre">noop_mask</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">batch</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">head</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">token_q</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">token_kv</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/nn/attention/flex_attention.html#noop_mask"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/attention/flex_attention.py#L131"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.attention.flex_attention.noop_mask" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.attention.flex_attention.</span></span><span class="sig-name descname"><span class="pre">noop_mask</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">batch</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">head</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">token_q</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">token_kv</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/nn/attention/flex_attention.html#noop_mask"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/attention/flex_attention.py#L131"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.attention.flex_attention.noop_mask" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns a noop mask_mod</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -857,7 +857,7 @@ <h2>BlockMask Utilities<a class="headerlink" href="#blockmask-utilities" title="
 <h2>BlockMask<a class="headerlink" href="#blockmask" title="Permalink to this heading">¶</a></h2>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.nn.attention.flex_attention.BlockMask">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.attention.flex_attention.</span></span><span class="sig-name descname"><span class="pre">BlockMask</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">seq_lengths</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kv_num_blocks</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kv_indices</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">full_kv_num_blocks</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">full_kv_indices</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">q_num_blocks</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">q_indices</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">full_q_num_blocks</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">full_q_indices</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">BLOCK_SIZE</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mask_mod</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/nn/attention/flex_attention.html#BlockMask"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/attention/flex_attention.py#L203"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.attention.flex_attention.BlockMask" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.nn.attention.flex_attention.</span></span><span class="sig-name descname"><span class="pre">BlockMask</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">seq_lengths</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kv_num_blocks</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kv_indices</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">full_kv_num_blocks</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">full_kv_indices</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">q_num_blocks</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">q_indices</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">full_q_num_blocks</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">full_q_indices</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">BLOCK_SIZE</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mask_mod</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/nn/attention/flex_attention.html#BlockMask"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/attention/flex_attention.py#L203"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.attention.flex_attention.BlockMask" title="Permalink to this definition">¶</a></dt>
 <dd><p>BlockMask is our format for representing a block-sparse attention mask.
 It is somewhat of a cross in-between BCSR and a non-sparse format.</p>
 <div class="section" id="basics">
@@ -910,7 +910,7 @@ <h3>Details<a class="headerlink" href="#details" title="Permalink to this headin
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.attention.flex_attention.BlockMask.as_tuple">
-<span class="sig-name descname"><span class="pre">as_tuple</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">flatten</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/nn/attention/flex_attention.html#BlockMask.as_tuple"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/attention/flex_attention.py#L384"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.attention.flex_attention.BlockMask.as_tuple" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">as_tuple</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">flatten</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/nn/attention/flex_attention.html#BlockMask.as_tuple"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/attention/flex_attention.py#L384"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.attention.flex_attention.BlockMask.as_tuple" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns a tuple of the attributes of the BlockMask.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -921,7 +921,7 @@ <h3>Details<a class="headerlink" href="#details" title="Permalink to this headin
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.attention.flex_attention.BlockMask.from_kv_blocks">
-<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_kv_blocks</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">kv_num_blocks</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kv_indices</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">full_kv_num_blocks</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">full_kv_indices</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">BLOCK_SIZE</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">128</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mask_mod</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">seq_lengths</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/nn/attention/flex_attention.html#BlockMask.from_kv_blocks"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/attention/flex_attention.py#L315"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.attention.flex_attention.BlockMask.from_kv_blocks" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_kv_blocks</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">kv_num_blocks</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kv_indices</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">full_kv_num_blocks</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">full_kv_indices</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">BLOCK_SIZE</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">128</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mask_mod</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">seq_lengths</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/nn/attention/flex_attention.html#BlockMask.from_kv_blocks"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/attention/flex_attention.py#L315"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.attention.flex_attention.BlockMask.from_kv_blocks" title="Permalink to this definition">¶</a></dt>
 <dd><p>Creates a BlockMask instance from key-value block information.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -986,7 +986,7 @@ <h3>Details<a class="headerlink" href="#details" title="Permalink to this headin
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.attention.flex_attention.BlockMask.numel">
-<span class="sig-name descname"><span class="pre">numel</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/nn/attention/flex_attention.html#BlockMask.numel"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/attention/flex_attention.py#L530"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.attention.flex_attention.BlockMask.numel" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">numel</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/nn/attention/flex_attention.html#BlockMask.numel"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/attention/flex_attention.py#L530"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.attention.flex_attention.BlockMask.numel" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns the number of elements (not accounting for sparsity) in the mask.</p>
 </dd></dl>
 
@@ -1012,7 +1012,7 @@ <h3>Details<a class="headerlink" href="#details" title="Permalink to this headin
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.attention.flex_attention.BlockMask.sparsity">
-<span class="sig-name descname"><span class="pre">sparsity</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/nn/attention/flex_attention.html#BlockMask.sparsity"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/attention/flex_attention.py#L539"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.attention.flex_attention.BlockMask.sparsity" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">sparsity</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/nn/attention/flex_attention.html#BlockMask.sparsity"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/attention/flex_attention.py#L539"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.attention.flex_attention.BlockMask.sparsity" title="Permalink to this definition">¶</a></dt>
 <dd><p>Computes the percentage of blocks that are sparse (i.e. not computed)</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -1023,7 +1023,7 @@ <h3>Details<a class="headerlink" href="#details" title="Permalink to this headin
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.attention.flex_attention.BlockMask.to">
-<span class="sig-name descname"><span class="pre">to</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/nn/attention/flex_attention.html#BlockMask.to"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/attention/flex_attention.py#L626"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.attention.flex_attention.BlockMask.to" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">to</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/nn/attention/flex_attention.html#BlockMask.to"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/attention/flex_attention.py#L626"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.attention.flex_attention.BlockMask.to" title="Permalink to this definition">¶</a></dt>
 <dd><p>Moves the BlockMask to the specified device.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -1049,7 +1049,7 @@ <h3>Details<a class="headerlink" href="#details" title="Permalink to this headin
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.attention.flex_attention.BlockMask.to_dense">
-<span class="sig-name descname"><span class="pre">to_dense</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/nn/attention/flex_attention.html#BlockMask.to_dense"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/attention/flex_attention.py#L550"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.attention.flex_attention.BlockMask.to_dense" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">to_dense</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/nn/attention/flex_attention.html#BlockMask.to_dense"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/attention/flex_attention.py#L550"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.attention.flex_attention.BlockMask.to_dense" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns a dense block that is equivalent to the block mask.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -1060,7 +1060,7 @@ <h3>Details<a class="headerlink" href="#details" title="Permalink to this headin
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.nn.attention.flex_attention.BlockMask.to_string">
-<span class="sig-name descname"><span class="pre">to_string</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">grid_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">(20,</span> <span class="pre">20)</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">limit</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">4</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/nn/attention/flex_attention.html#BlockMask.to_string"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/attention/flex_attention.py#L560"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.attention.flex_attention.BlockMask.to_string" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">to_string</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">grid_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">(20,</span> <span class="pre">20)</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">limit</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">4</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/nn/attention/flex_attention.html#BlockMask.to_string"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/attention/flex_attention.py#L560"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.attention.flex_attention.BlockMask.to_string" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns a string representation of the block mask. Quite nifty.</p>
 <p>If grid_size is None, prints out an uncompressed version. Warning, it can be quite big!</p>
 </dd></dl>
diff --git a/main/nn.attention.html b/main/nn.attention.html
index dc0efe72a83..cce0c6d4945 100644
--- a/main/nn.attention.html
+++ b/main/nn.attention.html
@@ -300,7 +300,7 @@
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     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
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     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/nn.functional.html b/main/nn.functional.html
index 45c99bef2ef..7a0dd7a4820 100644
--- a/main/nn.functional.html
+++ b/main/nn.functional.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/nn.html b/main/nn.html
index 2788af4079e..07c98f58939 100644
--- a/main/nn.html
+++ b/main/nn.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
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     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/nn.init.html b/main/nn.init.html
index cc8dd705273..b25bff58b09 100644
--- a/main/nn.init.html
+++ b/main/nn.init.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -616,7 +616,7 @@
 </div>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.init.calculate_gain">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.init.</span></span><span class="sig-name descname"><span class="pre">calculate_gain</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">nonlinearity</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">param</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/nn/init.html#calculate_gain"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/init.py#L72"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.init.calculate_gain" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.init.</span></span><span class="sig-name descname"><span class="pre">calculate_gain</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">nonlinearity</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">param</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/nn/init.html#calculate_gain"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/init.py#L72"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.init.calculate_gain" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the recommended gain value for the given nonlinearity function.</p>
 <p>The values are as follows:</p>
 <table class="docutils colwidths-auto align-default">
@@ -694,7 +694,7 @@
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.init.uniform_">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.init.</span></span><span class="sig-name descname"><span class="pre">uniform_</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">a</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">b</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">generator</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/nn/init.html#uniform_"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/init.py#L142"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.init.uniform_" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.init.</span></span><span class="sig-name descname"><span class="pre">uniform_</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">a</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">b</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">generator</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/nn/init.html#uniform_"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/init.py#L142"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.init.uniform_" title="Permalink to this definition">¶</a></dt>
 <dd><p>Fill the input Tensor with values drawn from the uniform distribution.</p>
 <p><span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi mathvariant="script">U</mi><mo stretchy="false">(</mo><mi>a</mi><mo separator="true">,</mo><mi>b</mi><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">\mathcal{U}(a, b)</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mord mathcal" style="margin-right:0.09931em;">U</span><span class="mopen">(</span><span class="mord mathnormal">a</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord mathnormal">b</span><span class="mclose">)</span></span></span></span></span>.</p>
 <dl class="field-list simple">
@@ -719,7 +719,7 @@
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.init.normal_">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.init.</span></span><span class="sig-name descname"><span class="pre">normal_</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mean</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">std</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">generator</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/nn/init.html#normal_"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/init.py#L169"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.init.normal_" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.init.</span></span><span class="sig-name descname"><span class="pre">normal_</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mean</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">std</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">generator</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/nn/init.html#normal_"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/init.py#L169"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.init.normal_" title="Permalink to this definition">¶</a></dt>
 <dd><p>Fill the input Tensor with values drawn from the normal distribution.</p>
 <p><span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi mathvariant="script">N</mi><mo stretchy="false">(</mo><mtext>mean</mtext><mo separator="true">,</mo><msup><mtext>std</mtext><mn>2</mn></msup><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">\mathcal{N}(\text{mean}, \text{std}^2)</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1.1484em;vertical-align:-0.25em;"></span><span class="mord mathcal" style="margin-right:0.14736em;">N</span><span class="mopen">(</span><span class="mord text"><span class="mord">mean</span></span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord"><span class="mord text"><span class="mord">std</span></span><span class="msupsub"><span class="vlist-t"><span class="vlist-r"><span class="vlist" style="height:0.8984em;"><span style="top:-3.1473em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight">2</span></span></span></span></span></span></span></span><span class="mclose">)</span></span></span></span></span>.</p>
 <dl class="field-list simple">
@@ -744,7 +744,7 @@
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.init.constant_">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.init.</span></span><span class="sig-name descname"><span class="pre">constant_</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">val</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/nn/init.html#constant_"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/init.py#L227"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.init.constant_" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.init.</span></span><span class="sig-name descname"><span class="pre">constant_</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">val</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/nn/init.html#constant_"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/init.py#L227"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.init.constant_" title="Permalink to this definition">¶</a></dt>
 <dd><p>Fill the input Tensor with the value <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mtext>val</mtext></mrow><annotation encoding="application/x-tex">\text{val}</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.6944em;"></span><span class="mord text"><span class="mord">val</span></span></span></span></span></span>.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -766,7 +766,7 @@
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.init.ones_">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.init.</span></span><span class="sig-name descname"><span class="pre">ones_</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tensor</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/nn/init.html#ones_"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/init.py#L245"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.init.ones_" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.init.</span></span><span class="sig-name descname"><span class="pre">ones_</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tensor</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/nn/init.html#ones_"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/init.py#L245"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.init.ones_" title="Permalink to this definition">¶</a></dt>
 <dd><p>Fill the input Tensor with the scalar value <cite>1</cite>.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -785,7 +785,7 @@
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.init.zeros_">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.init.</span></span><span class="sig-name descname"><span class="pre">zeros_</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tensor</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/nn/init.html#zeros_"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/init.py#L258"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.init.zeros_" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.init.</span></span><span class="sig-name descname"><span class="pre">zeros_</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tensor</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/nn/init.html#zeros_"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/init.py#L258"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.init.zeros_" title="Permalink to this definition">¶</a></dt>
 <dd><p>Fill the input Tensor with the scalar value <cite>0</cite>.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -804,7 +804,7 @@
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.init.eye_">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.init.</span></span><span class="sig-name descname"><span class="pre">eye_</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tensor</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/nn/init.html#eye_"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/init.py#L271"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.init.eye_" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.init.</span></span><span class="sig-name descname"><span class="pre">eye_</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tensor</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/nn/init.html#eye_"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/init.py#L271"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.init.eye_" title="Permalink to this definition">¶</a></dt>
 <dd><p>Fill the 2-dimensional input <cite>Tensor</cite> with the identity matrix.</p>
 <p>Preserves the identity of the inputs in <cite>Linear</cite> layers, where as
 many inputs are preserved as possible.</p>
@@ -822,7 +822,7 @@
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.init.dirac_">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.init.</span></span><span class="sig-name descname"><span class="pre">dirac_</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/nn/init.html#dirac_"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/init.py#L292"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.init.dirac_" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.init.</span></span><span class="sig-name descname"><span class="pre">dirac_</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/nn/init.html#dirac_"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/init.py#L292"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.init.dirac_" title="Permalink to this definition">¶</a></dt>
 <dd><p>Fill the {3, 4, 5}-dimensional input <cite>Tensor</cite> with the Dirac delta function.</p>
 <p>Preserves the identity of the inputs in <cite>Convolutional</cite>
 layers, where as many input channels are preserved as possible. In case
@@ -846,7 +846,7 @@
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.init.xavier_uniform_">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.init.</span></span><span class="sig-name descname"><span class="pre">xavier_uniform_</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">gain</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">generator</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/nn/init.html#xavier_uniform_"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/init.py#L366"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.init.xavier_uniform_" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.init.</span></span><span class="sig-name descname"><span class="pre">xavier_uniform_</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">gain</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">generator</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/nn/init.html#xavier_uniform_"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/init.py#L366"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.init.xavier_uniform_" title="Permalink to this definition">¶</a></dt>
 <dd><p>Fill the input <cite>Tensor</cite> with values using a Xavier uniform distribution.</p>
 <p>The method is described in <cite>Understanding the difficulty of training
 deep feedforward neural networks</cite> - Glorot, X. &amp; Bengio, Y. (2010).
@@ -893,7 +893,7 @@
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.init.xavier_normal_">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.init.</span></span><span class="sig-name descname"><span class="pre">xavier_normal_</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">gain</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">generator</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/nn/init.html#xavier_normal_"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/init.py#L407"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.init.xavier_normal_" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.init.</span></span><span class="sig-name descname"><span class="pre">xavier_normal_</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">gain</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">generator</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/nn/init.html#xavier_normal_"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/init.py#L407"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.init.xavier_normal_" title="Permalink to this definition">¶</a></dt>
 <dd><p>Fill the input <cite>Tensor</cite> with values using a Xavier normal distribution.</p>
 <p>The method is described in <cite>Understanding the difficulty of training deep feedforward
 neural networks</cite> - Glorot, X. &amp; Bengio, Y. (2010). The resulting tensor
@@ -939,7 +939,7 @@
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.init.kaiming_uniform_">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.init.</span></span><span class="sig-name descname"><span class="pre">kaiming_uniform_</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">a</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'fan_in'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">nonlinearity</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'leaky_relu'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">generator</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/nn/init.html#kaiming_uniform_"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/init.py#L456"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.init.kaiming_uniform_" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.init.</span></span><span class="sig-name descname"><span class="pre">kaiming_uniform_</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">a</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'fan_in'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">nonlinearity</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'leaky_relu'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">generator</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/nn/init.html#kaiming_uniform_"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/init.py#L456"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.init.kaiming_uniform_" title="Permalink to this definition">¶</a></dt>
 <dd><p>Fill the input <cite>Tensor</cite> with values using a Kaiming uniform distribution.</p>
 <p>The method is described in <cite>Delving deep into rectifiers: Surpassing
 human-level performance on ImageNet classification</cite> - He, K. et al. (2015).
@@ -990,7 +990,7 @@
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.init.kaiming_normal_">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.init.</span></span><span class="sig-name descname"><span class="pre">kaiming_normal_</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">a</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'fan_in'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">nonlinearity</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'leaky_relu'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">generator</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/nn/init.html#kaiming_normal_"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/init.py#L521"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.init.kaiming_normal_" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.init.</span></span><span class="sig-name descname"><span class="pre">kaiming_normal_</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">a</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'fan_in'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">nonlinearity</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'leaky_relu'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">generator</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/nn/init.html#kaiming_normal_"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/init.py#L521"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.init.kaiming_normal_" title="Permalink to this definition">¶</a></dt>
 <dd><p>Fill the input <cite>Tensor</cite> with values using a Kaiming normal distribution.</p>
 <p>The method is described in <cite>Delving deep into rectifiers: Surpassing
 human-level performance on ImageNet classification</cite> - He, K. et al. (2015).
@@ -1044,7 +1044,7 @@
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.init.trunc_normal_">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.init.</span></span><span class="sig-name descname"><span class="pre">trunc_normal_</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mean</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">std</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">a</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-2.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">b</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">generator</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/nn/init.html#trunc_normal_"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/init.py#L196"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.init.trunc_normal_" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.init.</span></span><span class="sig-name descname"><span class="pre">trunc_normal_</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mean</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">std</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">a</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-2.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">b</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">generator</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/nn/init.html#trunc_normal_"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/init.py#L196"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.init.trunc_normal_" title="Permalink to this definition">¶</a></dt>
 <dd><p>Fill the input Tensor with values drawn from a truncated normal distribution.</p>
 <p>The values are effectively drawn from the
 normal distribution <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi mathvariant="script">N</mi><mo stretchy="false">(</mo><mtext>mean</mtext><mo separator="true">,</mo><msup><mtext>std</mtext><mn>2</mn></msup><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">\mathcal{N}(\text{mean}, \text{std}^2)</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1.1484em;vertical-align:-0.25em;"></span><span class="mord mathcal" style="margin-right:0.14736em;">N</span><span class="mopen">(</span><span class="mord text"><span class="mord">mean</span></span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord"><span class="mord text"><span class="mord">std</span></span><span class="msupsub"><span class="vlist-t"><span class="vlist-r"><span class="vlist" style="height:0.8984em;"><span style="top:-3.1473em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight">2</span></span></span></span></span></span></span></span><span class="mclose">)</span></span></span></span></span>
@@ -1075,7 +1075,7 @@
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.init.orthogonal_">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.init.</span></span><span class="sig-name descname"><span class="pre">orthogonal_</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">gain</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">generator</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/nn/init.html#orthogonal_"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/init.py#L574"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.init.orthogonal_" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.init.</span></span><span class="sig-name descname"><span class="pre">orthogonal_</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">gain</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">generator</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/nn/init.html#orthogonal_"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/init.py#L574"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.init.orthogonal_" title="Permalink to this definition">¶</a></dt>
 <dd><p>Fill the input <cite>Tensor</cite> with a (semi) orthogonal matrix.</p>
 <p>Described in <cite>Exact solutions to the nonlinear dynamics of learning in deep
 linear neural networks</cite> - Saxe, A. et al. (2013). The input tensor must have
@@ -1099,7 +1099,7 @@
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.nn.init.sparse_">
-<span class="sig-prename descclassname"><span class="pre">torch.nn.init.</span></span><span class="sig-name descname"><span class="pre">sparse_</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sparsity</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">std</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.01</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">generator</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/nn/init.html#sparse_"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/nn/init.py#L625"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.init.sparse_" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.nn.init.</span></span><span class="sig-name descname"><span class="pre">sparse_</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sparsity</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">std</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.01</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">generator</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/nn/init.html#sparse_"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/nn/init.py#L625"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.nn.init.sparse_" title="Permalink to this definition">¶</a></dt>
 <dd><p>Fill the 2D input <cite>Tensor</cite> as a sparse matrix.</p>
 <p>The non-zero elements will be drawn from the normal distribution
 <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi mathvariant="script">N</mi><mo stretchy="false">(</mo><mn>0</mn><mo separator="true">,</mo><mn>0.01</mn><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">\mathcal{N}(0, 0.01)</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mord mathcal" style="margin-right:0.14736em;">N</span><span class="mopen">(</span><span class="mord">0</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.1667em;"></span><span class="mord">0.01</span><span class="mclose">)</span></span></span></span></span>, as described in <cite>Deep learning via
diff --git a/main/notes/amp_examples.html b/main/notes/amp_examples.html
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+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
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diff --git a/main/notes/cuda.html b/main/notes/cuda.html
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@@ -727,6 +727,8 @@
 </div>
 <div class="section" id="reduced-precision-reduction-in-fp16-gemms">
 <span id="fp16reducedprecision"></span><h2>Reduced Precision Reduction in FP16 GEMMs<a class="headerlink" href="#reduced-precision-reduction-in-fp16-gemms" title="Permalink to this heading">¶</a></h2>
+<p>(Distinct from full FP16 accumulation that is intended for hardware that has higher throughput
+with FP16 accumulation than FP32 accumulation, see <a class="reference internal" href="#fp16accumulation"><span class="std std-ref">Full FP16 accumulation</span></a>)</p>
 <p>fp16 GEMMs are potentially done with some intermediate reduced precision reductions (e.g., in fp16 rather than fp32). These selective reductions in precision can allow for higher performance on certain workloads (particularly those with a large <cite>k</cite> dimension) and GPU architectures at the cost of numerical precision and potential for overflow.</p>
 <p>Some example benchmark data on V100:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="p">[</span><span class="o">---------------------------</span> <span class="n">bench_gemm_transformer</span> <span class="o">--------------------------</span><span class="p">]</span>
@@ -772,6 +774,21 @@
 </pre></div>
 </div>
 </div>
+<div class="section" id="full-fp16-accmumulation-in-fp16-gemms">
+<span id="fp16accumulation"></span><h2>Full FP16 Accmumulation in FP16 GEMMs<a class="headerlink" href="#full-fp16-accmumulation-in-fp16-gemms" title="Permalink to this heading">¶</a></h2>
+<p>Certain GPUs have increased performance when doing _all_ FP16 GEMM accumulation
+in FP16, at the cost of numerical precision and greater likelihood of overflow.
+Note that this setting only has an effect on GPUs of compute capability 7.0 (Volta)
+or newer.</p>
+<p>This behavior can be enabled via:</p>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">torch</span><span class="o">.</span><span class="n">backends</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">matmul</span><span class="o">.</span><span class="n">allow_fp16_accumulation</span> <span class="o">=</span> <span class="kc">True</span>
+</pre></div>
+</div>
+<p>To toggle the reduced precision reduction flags in C++, one can do</p>
+<div class="highlight-C++ notranslate"><div class="highlight"><pre><span></span><span class="n">at</span><span class="o">::</span><span class="n">globalContext</span><span class="p">().</span><span class="n">setAllowFP16AccumulationCuBLAS</span><span class="p">(</span><span class="nb">true</span><span class="p">);</span>
+</pre></div>
+</div>
+</div>
 <div class="section" id="asynchronous-execution">
 <h2>Asynchronous execution<a class="headerlink" href="#asynchronous-execution" title="Permalink to this heading">¶</a></h2>
 <p>By default, GPU operations are asynchronous.  When you call a function that
@@ -1810,6 +1827,7 @@ <h4>Sharing memory across captures<a class="headerlink" href="#sharing-memory-ac
 <li><a class="reference internal" href="#tensorfloat-32-tf32-on-ampere-and-later-devices">TensorFloat-32 (TF32) on Ampere (and later) devices</a></li>
 <li><a class="reference internal" href="#reduced-precision-reduction-in-fp16-gemms">Reduced Precision Reduction in FP16 GEMMs</a></li>
 <li><a class="reference internal" href="#reduced-precision-reduction-in-bf16-gemms">Reduced Precision Reduction in BF16 GEMMs</a></li>
+<li><a class="reference internal" href="#full-fp16-accmumulation-in-fp16-gemms">Full FP16 Accmumulation in FP16 GEMMs</a></li>
 <li><a class="reference internal" href="#asynchronous-execution">Asynchronous execution</a><ul>
 <li><a class="reference internal" href="#cuda-streams">CUDA streams</a></li>
 <li><a class="reference internal" href="#stream-semantics-of-backward-passes">Stream semantics of backward passes</a><ul>
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@@ -1028,7 +1028,7 @@ <h3><a class="toc-backref" href="#id14">torch.full always inferring a float dtyp
 <p>The following utility functions are related to serialization:</p>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.serialization.register_package">
-<span class="sig-prename descclassname"><span class="pre">torch.serialization.</span></span><span class="sig-name descname"><span class="pre">register_package</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">priority</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tagger</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">deserializer</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/serialization.html#register_package"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/serialization.py#L427"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.serialization.register_package" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.serialization.</span></span><span class="sig-name descname"><span class="pre">register_package</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">priority</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tagger</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">deserializer</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/serialization.html#register_package"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/serialization.py#L427"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.serialization.register_package" title="Permalink to this definition">¶</a></dt>
 <dd><p>Registers callables for tagging and deserializing storage objects with an associated priority.
 Tagging associates a device with a storage object at save time while deserializing moves a
 storage object to an appropriate device at load time. <code class="xref py py-attr docutils literal notranslate"><span class="pre">tagger</span></code> and <code class="xref py py-attr docutils literal notranslate"><span class="pre">deserializer</span></code>
@@ -1069,7 +1069,7 @@ <h3><a class="toc-backref" href="#id14">torch.full always inferring a float dtyp
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.serialization.get_crc32_options">
-<span class="sig-prename descclassname"><span class="pre">torch.serialization.</span></span><span class="sig-name descname"><span class="pre">get_crc32_options</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/serialization.html#get_crc32_options"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/serialization.py#L171"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.serialization.get_crc32_options" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.serialization.</span></span><span class="sig-name descname"><span class="pre">get_crc32_options</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/serialization.html#get_crc32_options"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/serialization.py#L171"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.serialization.get_crc32_options" title="Permalink to this definition">¶</a></dt>
 <dd><p>Get whether <a class="reference internal" href="../generated/torch.save.html#torch.save" title="torch.save"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.save()</span></code></a> computes and writes crc32 for each record.</p>
 <p>Defaults to <code class="docutils literal notranslate"><span class="pre">True</span></code>.</p>
 <dl class="field-list simple">
@@ -1081,7 +1081,7 @@ <h3><a class="toc-backref" href="#id14">torch.full always inferring a float dtyp
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.serialization.set_crc32_options">
-<span class="sig-prename descclassname"><span class="pre">torch.serialization.</span></span><span class="sig-name descname"><span class="pre">set_crc32_options</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">compute_crc32</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/serialization.html#set_crc32_options"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/serialization.py#L182"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.serialization.set_crc32_options" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.serialization.</span></span><span class="sig-name descname"><span class="pre">set_crc32_options</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">compute_crc32</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/serialization.html#set_crc32_options"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/serialization.py#L182"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.serialization.set_crc32_options" title="Permalink to this definition">¶</a></dt>
 <dd><p>Set whether <a class="reference internal" href="../generated/torch.save.html#torch.save" title="torch.save"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.save()</span></code></a> computes and writes crc32 for each record.</p>
 <div class="admonition note">
 <p class="admonition-title">Note</p>
@@ -1098,7 +1098,7 @@ <h3><a class="toc-backref" href="#id14">torch.full always inferring a float dtyp
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.serialization.get_default_load_endianness">
-<span class="sig-prename descclassname"><span class="pre">torch.serialization.</span></span><span class="sig-name descname"><span class="pre">get_default_load_endianness</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/serialization.html#get_default_load_endianness"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/serialization.py#L137"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.serialization.get_default_load_endianness" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.serialization.</span></span><span class="sig-name descname"><span class="pre">get_default_load_endianness</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/serialization.html#get_default_load_endianness"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/serialization.py#L137"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.serialization.get_default_load_endianness" title="Permalink to this definition">¶</a></dt>
 <dd><p>Get fallback byte order for loading files</p>
 <p>If byteorder mark is not present in saved checkpoint,
 this byte order is used as fallback.
@@ -1115,7 +1115,7 @@ <h3><a class="toc-backref" href="#id14">torch.full always inferring a float dtyp
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.serialization.set_default_load_endianness">
-<span class="sig-prename descclassname"><span class="pre">torch.serialization.</span></span><span class="sig-name descname"><span class="pre">set_default_load_endianness</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">endianness</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/serialization.html#set_default_load_endianness"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/serialization.py#L153"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.serialization.set_default_load_endianness" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.serialization.</span></span><span class="sig-name descname"><span class="pre">set_default_load_endianness</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">endianness</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/serialization.html#set_default_load_endianness"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/serialization.py#L153"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.serialization.set_default_load_endianness" title="Permalink to this definition">¶</a></dt>
 <dd><p>Set fallback byte order for loading files</p>
 <p>If byteorder mark is not present in saved checkpoint,
 this byte order is used as fallback.
@@ -1129,7 +1129,7 @@ <h3><a class="toc-backref" href="#id14">torch.full always inferring a float dtyp
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.serialization.get_default_mmap_options">
-<span class="sig-prename descclassname"><span class="pre">torch.serialization.</span></span><span class="sig-name descname"><span class="pre">get_default_mmap_options</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/serialization.html#get_default_mmap_options"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/serialization.py#L199"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.serialization.get_default_mmap_options" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.serialization.</span></span><span class="sig-name descname"><span class="pre">get_default_mmap_options</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/serialization.html#get_default_mmap_options"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/serialization.py#L199"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.serialization.get_default_mmap_options" title="Permalink to this definition">¶</a></dt>
 <dd><p>Get default mmap options for <a class="reference internal" href="../generated/torch.load.html#torch.load" title="torch.load"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.load()</span></code></a> with <code class="docutils literal notranslate"><span class="pre">mmap=True</span></code>.</p>
 <p>Defaults to <code class="docutils literal notranslate"><span class="pre">mmap.MAP_PRIVATE</span></code>.</p>
 <dl class="field-list simple">
@@ -1144,7 +1144,7 @@ <h3><a class="toc-backref" href="#id14">torch.full always inferring a float dtyp
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.serialization.set_default_mmap_options">
-<span class="sig-prename descclassname"><span class="pre">torch.serialization.</span></span><span class="sig-name descname"><span class="pre">set_default_mmap_options</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">flags</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/serialization.html#set_default_mmap_options"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/serialization.py#L214"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.serialization.set_default_mmap_options" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.serialization.</span></span><span class="sig-name descname"><span class="pre">set_default_mmap_options</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">flags</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/serialization.html#set_default_mmap_options"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/serialization.py#L214"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.serialization.set_default_mmap_options" title="Permalink to this definition">¶</a></dt>
 <dd><p>Context manager or function to set default mmap options for <a class="reference internal" href="../generated/torch.load.html#torch.load" title="torch.load"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.load()</span></code></a> with <code class="docutils literal notranslate"><span class="pre">mmap=True</span></code> to flags.</p>
 <p>For now, only either <code class="docutils literal notranslate"><span class="pre">mmap.MAP_PRIVATE</span></code> or <code class="docutils literal notranslate"><span class="pre">mmap.MAP_SHARED</span></code> are supported.
 Please open an issue if you need any other option to be added here.</p>
@@ -1161,7 +1161,7 @@ <h3><a class="toc-backref" href="#id14">torch.full always inferring a float dtyp
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.serialization.add_safe_globals">
-<span class="sig-prename descclassname"><span class="pre">torch.serialization.</span></span><span class="sig-name descname"><span class="pre">add_safe_globals</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">safe_globals</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/serialization.html#add_safe_globals"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/serialization.py#L267"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.serialization.add_safe_globals" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.serialization.</span></span><span class="sig-name descname"><span class="pre">add_safe_globals</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">safe_globals</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/serialization.html#add_safe_globals"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/serialization.py#L267"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.serialization.add_safe_globals" title="Permalink to this definition">¶</a></dt>
 <dd><p>Marks the given globals as safe for <code class="docutils literal notranslate"><span class="pre">weights_only</span></code> load. For example, functions
 added to this list can be called during unpickling, classes could be instantiated
 and have state set.</p>
@@ -1196,7 +1196,7 @@ <h3><a class="toc-backref" href="#id14">torch.full always inferring a float dtyp
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.serialization.clear_safe_globals">
-<span class="sig-prename descclassname"><span class="pre">torch.serialization.</span></span><span class="sig-name descname"><span class="pre">clear_safe_globals</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/serialization.html#clear_safe_globals"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/serialization.py#L253"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.serialization.clear_safe_globals" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.serialization.</span></span><span class="sig-name descname"><span class="pre">clear_safe_globals</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/serialization.html#clear_safe_globals"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/serialization.py#L253"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.serialization.clear_safe_globals" title="Permalink to this definition">¶</a></dt>
 <dd><p>Clears the list of globals that are safe for <code class="docutils literal notranslate"><span class="pre">weights_only</span></code> load.</p>
 <dl class="field-list simple">
 </dl>
@@ -1204,7 +1204,7 @@ <h3><a class="toc-backref" href="#id14">torch.full always inferring a float dtyp
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.serialization.get_safe_globals">
-<span class="sig-prename descclassname"><span class="pre">torch.serialization.</span></span><span class="sig-name descname"><span class="pre">get_safe_globals</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/serialization.html#get_safe_globals"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/serialization.py#L260"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.serialization.get_safe_globals" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.serialization.</span></span><span class="sig-name descname"><span class="pre">get_safe_globals</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/serialization.html#get_safe_globals"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/serialization.py#L260"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.serialization.get_safe_globals" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns the list of user-added globals that are safe for <code class="docutils literal notranslate"><span class="pre">weights_only</span></code> load.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -1215,7 +1215,7 @@ <h3><a class="toc-backref" href="#id14">torch.full always inferring a float dtyp
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.serialization.get_unsafe_globals_in_checkpoint">
-<span class="sig-prename descclassname"><span class="pre">torch.serialization.</span></span><span class="sig-name descname"><span class="pre">get_unsafe_globals_in_checkpoint</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">f</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/serialization.html#get_unsafe_globals_in_checkpoint"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/serialization.py#L328"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.serialization.get_unsafe_globals_in_checkpoint" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.serialization.</span></span><span class="sig-name descname"><span class="pre">get_unsafe_globals_in_checkpoint</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">f</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/serialization.html#get_unsafe_globals_in_checkpoint"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/serialization.py#L328"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.serialization.get_unsafe_globals_in_checkpoint" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns a list of strings of functions/classes in a <code class="docutils literal notranslate"><span class="pre">torch.save</span></code> object that are not safe for <code class="docutils literal notranslate"><span class="pre">weights_only</span></code>.</p>
 <p>For a given function or class <code class="docutils literal notranslate"><span class="pre">f</span></code>, the corresponding string will be of the form
 <code class="docutils literal notranslate"><span class="pre">{f.__module__}.{f.__name__}</span></code>.</p>
@@ -1243,7 +1243,7 @@ <h3><a class="toc-backref" href="#id14">torch.full always inferring a float dtyp
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.serialization.safe_globals">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.serialization.</span></span><span class="sig-name descname"><span class="pre">safe_globals</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">safe_globals</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/serialization.html#safe_globals"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/serialization.py#L303"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.serialization.safe_globals" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.serialization.</span></span><span class="sig-name descname"><span class="pre">safe_globals</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">safe_globals</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/serialization.html#safe_globals"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/serialization.py#L303"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.serialization.safe_globals" title="Permalink to this definition">¶</a></dt>
 <dd><p>Context-manager that adds certain globals as safe for <code class="docutils literal notranslate"><span class="pre">weights_only</span></code> load.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -1271,7 +1271,7 @@ <h3><a class="toc-backref" href="#id14">torch.full always inferring a float dtyp
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.serialization.skip_data">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.serialization.</span></span><span class="sig-name descname"><span class="pre">skip_data</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">materialize_fake_tensors</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/serialization.html#skip_data"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/serialization.py#L370"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.serialization.skip_data" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.serialization.</span></span><span class="sig-name descname"><span class="pre">skip_data</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">materialize_fake_tensors</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/serialization.html#skip_data"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/serialization.py#L370"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.serialization.skip_data" title="Permalink to this definition">¶</a></dt>
 <dd><p>Context-manager that skips writing storage bytes for <code class="docutils literal notranslate"><span class="pre">torch.save</span></code> calls.</p>
 <p>Storages will still be saved, but the space that their bytes would usually be written to
 will be empty space. The storage bytes can then be populated in a separate pass.</p>
diff --git a/main/notes/windows.html b/main/notes/windows.html
index c44bb491840..c66fc569bc5 100644
--- a/main/notes/windows.html
+++ b/main/notes/windows.html
@@ -300,7 +300,7 @@
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     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/objects.inv b/main/objects.inv
index b46b7db2adf..fbac30b502a 100644
Binary files a/main/objects.inv and b/main/objects.inv differ
diff --git a/main/onnx.html b/main/onnx.html
index b3cdca49124..d5c0e009c30 100644
--- a/main/onnx.html
+++ b/main/onnx.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/onnx_dynamo.html b/main/onnx_dynamo.html
index ffeb06471a0..3cafe53b607 100644
--- a/main/onnx_dynamo.html
+++ b/main/onnx_dynamo.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -721,7 +721,7 @@ <h2><a class="toc-backref" href="#id5">When the conversion fails</a><a class="he
 <h2><a class="toc-backref" href="#id6">API Reference</a><a class="headerlink" href="#api-reference" title="Permalink to this heading">¶</a></h2>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.onnx.dynamo_export">
-<span class="sig-prename descclassname"><span class="pre">torch.onnx.</span></span><span class="sig-name descname"><span class="pre">dynamo_export</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">/</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">model_args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">export_options</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">model_kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/onnx.html#dynamo_export"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/onnx/__init__.py#L406"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.onnx.dynamo_export" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.onnx.</span></span><span class="sig-name descname"><span class="pre">dynamo_export</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">/</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">model_args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">export_options</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">model_kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/onnx.html#dynamo_export"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/onnx/__init__.py#L405"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.onnx.dynamo_export" title="Permalink to this definition">¶</a></dt>
 <dd><p>Export a torch.nn.Module to an ONNX graph.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -778,7 +778,7 @@ <h2><a class="toc-backref" href="#id6">API Reference</a><a class="headerlink" hr
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.onnx.ONNXProgram.apply_weights">
-<span class="sig-name descname"><span class="pre">apply_weights</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state_dict</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/onnx/_internal/exporter/_onnx_program.html#ONNXProgram.apply_weights"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/onnx/_internal/exporter/_onnx_program.py#L192"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.onnx.ONNXProgram.apply_weights" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">apply_weights</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state_dict</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/onnx/_internal/exporter/_onnx_program.html#ONNXProgram.apply_weights"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/onnx/_internal/exporter/_onnx_program.py#L192"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.onnx.ONNXProgram.apply_weights" title="Permalink to this definition">¶</a></dt>
 <dd><p>Apply the weights from the specified state dict to the ONNX model.</p>
 <p>Use this method to replace FakeTensors or other weights.</p>
 <dl class="field-list simple">
@@ -790,7 +790,7 @@ <h2><a class="toc-backref" href="#id6">API Reference</a><a class="headerlink" hr
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.onnx.ONNXProgram.initialize_inference_session">
-<span class="sig-name descname"><span class="pre">initialize_inference_session</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">initializer=&lt;function</span> <span class="pre">_ort_session_initializer&gt;</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/onnx/_internal/exporter/_onnx_program.html#ONNXProgram.initialize_inference_session"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/onnx/_internal/exporter/_onnx_program.py#L214"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.onnx.ONNXProgram.initialize_inference_session" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">initialize_inference_session</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">initializer=&lt;function</span> <span class="pre">_ort_session_initializer&gt;</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/onnx/_internal/exporter/_onnx_program.html#ONNXProgram.initialize_inference_session"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/onnx/_internal/exporter/_onnx_program.py#L214"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.onnx.ONNXProgram.initialize_inference_session" title="Permalink to this definition">¶</a></dt>
 <dd><p>Initialize the ONNX Runtime inference session.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -809,7 +809,7 @@ <h2><a class="toc-backref" href="#id6">API Reference</a><a class="headerlink" hr
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.onnx.ONNXProgram.optimize">
-<span class="sig-name descname"><span class="pre">optimize</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/onnx/_internal/exporter/_onnx_program.html#ONNXProgram.optimize"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/onnx/_internal/exporter/_onnx_program.py#L119"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.onnx.ONNXProgram.optimize" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">optimize</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/onnx/_internal/exporter/_onnx_program.html#ONNXProgram.optimize"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/onnx/_internal/exporter/_onnx_program.py#L119"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.onnx.ONNXProgram.optimize" title="Permalink to this definition">¶</a></dt>
 <dd><p>Optimize the ONNX model.</p>
 <p>This method optimizes the ONNX model by performing constant folding and
 eliminating redundancies in the graph. The optimization is done in-place.</p>
@@ -819,7 +819,7 @@ <h2><a class="toc-backref" href="#id6">API Reference</a><a class="headerlink" hr
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.onnx.ONNXProgram.release">
-<span class="sig-name descname"><span class="pre">release</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/onnx/_internal/exporter/_onnx_program.html#ONNXProgram.release"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/onnx/_internal/exporter/_onnx_program.py#L244"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.onnx.ONNXProgram.release" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">release</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/onnx/_internal/exporter/_onnx_program.html#ONNXProgram.release"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/onnx/_internal/exporter/_onnx_program.py#L244"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.onnx.ONNXProgram.release" title="Permalink to this definition">¶</a></dt>
 <dd><p>Release the inference session.</p>
 <p>You may call this method to release the resources used by the inference session.</p>
 <dl class="field-list simple">
@@ -828,7 +828,7 @@ <h2><a class="toc-backref" href="#id6">API Reference</a><a class="headerlink" hr
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.onnx.ONNXProgram.save">
-<span class="sig-name descname"><span class="pre">save</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">destination</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">include_initializers</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">keep_initializers_as_inputs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">external_data</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/onnx/_internal/exporter/_onnx_program.html#ONNXProgram.save"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/onnx/_internal/exporter/_onnx_program.py#L127"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.onnx.ONNXProgram.save" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">save</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">destination</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">include_initializers</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">keep_initializers_as_inputs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">external_data</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/onnx/_internal/exporter/_onnx_program.html#ONNXProgram.save"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/onnx/_internal/exporter/_onnx_program.py#L127"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.onnx.ONNXProgram.save" title="Permalink to this definition">¶</a></dt>
 <dd><p>Save the ONNX model to the specified destination.</p>
 <p>When <code class="docutils literal notranslate"><span class="pre">external_data</span></code> is <code class="docutils literal notranslate"><span class="pre">True</span></code> or the model is larger than 2GB,
 the weights are saved as external data in a separate file.</p>
@@ -887,7 +887,7 @@ <h2><a class="toc-backref" href="#id6">API Reference</a><a class="headerlink" hr
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.onnx.enable_fake_mode">
-<span class="sig-prename descclassname"><span class="pre">torch.onnx.</span></span><span class="sig-name descname"><span class="pre">enable_fake_mode</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/onnx/_internal/_exporter_legacy.py#L363"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.onnx.enable_fake_mode" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.onnx.</span></span><span class="sig-name descname"><span class="pre">enable_fake_mode</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/onnx/_internal/_exporter_legacy.py#L372"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.onnx.enable_fake_mode" title="Permalink to this definition">¶</a></dt>
 <dd><p>Enable fake mode for the duration of the context.</p>
 <p>Internally it instantiates a <code class="xref py py-class docutils literal notranslate"><span class="pre">torch._subclasses.fake_tensor.FakeTensorMode</span></code> context manager
 that converts user input and model parameters into <code class="xref py py-class docutils literal notranslate"><span class="pre">torch._subclasses.fake_tensor.FakeTensor</span></code>.</p>
@@ -958,11 +958,9 @@ <h2><a class="toc-backref" href="#id6">API Reference</a><a class="headerlink" hr
 <p>The registry maintains a mapping from qualified names to symbolic functions under a
 fixed opset version. It supports registering custom onnx-script functions and for
 dispatcher to dispatch calls to the appropriate function.</p>
-<dl class="field-list simple">
-</dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.onnx.OnnxRegistry.get_op_functions">
-<span class="sig-name descname"><span class="pre">get_op_functions</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">namespace</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">op_name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">overload</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/onnx/_internal/_exporter_legacy.html#OnnxRegistry.get_op_functions"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/onnx/_internal/_exporter_legacy.py#L178"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.onnx.OnnxRegistry.get_op_functions" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">get_op_functions</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">namespace</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">op_name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">overload</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/onnx/_internal/_exporter_legacy.html#OnnxRegistry.get_op_functions"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/onnx/_internal/_exporter_legacy.py#L183"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.onnx.OnnxRegistry.get_op_functions" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns a list of ONNXFunctions for the given op: torch.ops.&lt;namespace&gt;.&lt;op_name&gt;.&lt;overload&gt;.</p>
 <p>The list is ordered by the time of registration. The custom operators should be
 in the second half of the list.</p>
@@ -987,7 +985,7 @@ <h2><a class="toc-backref" href="#id6">API Reference</a><a class="headerlink" hr
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.onnx.OnnxRegistry.is_registered_op">
-<span class="sig-name descname"><span class="pre">is_registered_op</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">namespace</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">op_name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">overload</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/onnx/_internal/_exporter_legacy.html#OnnxRegistry.is_registered_op"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/onnx/_internal/_exporter_legacy.py#L200"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.onnx.OnnxRegistry.is_registered_op" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">is_registered_op</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">namespace</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">op_name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">overload</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/onnx/_internal/_exporter_legacy.html#OnnxRegistry.is_registered_op"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/onnx/_internal/_exporter_legacy.py#L205"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.onnx.OnnxRegistry.is_registered_op" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns whether the given op is registered: torch.ops.&lt;namespace&gt;.&lt;op_name&gt;.&lt;overload&gt;.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -1015,7 +1013,7 @@ <h2><a class="toc-backref" href="#id6">API Reference</a><a class="headerlink" hr
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.onnx.OnnxRegistry.register_op">
-<span class="sig-name descname"><span class="pre">register_op</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">function</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">namespace</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">op_name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">overload</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">is_complex</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/onnx/_internal/_exporter_legacy.html#OnnxRegistry.register_op"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/onnx/_internal/_exporter_legacy.py#L146"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.onnx.OnnxRegistry.register_op" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_op</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">function</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">namespace</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">op_name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">overload</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">is_complex</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/onnx/_internal/_exporter_legacy.html#OnnxRegistry.register_op"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/onnx/_internal/_exporter_legacy.py#L151"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.onnx.OnnxRegistry.register_op" title="Permalink to this definition">¶</a></dt>
 <dd><p>Registers a custom operator: torch.ops.&lt;namespace&gt;.&lt;op_name&gt;.&lt;overload&gt;.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/onnx_dynamo_memory_usage.html b/main/onnx_dynamo_memory_usage.html
index 49c62caf0f3..c3b24d01a61 100644
--- a/main/onnx_dynamo_memory_usage.html
+++ b/main/onnx_dynamo_memory_usage.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/onnx_dynamo_onnxruntime_backend.html b/main/onnx_dynamo_onnxruntime_backend.html
index 80840e48b54..cb9ec18d490 100644
--- a/main/onnx_dynamo_onnxruntime_backend.html
+++ b/main/onnx_dynamo_onnxruntime_backend.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -617,7 +617,7 @@ <h1>ONNX Backend for TorchDynamo<a class="headerlink" href="#onnx-backend-for-to
 </div>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.onnx.is_onnxrt_backend_supported">
-<span class="sig-prename descclassname"><span class="pre">torch.onnx.</span></span><span class="sig-name descname"><span class="pre">is_onnxrt_backend_supported</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/onnx/_internal/onnxruntime.py#L47"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.onnx.is_onnxrt_backend_supported" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.onnx.</span></span><span class="sig-name descname"><span class="pre">is_onnxrt_backend_supported</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/onnx/_internal/onnxruntime.py#L47"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.onnx.is_onnxrt_backend_supported" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns <code class="docutils literal notranslate"><span class="pre">True</span></code> if ONNX Runtime dependencies are installed and usable
 to support TorchDynamo backend integration; <code class="docutils literal notranslate"><span class="pre">False</span></code> otherwise.</p>
 <p>Example:</p>
diff --git a/main/onnx_torchscript.html b/main/onnx_torchscript.html
index 438cbe4746e..37d840d1204 100644
--- a/main/onnx_torchscript.html
+++ b/main/onnx_torchscript.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -1258,7 +1258,7 @@ <h2><a class="toc-backref" href="#id27">Frequently Asked Questions</a><a class="
 <h3><a class="toc-backref" href="#id29">Functions</a><a class="headerlink" href="#functions" title="Permalink to this heading">¶</a></h3>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.onnx.export">
-<span class="sig-prename descclassname"><span class="pre">torch.onnx.</span></span><span class="sig-name descname"><span class="pre">export</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args=()</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">f=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kwargs=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">export_params=True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_names=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_names=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opset_version=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dynamic_axes=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">keep_initializers_as_inputs=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dynamo=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">external_data=True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dynamic_shapes=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">custom_translation_table=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">report=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">optimize=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verify=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">profile=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dump_exported_program=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">artifacts_dir='.'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fallback=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">training=&lt;TrainingMode.EVAL:</span> <span class="pre">0&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">operator_export_type=&lt;OperatorExportTypes.ONNX:</span> <span class="pre">0&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">do_constant_folding=True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">custom_opsets=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">export_modules_as_functions=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">autograd_inlining=True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">**_</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/onnx.html#export"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/onnx/__init__.py#L130"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.onnx.export" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.onnx.</span></span><span class="sig-name descname"><span class="pre">export</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args=()</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">f=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kwargs=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">export_params=True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_names=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_names=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opset_version=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dynamic_axes=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">keep_initializers_as_inputs=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dynamo=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">external_data=True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dynamic_shapes=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">custom_translation_table=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">report=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">optimize=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verify=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">profile=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dump_exported_program=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">artifacts_dir='.'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fallback=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">training=&lt;TrainingMode.EVAL:</span> <span class="pre">0&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">operator_export_type=&lt;OperatorExportTypes.ONNX:</span> <span class="pre">0&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">do_constant_folding=True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">custom_opsets=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">export_modules_as_functions=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">autograd_inlining=True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/onnx.html#export"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/onnx/__init__.py#L130"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.onnx.export" title="Permalink to this definition">¶</a></dt>
 <dd><p>Exports a model into ONNX format.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -1452,7 +1452,7 @@ <h3><a class="toc-backref" href="#id29">Functions</a><a class="headerlink" href=
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.onnx.register_custom_op_symbolic">
-<span class="sig-prename descclassname"><span class="pre">torch.onnx.</span></span><span class="sig-name descname"><span class="pre">register_custom_op_symbolic</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">symbolic_name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">symbolic_fn</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opset_version</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/onnx/utils.html#register_custom_op_symbolic"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/onnx/utils.py#L1760"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.onnx.register_custom_op_symbolic" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.onnx.</span></span><span class="sig-name descname"><span class="pre">register_custom_op_symbolic</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">symbolic_name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">symbolic_fn</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opset_version</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/onnx/utils.html#register_custom_op_symbolic"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/onnx/utils.py#L1760"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.onnx.register_custom_op_symbolic" title="Permalink to this definition">¶</a></dt>
 <dd><p>Registers a symbolic function for a custom operator.</p>
 <p>When the user registers symbolic for custom/contrib ops,
 it is highly recommended to add shape inference for that operator via setType API,
@@ -1475,7 +1475,7 @@ <h3><a class="toc-backref" href="#id29">Functions</a><a class="headerlink" href=
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.onnx.unregister_custom_op_symbolic">
-<span class="sig-prename descclassname"><span class="pre">torch.onnx.</span></span><span class="sig-name descname"><span class="pre">unregister_custom_op_symbolic</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">symbolic_name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opset_version</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/onnx/utils.html#unregister_custom_op_symbolic"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/onnx/utils.py#L1790"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.onnx.unregister_custom_op_symbolic" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.onnx.</span></span><span class="sig-name descname"><span class="pre">unregister_custom_op_symbolic</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">symbolic_name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opset_version</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/onnx/utils.html#unregister_custom_op_symbolic"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/onnx/utils.py#L1790"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.onnx.unregister_custom_op_symbolic" title="Permalink to this definition">¶</a></dt>
 <dd><p>Unregisters <code class="docutils literal notranslate"><span class="pre">symbolic_name</span></code>.</p>
 <p>See “Custom Operators” in the module documentation for an example usage.</p>
 <dl class="field-list simple">
@@ -1491,7 +1491,7 @@ <h3><a class="toc-backref" href="#id29">Functions</a><a class="headerlink" href=
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.onnx.select_model_mode_for_export">
-<span class="sig-prename descclassname"><span class="pre">torch.onnx.</span></span><span class="sig-name descname"><span class="pre">select_model_mode_for_export</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mode</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/onnx/utils.html#select_model_mode_for_export"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/onnx/utils.py#L58"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.onnx.select_model_mode_for_export" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.onnx.</span></span><span class="sig-name descname"><span class="pre">select_model_mode_for_export</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mode</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/onnx/utils.html#select_model_mode_for_export"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/onnx/utils.py#L58"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.onnx.select_model_mode_for_export" title="Permalink to this definition">¶</a></dt>
 <dd><p>A context manager to temporarily set the training mode of <code class="docutils literal notranslate"><span class="pre">model</span></code>
 to <code class="docutils literal notranslate"><span class="pre">mode</span></code>, resetting it when we exit the with-block.</p>
 <dl class="field-list simple">
@@ -1506,7 +1506,7 @@ <h3><a class="toc-backref" href="#id29">Functions</a><a class="headerlink" href=
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.onnx.is_in_onnx_export">
-<span class="sig-prename descclassname"><span class="pre">torch.onnx.</span></span><span class="sig-name descname"><span class="pre">is_in_onnx_export</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/onnx/utils.html#is_in_onnx_export"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/onnx/utils.py#L48"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.onnx.is_in_onnx_export" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.onnx.</span></span><span class="sig-name descname"><span class="pre">is_in_onnx_export</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/onnx/utils.html#is_in_onnx_export"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/onnx/utils.py#L48"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.onnx.is_in_onnx_export" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns whether it is in the middle of ONNX export.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -1517,7 +1517,7 @@ <h3><a class="toc-backref" href="#id29">Functions</a><a class="headerlink" href=
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.onnx.verification.find_mismatch">
-<span class="sig-prename descclassname"><span class="pre">torch.onnx.verification.</span></span><span class="sig-name descname"><span class="pre">find_mismatch</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">do_constant_folding=True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">training=&lt;TrainingMode.EVAL:</span> <span class="pre">0&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opset_version=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">keep_initializers_as_inputs=True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">options=None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/onnx/verification.html#find_mismatch"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/onnx/verification.py#L1678"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.onnx.verification.find_mismatch" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.onnx.verification.</span></span><span class="sig-name descname"><span class="pre">find_mismatch</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">do_constant_folding=True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">training=&lt;TrainingMode.EVAL:</span> <span class="pre">0&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">opset_version=None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">keep_initializers_as_inputs=True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">options=None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/onnx/verification.html#find_mismatch"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/onnx/verification.py#L1694"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.onnx.verification.find_mismatch" title="Permalink to this definition">¶</a></dt>
 <dd><p>Find all mismatches between the original model and the exported model.</p>
 <p>Experimental. The API is subject to change.</p>
 <p>This tool helps debug the mismatch between the original PyTorch model and exported
diff --git a/main/onnx_torchscript_supported_aten_ops.html b/main/onnx_torchscript_supported_aten_ops.html
index f765e34da36..2752bbc310d 100644
--- a/main/onnx_torchscript_supported_aten_ops.html
+++ b/main/onnx_torchscript_supported_aten_ops.html
@@ -298,7 +298,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/optim.html b/main/optim.html
index 0e0534c31cc..9892846eeef 100644
--- a/main/optim.html
+++ b/main/optim.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -724,7 +724,7 @@ <h4><code class="docutils literal notranslate"><span class="pre">optimizer.step(
 <span id="optimizer-algorithms"></span><h2>Base class<a class="headerlink" href="#base-class" title="Permalink to this heading">¶</a></h2>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.optim.Optimizer">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.optim.</span></span><span class="sig-name descname"><span class="pre">Optimizer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">params</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">defaults</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/optim/optimizer.html#Optimizer"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/optimizer.py#L316"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Optimizer" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.optim.</span></span><span class="sig-name descname"><span class="pre">Optimizer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">params</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">defaults</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/optim/optimizer.html#Optimizer"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/optimizer.py#L316"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.Optimizer" title="Permalink to this definition">¶</a></dt>
 <dd><p>Base class for all optimizers.</p>
 <div class="admonition warning">
 <p class="admonition-title">Warning</p>
@@ -1452,13 +1452,13 @@ <h3>Putting it all together: EMA<a class="headerlink" href="#putting-it-all-toge
 </table>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.optim.swa_utils.get_ema_multi_avg_fn">
-<span class="sig-prename descclassname"><span class="pre">torch.optim.swa_utils.</span></span><span class="sig-name descname"><span class="pre">get_ema_multi_avg_fn</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">decay</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.999</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/optim/swa_utils.html#get_ema_multi_avg_fn"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/swa_utils.py#L35"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.get_ema_multi_avg_fn" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.optim.swa_utils.</span></span><span class="sig-name descname"><span class="pre">get_ema_multi_avg_fn</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">decay</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.999</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/optim/swa_utils.html#get_ema_multi_avg_fn"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/swa_utils.py#L35"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.get_ema_multi_avg_fn" title="Permalink to this definition">¶</a></dt>
 <dd><p>Get the function applying exponential moving average (EMA) across multiple params.</p>
 </dd></dl>
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.optim.swa_utils.update_bn">
-<span class="sig-prename descclassname"><span class="pre">torch.optim.swa_utils.</span></span><span class="sig-name descname"><span class="pre">update_bn</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">loader</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/optim/swa_utils.html#update_bn"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/optim/swa_utils.py#L309"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.update_bn" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.optim.swa_utils.</span></span><span class="sig-name descname"><span class="pre">update_bn</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">loader</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/optim/swa_utils.html#update_bn"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/optim/swa_utils.py#L309"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.optim.swa_utils.update_bn" title="Permalink to this definition">¶</a></dt>
 <dd><p>Update BatchNorm running_mean, running_var buffers in the model.</p>
 <p>It performs one pass over data in <cite>loader</cite> to estimate the activation
 statistics for BatchNorm layers in the model.</p>
diff --git a/main/package.html b/main/package.html
index 0c08b748031..fe1c54548ae 100644
--- a/main/package.html
+++ b/main/package.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -1299,7 +1299,7 @@ <h4>Mangling<a class="headerlink" href="#mangling" title="Permalink to this head
 <h2><a class="toc-backref" href="#id20">API Reference</a><a class="headerlink" href="#api-reference" title="Permalink to this heading">¶</a></h2>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.package.PackagingError">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.package.</span></span><span class="sig-name descname"><span class="pre">PackagingError</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dependency_graph</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">debug</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/package/package_exporter.html#PackagingError"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/package/package_exporter.py#L118"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.package.PackagingError" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.package.</span></span><span class="sig-name descname"><span class="pre">PackagingError</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dependency_graph</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">debug</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/package/package_exporter.html#PackagingError"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/package/package_exporter.py#L118"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.package.PackagingError" title="Permalink to this definition">¶</a></dt>
 <dd><p>This exception is raised when there is an issue with exporting a package.
 <code class="docutils literal notranslate"><span class="pre">PackageExporter</span></code> will attempt to gather up all the errors and present
 them to you at once.</p>
@@ -1309,14 +1309,14 @@ <h2><a class="toc-backref" href="#id20">API Reference</a><a class="headerlink" h
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.package.EmptyMatchError">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.package.</span></span><span class="sig-name descname"><span class="pre">EmptyMatchError</span></span><a class="reference internal" href="_modules/torch/package/package_exporter.html#EmptyMatchError"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/package/package_exporter.py#L112"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.package.EmptyMatchError" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.package.</span></span><span class="sig-name descname"><span class="pre">EmptyMatchError</span></span><a class="reference internal" href="_modules/torch/package/package_exporter.html#EmptyMatchError"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/package/package_exporter.py#L112"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.package.EmptyMatchError" title="Permalink to this definition">¶</a></dt>
 <dd><p>This is an exception that is thrown when a mock or extern is marked as
 <code class="docutils literal notranslate"><span class="pre">allow_empty=False</span></code>, and is not matched with any module during packaging.</p>
 </dd></dl>
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.package.PackageExporter">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.package.</span></span><span class="sig-name descname"><span class="pre">PackageExporter</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">f</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">importer=&lt;torch.package.importer._SysImporter</span> <span class="pre">object&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">debug=False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/package/package_exporter.html#PackageExporter"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/package/package_exporter.py#L172"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.package.PackageExporter" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.package.</span></span><span class="sig-name descname"><span class="pre">PackageExporter</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">f</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">importer=&lt;torch.package.importer._SysImporter</span> <span class="pre">object&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">debug=False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/package/package_exporter.html#PackageExporter"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/package/package_exporter.py#L172"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.package.PackageExporter" title="Permalink to this definition">¶</a></dt>
 <dd><p>Exporters allow you to write packages of code, pickled Python data, and
 arbitrary binary and text resources into a self-contained package.</p>
 <p>Imports can load this code in a hermetic way, such that code is loaded
@@ -1340,7 +1340,7 @@ <h2><a class="toc-backref" href="#id20">API Reference</a><a class="headerlink" h
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.package.PackageExporter.__init__">
-<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">f</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">importer=&lt;torch.package.importer._SysImporter</span> <span class="pre">object&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">debug=False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/package/package_exporter.html#PackageExporter.__init__"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/package/package_exporter.py#L203"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.package.PackageExporter.__init__" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">f</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">importer=&lt;torch.package.importer._SysImporter</span> <span class="pre">object&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">debug=False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/package/package_exporter.html#PackageExporter.__init__"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/package/package_exporter.py#L203"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.package.PackageExporter.__init__" title="Permalink to this definition">¶</a></dt>
 <dd><p>Create an exporter.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -1357,7 +1357,7 @@ <h2><a class="toc-backref" href="#id20">API Reference</a><a class="headerlink" h
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.package.PackageExporter.add_dependency">
-<span class="sig-name descname"><span class="pre">add_dependency</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module_name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dependencies</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/package/package_exporter.html#PackageExporter.add_dependency"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/package/package_exporter.py#L452"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.package.PackageExporter.add_dependency" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">add_dependency</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module_name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dependencies</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/package/package_exporter.html#PackageExporter.add_dependency"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/package/package_exporter.py#L452"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.package.PackageExporter.add_dependency" title="Permalink to this definition">¶</a></dt>
 <dd><p>Given a module, add it to the dependency graph according to patterns
 specified by the user.</p>
 <dl class="field-list simple">
@@ -1366,7 +1366,7 @@ <h2><a class="toc-backref" href="#id20">API Reference</a><a class="headerlink" h
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.package.PackageExporter.all_paths">
-<span class="sig-name descname"><span class="pre">all_paths</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">src</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dst</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/package/package_exporter.html#PackageExporter.all_paths"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/package/package_exporter.py#L1164"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.package.PackageExporter.all_paths" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">all_paths</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">src</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dst</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/package/package_exporter.html#PackageExporter.all_paths"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/package/package_exporter.py#L1164"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.package.PackageExporter.all_paths" title="Permalink to this definition">¶</a></dt>
 <dd><dl class="simple">
 <dt>Return a dot representation of the subgraph</dt><dd><p>that has all paths from src to dst.</p>
 </dd>
@@ -1384,7 +1384,7 @@ <h2><a class="toc-backref" href="#id20">API Reference</a><a class="headerlink" h
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.package.PackageExporter.close">
-<span class="sig-name descname"><span class="pre">close</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/package/package_exporter.html#PackageExporter.close"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/package/package_exporter.py#L1067"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.package.PackageExporter.close" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">close</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/package/package_exporter.html#PackageExporter.close"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/package/package_exporter.py#L1067"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.package.PackageExporter.close" title="Permalink to this definition">¶</a></dt>
 <dd><p>Write the package to the filesystem. Any calls after <a class="reference internal" href="#torch.package.PackageExporter.close" title="torch.package.PackageExporter.close"><code class="xref py py-meth docutils literal notranslate"><span class="pre">close()</span></code></a> are now invalid.
 It is preferable to use resource guard syntax instead:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">with</span> <span class="n">PackageExporter</span><span class="p">(</span><span class="s2">&quot;file.zip&quot;</span><span class="p">)</span> <span class="k">as</span> <span class="n">e</span><span class="p">:</span>
@@ -1395,7 +1395,7 @@ <h2><a class="toc-backref" href="#id20">API Reference</a><a class="headerlink" h
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.package.PackageExporter.denied_modules">
-<span class="sig-name descname"><span class="pre">denied_modules</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/package/package_exporter.html#PackageExporter.denied_modules"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/package/package_exporter.py#L1144"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.package.PackageExporter.denied_modules" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">denied_modules</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/package/package_exporter.html#PackageExporter.denied_modules"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/package/package_exporter.py#L1144"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.package.PackageExporter.denied_modules" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return all modules that are currently denied.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Returns</dt>
@@ -1410,7 +1410,7 @@ <h2><a class="toc-backref" href="#id20">API Reference</a><a class="headerlink" h
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.package.PackageExporter.deny">
-<span class="sig-name descname"><span class="pre">deny</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">include</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">exclude</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">()</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/package/package_exporter.html#PackageExporter.deny"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/package/package_exporter.py#L890"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.package.PackageExporter.deny" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">deny</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">include</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">exclude</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">()</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/package/package_exporter.html#PackageExporter.deny"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/package/package_exporter.py#L890"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.package.PackageExporter.deny" title="Permalink to this definition">¶</a></dt>
 <dd><p>Blocklist modules who names match the given glob patterns from the list of modules the package can import.
 If a dependency on any matching packages is found, a <a class="reference internal" href="#torch.package.PackagingError" title="torch.package.PackagingError"><code class="xref py py-class docutils literal notranslate"><span class="pre">PackagingError</span></code></a> is raised.</p>
 <dl class="field-list simple">
@@ -1426,7 +1426,7 @@ <h2><a class="toc-backref" href="#id20">API Reference</a><a class="headerlink" h
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.package.PackageExporter.dependency_graph_string">
-<span class="sig-name descname"><span class="pre">dependency_graph_string</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/package/package_exporter.html#PackageExporter.dependency_graph_string"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/package/package_exporter.py#L1098"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.package.PackageExporter.dependency_graph_string" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">dependency_graph_string</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/package/package_exporter.html#PackageExporter.dependency_graph_string"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/package/package_exporter.py#L1098"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.package.PackageExporter.dependency_graph_string" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns digraph string representation of dependencies in package.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Returns</dt>
@@ -1440,7 +1440,7 @@ <h2><a class="toc-backref" href="#id20">API Reference</a><a class="headerlink" h
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.package.PackageExporter.extern">
-<span class="sig-name descname"><span class="pre">extern</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">include</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">exclude</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">()</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">allow_empty</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/package/package_exporter.html#PackageExporter.extern"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/package/package_exporter.py#L859"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.package.PackageExporter.extern" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">extern</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">include</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">exclude</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">()</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">allow_empty</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/package/package_exporter.html#PackageExporter.extern"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/package/package_exporter.py#L859"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.package.PackageExporter.extern" title="Permalink to this definition">¶</a></dt>
 <dd><p>Include <code class="docutils literal notranslate"><span class="pre">module</span></code> in the list of external modules the package can import.
 This will prevent dependency discovery from saving
 it in the package. The importer will load an external module directly from the standard import system.
@@ -1465,7 +1465,7 @@ <h2><a class="toc-backref" href="#id20">API Reference</a><a class="headerlink" h
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.package.PackageExporter.externed_modules">
-<span class="sig-name descname"><span class="pre">externed_modules</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/package/package_exporter.html#PackageExporter.externed_modules"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/package/package_exporter.py#L1117"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.package.PackageExporter.externed_modules" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">externed_modules</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/package/package_exporter.html#PackageExporter.externed_modules"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/package/package_exporter.py#L1117"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.package.PackageExporter.externed_modules" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return all modules that are currently externed.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Returns</dt>
@@ -1480,7 +1480,7 @@ <h2><a class="toc-backref" href="#id20">API Reference</a><a class="headerlink" h
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.package.PackageExporter.get_rdeps">
-<span class="sig-name descname"><span class="pre">get_rdeps</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module_name</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/package/package_exporter.html#PackageExporter.get_rdeps"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/package/package_exporter.py#L1153"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.package.PackageExporter.get_rdeps" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">get_rdeps</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module_name</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/package/package_exporter.html#PackageExporter.get_rdeps"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/package/package_exporter.py#L1153"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.package.PackageExporter.get_rdeps" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return a list of all modules which depend on the module <code class="docutils literal notranslate"><span class="pre">module_name</span></code>.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Returns</dt>
@@ -1494,7 +1494,7 @@ <h2><a class="toc-backref" href="#id20">API Reference</a><a class="headerlink" h
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.package.PackageExporter.get_unique_id">
-<span class="sig-name descname"><span class="pre">get_unique_id</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/package/package_exporter.html#PackageExporter.get_unique_id"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/package/package_exporter.py#L316"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.package.PackageExporter.get_unique_id" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">get_unique_id</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/package/package_exporter.html#PackageExporter.get_unique_id"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/package/package_exporter.py#L316"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.package.PackageExporter.get_unique_id" title="Permalink to this definition">¶</a></dt>
 <dd><p>Get an id. This id is guaranteed to only be handed out once for this package.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -1505,7 +1505,7 @@ <h2><a class="toc-backref" href="#id20">API Reference</a><a class="headerlink" h
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.package.PackageExporter.intern">
-<span class="sig-name descname"><span class="pre">intern</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">include</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">exclude</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">()</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">allow_empty</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/package/package_exporter.html#PackageExporter.intern"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/package/package_exporter.py#L792"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.package.PackageExporter.intern" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">intern</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">include</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">exclude</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">()</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">allow_empty</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/package/package_exporter.html#PackageExporter.intern"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/package/package_exporter.py#L792"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.package.PackageExporter.intern" title="Permalink to this definition">¶</a></dt>
 <dd><p>Specify modules that should be packaged. A module must match some <code class="docutils literal notranslate"><span class="pre">intern</span></code> pattern in order to be
 included in the package and have its dependencies processed recursively.</p>
 <dl class="field-list simple">
@@ -1525,7 +1525,7 @@ <h2><a class="toc-backref" href="#id20">API Reference</a><a class="headerlink" h
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.package.PackageExporter.interned_modules">
-<span class="sig-name descname"><span class="pre">interned_modules</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/package/package_exporter.html#PackageExporter.interned_modules"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/package/package_exporter.py#L1126"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.package.PackageExporter.interned_modules" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">interned_modules</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/package/package_exporter.html#PackageExporter.interned_modules"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/package/package_exporter.py#L1126"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.package.PackageExporter.interned_modules" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return all modules that are currently interned.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Returns</dt>
@@ -1540,7 +1540,7 @@ <h2><a class="toc-backref" href="#id20">API Reference</a><a class="headerlink" h
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.package.PackageExporter.mock">
-<span class="sig-name descname"><span class="pre">mock</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">include</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">exclude</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">()</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">allow_empty</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/package/package_exporter.html#PackageExporter.mock"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/package/package_exporter.py#L818"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.package.PackageExporter.mock" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">mock</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">include</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">exclude</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">()</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">allow_empty</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/package/package_exporter.html#PackageExporter.mock"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/package/package_exporter.py#L818"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.package.PackageExporter.mock" title="Permalink to this definition">¶</a></dt>
 <dd><p>Replace some required modules with a mock implementation.  Mocked modules will return a fake
 object for any attribute accessed from it. Because we copy file-by-file, the dependency resolution will sometimes
 find files that are imported by model files but whose functionality is never used
@@ -1576,7 +1576,7 @@ <h2><a class="toc-backref" href="#id20">API Reference</a><a class="headerlink" h
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.package.PackageExporter.mocked_modules">
-<span class="sig-name descname"><span class="pre">mocked_modules</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/package/package_exporter.html#PackageExporter.mocked_modules"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/package/package_exporter.py#L1135"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.package.PackageExporter.mocked_modules" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">mocked_modules</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/package/package_exporter.html#PackageExporter.mocked_modules"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/package/package_exporter.py#L1135"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.package.PackageExporter.mocked_modules" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return all modules that are currently mocked.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Returns</dt>
@@ -1591,7 +1591,7 @@ <h2><a class="toc-backref" href="#id20">API Reference</a><a class="headerlink" h
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.package.PackageExporter.register_extern_hook">
-<span class="sig-name descname"><span class="pre">register_extern_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/package/package_exporter.html#PackageExporter.register_extern_hook"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/package/package_exporter.py#L735"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.package.PackageExporter.register_extern_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_extern_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/package/package_exporter.html#PackageExporter.register_extern_hook"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/package/package_exporter.py#L735"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.package.PackageExporter.register_extern_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Registers an extern hook on the exporter.</p>
 <p>The hook will be called each time a module matches against an <a class="reference internal" href="#torch.package.PackageExporter.extern" title="torch.package.PackageExporter.extern"><code class="xref py py-meth docutils literal notranslate"><span class="pre">extern()</span></code></a> pattern.
 It should have the following signature:</p>
@@ -1612,7 +1612,7 @@ <h2><a class="toc-backref" href="#id20">API Reference</a><a class="headerlink" h
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.package.PackageExporter.register_intern_hook">
-<span class="sig-name descname"><span class="pre">register_intern_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/package/package_exporter.html#PackageExporter.register_intern_hook"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/package/package_exporter.py#L773"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.package.PackageExporter.register_intern_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_intern_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/package/package_exporter.html#PackageExporter.register_intern_hook"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/package/package_exporter.py#L773"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.package.PackageExporter.register_intern_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Registers an intern hook on the exporter.</p>
 <p>The hook will be called each time a module matches against an <a class="reference internal" href="#torch.package.PackageExporter.intern" title="torch.package.PackageExporter.intern"><code class="xref py py-meth docutils literal notranslate"><span class="pre">intern()</span></code></a> pattern.
 It should have the following signature:</p>
@@ -1633,7 +1633,7 @@ <h2><a class="toc-backref" href="#id20">API Reference</a><a class="headerlink" h
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.package.PackageExporter.register_mock_hook">
-<span class="sig-name descname"><span class="pre">register_mock_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/package/package_exporter.html#PackageExporter.register_mock_hook"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/package/package_exporter.py#L754"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.package.PackageExporter.register_mock_hook" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">register_mock_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/package/package_exporter.html#PackageExporter.register_mock_hook"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/package/package_exporter.py#L754"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.package.PackageExporter.register_mock_hook" title="Permalink to this definition">¶</a></dt>
 <dd><p>Registers a mock hook on the exporter.</p>
 <p>The hook will be called each time a module matches against a <a class="reference internal" href="#torch.package.PackageExporter.mock" title="torch.package.PackageExporter.mock"><code class="xref py py-meth docutils literal notranslate"><span class="pre">mock()</span></code></a> pattern.
 It should have the following signature:</p>
@@ -1654,7 +1654,7 @@ <h2><a class="toc-backref" href="#id20">API Reference</a><a class="headerlink" h
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.package.PackageExporter.save_binary">
-<span class="sig-name descname"><span class="pre">save_binary</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">package</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">resource</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">binary</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/package/package_exporter.html#PackageExporter.save_binary"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/package/package_exporter.py#L724"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.package.PackageExporter.save_binary" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">save_binary</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">package</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">resource</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">binary</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/package/package_exporter.html#PackageExporter.save_binary"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/package/package_exporter.py#L724"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.package.PackageExporter.save_binary" title="Permalink to this definition">¶</a></dt>
 <dd><p>Save raw bytes to the package.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -1669,7 +1669,7 @@ <h2><a class="toc-backref" href="#id20">API Reference</a><a class="headerlink" h
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.package.PackageExporter.save_module">
-<span class="sig-name descname"><span class="pre">save_module</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module_name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dependencies</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/package/package_exporter.html#PackageExporter.save_module"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/package/package_exporter.py#L511"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.package.PackageExporter.save_module" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">save_module</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module_name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dependencies</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/package/package_exporter.html#PackageExporter.save_module"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/package/package_exporter.py#L511"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.package.PackageExporter.save_module" title="Permalink to this definition">¶</a></dt>
 <dd><p>Save the code for <code class="docutils literal notranslate"><span class="pre">module</span></code> into the package. Code for the module is resolved using the <code class="docutils literal notranslate"><span class="pre">importers</span></code> path to find the
 module object, and then using its <code class="docutils literal notranslate"><span class="pre">__file__</span></code> attribute to find the source code.</p>
 <dl class="field-list simple">
@@ -1685,7 +1685,7 @@ <h2><a class="toc-backref" href="#id20">API Reference</a><a class="headerlink" h
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.package.PackageExporter.save_pickle">
-<span class="sig-name descname"><span class="pre">save_pickle</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">package</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">resource</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">obj</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dependencies</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pickle_protocol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">3</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/package/package_exporter.html#PackageExporter.save_pickle"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/package/package_exporter.py#L583"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.package.PackageExporter.save_pickle" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">save_pickle</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">package</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">resource</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">obj</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dependencies</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pickle_protocol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">3</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/package/package_exporter.html#PackageExporter.save_pickle"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/package/package_exporter.py#L583"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.package.PackageExporter.save_pickle" title="Permalink to this definition">¶</a></dt>
 <dd><p>Save a python object to the archive using pickle. Equivalent to <a class="reference internal" href="generated/torch.save.html#torch.save" title="torch.save"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.save()</span></code></a> but saving into
 the archive rather than a stand-alone file. Standard pickle does not save the code, only the objects.
 If <code class="docutils literal notranslate"><span class="pre">dependencies</span></code> is true, this method will also scan the pickled objects for which modules are required
@@ -1708,7 +1708,7 @@ <h2><a class="toc-backref" href="#id20">API Reference</a><a class="headerlink" h
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.package.PackageExporter.save_source_file">
-<span class="sig-name descname"><span class="pre">save_source_file</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module_name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">file_or_directory</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dependencies</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/package/package_exporter.html#PackageExporter.save_source_file"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/package/package_exporter.py#L262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.package.PackageExporter.save_source_file" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">save_source_file</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module_name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">file_or_directory</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dependencies</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/package/package_exporter.html#PackageExporter.save_source_file"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/package/package_exporter.py#L262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.package.PackageExporter.save_source_file" title="Permalink to this definition">¶</a></dt>
 <dd><p>Adds the local file system <code class="docutils literal notranslate"><span class="pre">file_or_directory</span></code> to the source package to provide the code
 for <code class="docutils literal notranslate"><span class="pre">module_name</span></code>.</p>
 <dl class="field-list simple">
@@ -1726,7 +1726,7 @@ <h2><a class="toc-backref" href="#id20">API Reference</a><a class="headerlink" h
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.package.PackageExporter.save_source_string">
-<span class="sig-name descname"><span class="pre">save_source_string</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module_name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">src</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">is_package</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dependencies</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/package/package_exporter.html#PackageExporter.save_source_string"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/package/package_exporter.py#L371"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.package.PackageExporter.save_source_string" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">save_source_string</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module_name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">src</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">is_package</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dependencies</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/package/package_exporter.html#PackageExporter.save_source_string"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/package/package_exporter.py#L371"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.package.PackageExporter.save_source_string" title="Permalink to this definition">¶</a></dt>
 <dd><p>Adds <code class="docutils literal notranslate"><span class="pre">src</span></code> as the source code for <code class="docutils literal notranslate"><span class="pre">module_name</span></code> in the exported package.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -1743,7 +1743,7 @@ <h2><a class="toc-backref" href="#id20">API Reference</a><a class="headerlink" h
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.package.PackageExporter.save_text">
-<span class="sig-name descname"><span class="pre">save_text</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">package</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">resource</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">text</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/package/package_exporter.html#PackageExporter.save_text"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/package/package_exporter.py#L714"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.package.PackageExporter.save_text" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">save_text</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">package</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">resource</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">text</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/package/package_exporter.html#PackageExporter.save_text"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/package/package_exporter.py#L714"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.package.PackageExporter.save_text" title="Permalink to this definition">¶</a></dt>
 <dd><p>Save text data to the package.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -1760,7 +1760,7 @@ <h2><a class="toc-backref" href="#id20">API Reference</a><a class="headerlink" h
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.package.PackageImporter">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.package.</span></span><span class="sig-name descname"><span class="pre">PackageImporter</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">file_or_buffer</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">module_allowed=&lt;function</span> <span class="pre">PackageImporter.&lt;lambda&gt;&gt;</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/package/package_importer.html#PackageImporter"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/package/package_importer.py#L66"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.package.PackageImporter" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.package.</span></span><span class="sig-name descname"><span class="pre">PackageImporter</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">file_or_buffer</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">module_allowed=&lt;function</span> <span class="pre">PackageImporter.&lt;lambda&gt;&gt;</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/package/package_importer.html#PackageImporter"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/package/package_importer.py#L66"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.package.PackageImporter" title="Permalink to this definition">¶</a></dt>
 <dd><p>Importers allow you to load code written to packages by <a class="reference internal" href="#torch.package.PackageExporter" title="torch.package.PackageExporter"><code class="xref py py-class docutils literal notranslate"><span class="pre">PackageExporter</span></code></a>.
 Code is loaded in a hermetic way, using files from the package
 rather than the normal python import system. This allows
@@ -1775,7 +1775,7 @@ <h2><a class="toc-backref" href="#id20">API Reference</a><a class="headerlink" h
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.package.PackageImporter.__init__">
-<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">file_or_buffer</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">module_allowed=&lt;function</span> <span class="pre">PackageImporter.&lt;lambda&gt;&gt;</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/package/package_importer.html#PackageImporter.__init__"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/package/package_importer.py#L86"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.package.PackageImporter.__init__" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">file_or_buffer</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">module_allowed=&lt;function</span> <span class="pre">PackageImporter.&lt;lambda&gt;&gt;</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/package/package_importer.html#PackageImporter.__init__"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/package/package_importer.py#L86"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.package.PackageImporter.__init__" title="Permalink to this definition">¶</a></dt>
 <dd><p>Open <code class="docutils literal notranslate"><span class="pre">file_or_buffer</span></code> for importing. This checks that the imported package only requires modules
 allowed by <code class="docutils literal notranslate"><span class="pre">module_allowed</span></code></p>
 <dl class="field-list simple">
@@ -1796,7 +1796,7 @@ <h2><a class="toc-backref" href="#id20">API Reference</a><a class="headerlink" h
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.package.PackageImporter.file_structure">
-<span class="sig-name descname"><span class="pre">file_structure</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">include</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'**'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">exclude</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">()</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/package/package_importer.html#PackageImporter.file_structure"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/package/package_importer.py#L322"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.package.PackageImporter.file_structure" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">file_structure</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">include</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'**'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">exclude</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">()</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/package/package_importer.html#PackageImporter.file_structure"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/package/package_importer.py#L322"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.package.PackageImporter.file_structure" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns a file structure representation of package’s zipfile.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -1818,7 +1818,7 @@ <h2><a class="toc-backref" href="#id20">API Reference</a><a class="headerlink" h
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.package.PackageImporter.id">
-<span class="sig-name descname"><span class="pre">id</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/package/package_importer.html#PackageImporter.id"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/package/package_importer.py#L313"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.package.PackageImporter.id" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">id</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/package/package_importer.html#PackageImporter.id"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/package/package_importer.py#L313"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.package.PackageImporter.id" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns internal identifier that torch.package uses to distinguish <a class="reference internal" href="#torch.package.PackageImporter" title="torch.package.PackageImporter"><code class="xref py py-class docutils literal notranslate"><span class="pre">PackageImporter</span></code></a> instances.
 Looks like:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="o">&lt;</span><span class="n">torch_package_0</span><span class="o">&gt;</span>
@@ -1828,7 +1828,7 @@ <h2><a class="toc-backref" href="#id20">API Reference</a><a class="headerlink" h
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.package.PackageImporter.import_module">
-<span class="sig-name descname"><span class="pre">import_module</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">package</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/package/package_importer.html#PackageImporter.import_module"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/package/package_importer.py#L157"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.package.PackageImporter.import_module" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">import_module</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">package</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/package/package_importer.html#PackageImporter.import_module"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/package/package_importer.py#L157"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.package.PackageImporter.import_module" title="Permalink to this definition">¶</a></dt>
 <dd><p>Load a module from the package if it hasn’t already been loaded, and then return
 the module. Modules are loaded locally
 to the importer and will appear in <code class="docutils literal notranslate"><span class="pre">self.modules</span></code> rather than <code class="docutils literal notranslate"><span class="pre">sys.modules</span></code>.</p>
@@ -1850,7 +1850,7 @@ <h2><a class="toc-backref" href="#id20">API Reference</a><a class="headerlink" h
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.package.PackageImporter.load_binary">
-<span class="sig-name descname"><span class="pre">load_binary</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">package</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">resource</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/package/package_importer.html#PackageImporter.load_binary"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/package/package_importer.py#L180"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.package.PackageImporter.load_binary" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">load_binary</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">package</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">resource</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/package/package_importer.html#PackageImporter.load_binary"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/package/package_importer.py#L180"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.package.PackageImporter.load_binary" title="Permalink to this definition">¶</a></dt>
 <dd><p>Load raw bytes.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -1870,7 +1870,7 @@ <h2><a class="toc-backref" href="#id20">API Reference</a><a class="headerlink" h
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.package.PackageImporter.load_pickle">
-<span class="sig-name descname"><span class="pre">load_pickle</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">package</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">resource</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">map_location</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/package/package_importer.html#PackageImporter.load_pickle"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/package/package_importer.py#L215"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.package.PackageImporter.load_pickle" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">load_pickle</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">package</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">resource</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">map_location</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/package/package_importer.html#PackageImporter.load_pickle"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/package/package_importer.py#L215"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.package.PackageImporter.load_pickle" title="Permalink to this definition">¶</a></dt>
 <dd><p>Unpickles the resource from the package, loading any modules that are needed to construct the objects
 using <a class="reference internal" href="#torch.package.PackageImporter.import_module" title="torch.package.PackageImporter.import_module"><code class="xref py py-meth docutils literal notranslate"><span class="pre">import_module()</span></code></a>.</p>
 <dl class="field-list simple">
@@ -1892,7 +1892,7 @@ <h2><a class="toc-backref" href="#id20">API Reference</a><a class="headerlink" h
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.package.PackageImporter.load_text">
-<span class="sig-name descname"><span class="pre">load_text</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">package</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">resource</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">encoding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'utf-8'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">errors</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'strict'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/package/package_importer.html#PackageImporter.load_text"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/package/package_importer.py#L194"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.package.PackageImporter.load_text" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">load_text</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">package</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">resource</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">encoding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'utf-8'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">errors</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'strict'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/package/package_importer.html#PackageImporter.load_text"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/package/package_importer.py#L194"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.package.PackageImporter.load_text" title="Permalink to this definition">¶</a></dt>
 <dd><p>Load a string.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -1914,7 +1914,7 @@ <h2><a class="toc-backref" href="#id20">API Reference</a><a class="headerlink" h
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.package.PackageImporter.python_version">
-<span class="sig-name descname"><span class="pre">python_version</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/package/package_importer.html#PackageImporter.python_version"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/package/package_importer.py#L341"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.package.PackageImporter.python_version" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">python_version</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/package/package_importer.html#PackageImporter.python_version"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/package/package_importer.py#L341"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.package.PackageImporter.python_version" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns the version of python that was used to create this package.</p>
 <p>Note: this function is experimental and not Forward Compatible. The plan is to move this into a lock
 file later on.</p>
@@ -1929,7 +1929,7 @@ <h2><a class="toc-backref" href="#id20">API Reference</a><a class="headerlink" h
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.package.Directory">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.package.</span></span><span class="sig-name descname"><span class="pre">Directory</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">is_dir</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/package/file_structure_representation.html#Directory"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/package/file_structure_representation.py#L9"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.package.Directory" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.package.</span></span><span class="sig-name descname"><span class="pre">Directory</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">is_dir</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/package/file_structure_representation.html#Directory"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/package/file_structure_representation.py#L9"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.package.Directory" title="Permalink to this definition">¶</a></dt>
 <dd><p>A file structure representation. Organized as Directory nodes that have lists of
 their Directory children. Directories for a package are created by calling
 <a class="reference internal" href="#torch.package.PackageImporter.file_structure" title="torch.package.PackageImporter.file_structure"><code class="xref py py-meth docutils literal notranslate"><span class="pre">PackageImporter.file_structure()</span></code></a>.</p>
@@ -1937,7 +1937,7 @@ <h2><a class="toc-backref" href="#id20">API Reference</a><a class="headerlink" h
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.package.Directory.has_file">
-<span class="sig-name descname"><span class="pre">has_file</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">filename</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/package/file_structure_representation.html#Directory.has_file"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/package/file_structure_representation.py#L47"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.package.Directory.has_file" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">has_file</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">filename</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/package/file_structure_representation.html#Directory.has_file"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/package/file_structure_representation.py#L47"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.package.Directory.has_file" title="Permalink to this definition">¶</a></dt>
 <dd><p>Checks if a file is present in a <a class="reference internal" href="#torch.package.Directory" title="torch.package.Directory"><code class="xref py py-class docutils literal notranslate"><span class="pre">Directory</span></code></a>.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/profiler.html b/main/profiler.html
index 55cc41f5140..6e0175f47d0 100644
--- a/main/profiler.html
+++ b/main/profiler.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -622,7 +622,7 @@ <h1>torch.profiler<a class="headerlink" href="#torch-profiler" title="Permalink
 <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to this heading">¶</a></h2>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.profiler._KinetoProfile">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.profiler.</span></span><span class="sig-name descname"><span class="pre">_KinetoProfile</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">activities</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">record_shapes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">profile_memory</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">with_stack</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">with_flops</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">with_modules</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">experimental_config</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">execution_trace_observer</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">acc_events</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">custom_trace_id_callback</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/profiler/profiler.html#_KinetoProfile"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/profiler/profiler.py#L94"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.profiler._KinetoProfile" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.profiler.</span></span><span class="sig-name descname"><span class="pre">_KinetoProfile</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">activities</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">record_shapes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">profile_memory</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">with_stack</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">with_flops</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">with_modules</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">experimental_config</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">execution_trace_observer</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">acc_events</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">custom_trace_id_callback</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/profiler/profiler.html#_KinetoProfile"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/profiler/profiler.py#L94"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.profiler._KinetoProfile" title="Permalink to this definition">¶</a></dt>
 <dd><p>Low-level profiler wrap the autograd profile</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -665,7 +665,7 @@ <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to
 </div>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.profiler._KinetoProfile.add_metadata">
-<span class="sig-name descname"><span class="pre">add_metadata</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">key</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/profiler/profiler.html#_KinetoProfile.add_metadata"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/profiler/profiler.py#L325"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.profiler._KinetoProfile.add_metadata" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">add_metadata</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">key</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/profiler/profiler.html#_KinetoProfile.add_metadata"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/profiler/profiler.py#L325"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.profiler._KinetoProfile.add_metadata" title="Permalink to this definition">¶</a></dt>
 <dd><p>Adds a user defined metadata with a string key and a string value
 into the trace file</p>
 <dl class="field-list simple">
@@ -674,7 +674,7 @@ <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.profiler._KinetoProfile.add_metadata_json">
-<span class="sig-name descname"><span class="pre">add_metadata_json</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">key</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/profiler/profiler.html#_KinetoProfile.add_metadata_json"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/profiler/profiler.py#L333"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.profiler._KinetoProfile.add_metadata_json" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">add_metadata_json</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">key</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/profiler/profiler.html#_KinetoProfile.add_metadata_json"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/profiler/profiler.py#L333"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.profiler._KinetoProfile.add_metadata_json" title="Permalink to this definition">¶</a></dt>
 <dd><p>Adds a user defined metadata with a string key and a valid json value
 into the trace file</p>
 <dl class="field-list simple">
@@ -683,14 +683,14 @@ <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.profiler._KinetoProfile.events">
-<span class="sig-name descname"><span class="pre">events</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/profiler/profiler.html#_KinetoProfile.events"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/profiler/profiler.py#L317"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.profiler._KinetoProfile.events" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">events</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/profiler/profiler.html#_KinetoProfile.events"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/profiler/profiler.py#L317"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.profiler._KinetoProfile.events" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns the list of unaggregated profiler events,
 to be used in the trace callback or after the profiling is finished</p>
 </dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.profiler._KinetoProfile.export_chrome_trace">
-<span class="sig-name descname"><span class="pre">export_chrome_trace</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">path</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/profiler/profiler.html#_KinetoProfile.export_chrome_trace"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/profiler/profiler.py#L243"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.profiler._KinetoProfile.export_chrome_trace" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">export_chrome_trace</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">path</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/profiler/profiler.html#_KinetoProfile.export_chrome_trace"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/profiler/profiler.py#L243"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.profiler._KinetoProfile.export_chrome_trace" title="Permalink to this definition">¶</a></dt>
 <dd><p>Exports the collected trace in Chrome JSON format. If kineto is enabled, only
 last cycle in schedule is exported.</p>
 <dl class="field-list simple">
@@ -699,7 +699,7 @@ <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.profiler._KinetoProfile.export_memory_timeline">
-<span class="sig-name descname"><span class="pre">export_memory_timeline</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">path</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/profiler/profiler.html#_KinetoProfile.export_memory_timeline"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/profiler/profiler.py#L376"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.profiler._KinetoProfile.export_memory_timeline" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">export_memory_timeline</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">path</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/profiler/profiler.html#_KinetoProfile.export_memory_timeline"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/profiler/profiler.py#L376"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.profiler._KinetoProfile.export_memory_timeline" title="Permalink to this definition">¶</a></dt>
 <dd><p>Export memory event information from the profiler collected
 tree for a given device, and export a timeline plot. There are 3
 exportable files using <code class="docutils literal notranslate"><span class="pre">export_memory_timeline</span></code>, each controlled by the
@@ -724,7 +724,7 @@ <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.profiler._KinetoProfile.export_stacks">
-<span class="sig-name descname"><span class="pre">export_stacks</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">path</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">metric</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'self_cpu_time_total'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/profiler/profiler.html#_KinetoProfile.export_stacks"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/profiler/profiler.py#L261"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.profiler._KinetoProfile.export_stacks" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">export_stacks</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">path</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">metric</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'self_cpu_time_total'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/profiler/profiler.html#_KinetoProfile.export_stacks"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/profiler/profiler.py#L261"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.profiler._KinetoProfile.export_stacks" title="Permalink to this definition">¶</a></dt>
 <dd><p>Save stack traces to a file</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -738,7 +738,7 @@ <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.profiler._KinetoProfile.key_averages">
-<span class="sig-name descname"><span class="pre">key_averages</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">group_by_input_shape</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group_by_stack_n</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/profiler/profiler.html#_KinetoProfile.key_averages"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/profiler/profiler.py#L304"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.profiler._KinetoProfile.key_averages" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">key_averages</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">group_by_input_shape</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group_by_stack_n</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/profiler/profiler.html#_KinetoProfile.key_averages"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/profiler/profiler.py#L304"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.profiler._KinetoProfile.key_averages" title="Permalink to this definition">¶</a></dt>
 <dd><p>Averages events, grouping them by operator name and (optionally) input shapes and
 stack.</p>
 <div class="admonition note">
@@ -752,7 +752,7 @@ <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.profiler._KinetoProfile.preset_metadata_json">
-<span class="sig-name descname"><span class="pre">preset_metadata_json</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">key</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/profiler/profiler.html#_KinetoProfile.preset_metadata_json"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/profiler/profiler.py#L340"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.profiler._KinetoProfile.preset_metadata_json" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">preset_metadata_json</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">key</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/profiler/profiler.html#_KinetoProfile.preset_metadata_json"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/profiler/profiler.py#L340"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.profiler._KinetoProfile.preset_metadata_json" title="Permalink to this definition">¶</a></dt>
 <dd><p>Preset a user defined metadata when the profiler is not started
 and added into the trace file later.
 Metadata is in the format of a string key and a valid json value</p>
@@ -762,7 +762,7 @@ <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.profiler._KinetoProfile.toggle_collection_dynamic">
-<span class="sig-name descname"><span class="pre">toggle_collection_dynamic</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">enable</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">activities</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/profiler/profiler.html#_KinetoProfile.toggle_collection_dynamic"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/profiler/profiler.py#L271"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.profiler._KinetoProfile.toggle_collection_dynamic" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">toggle_collection_dynamic</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">enable</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">activities</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/profiler/profiler.html#_KinetoProfile.toggle_collection_dynamic"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/profiler/profiler.py#L271"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.profiler._KinetoProfile.toggle_collection_dynamic" title="Permalink to this definition">¶</a></dt>
 <dd><p>Toggle collection of activities on/off at any point of collection. Currently supports toggling Torch Ops
 (CPU) and CUDA activity supported in Kineto</p>
 <dl class="field-list simple">
@@ -795,7 +795,7 @@ <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.profiler.profile">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.profiler.</span></span><span class="sig-name descname"><span class="pre">profile</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">activities</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">schedule</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">on_trace_ready</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">record_shapes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">profile_memory</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">with_stack</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">with_flops</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">with_modules</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">experimental_config</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">execution_trace_observer</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">acc_events</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_cuda</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">custom_trace_id_callback</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/profiler/profiler.html#profile"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/profiler/profiler.py#L533"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.profiler.profile" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.profiler.</span></span><span class="sig-name descname"><span class="pre">profile</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">activities</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">schedule</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">on_trace_ready</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">record_shapes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">profile_memory</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">with_stack</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">with_flops</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">with_modules</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">experimental_config</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">execution_trace_observer</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">acc_events</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_cuda</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">custom_trace_id_callback</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/profiler/profiler.html#profile"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/profiler/profiler.py#L533"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.profiler.profile" title="Permalink to this definition">¶</a></dt>
 <dd><p>Profiler context manager.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -927,19 +927,19 @@ <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to
 Note: One can also pass any object satisfying the _ITraceObserver interface.</p>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.profiler.profile.get_trace_id">
-<span class="sig-name descname"><span class="pre">get_trace_id</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/profiler/profiler.html#profile.get_trace_id"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/profiler/profiler.py#L843"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.profiler.profile.get_trace_id" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">get_trace_id</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/profiler/profiler.html#profile.get_trace_id"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/profiler/profiler.py#L843"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.profiler.profile.get_trace_id" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns the current trace ID.</p>
 </dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.profiler.profile.set_custom_trace_id_callback">
-<span class="sig-name descname"><span class="pre">set_custom_trace_id_callback</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">callback</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/profiler/profiler.html#profile.set_custom_trace_id_callback"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/profiler/profiler.py#L837"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.profiler.profile.set_custom_trace_id_callback" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">set_custom_trace_id_callback</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">callback</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/profiler/profiler.html#profile.set_custom_trace_id_callback"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/profiler/profiler.py#L837"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.profiler.profile.set_custom_trace_id_callback" title="Permalink to this definition">¶</a></dt>
 <dd><p>Sets a callback to be called when a new trace ID is generated.</p>
 </dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.profiler.profile.step">
-<span class="sig-name descname"><span class="pre">step</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/profiler/profiler.html#profile.step"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/profiler/profiler.py#L815"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.profiler.profile.step" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">step</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/profiler/profiler.html#profile.step"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/profiler/profiler.py#L815"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.profiler.profile.step" title="Permalink to this definition">¶</a></dt>
 <dd><p>Signals the profiler that the next profiling step has started.</p>
 </dd></dl>
 
@@ -947,7 +947,7 @@ <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.profiler.ProfilerAction">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.profiler.</span></span><span class="sig-name descname"><span class="pre">ProfilerAction</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/profiler/profiler.html#ProfilerAction"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/profiler/profiler.py#L427"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.profiler.ProfilerAction" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.profiler.</span></span><span class="sig-name descname"><span class="pre">ProfilerAction</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/profiler/profiler.html#ProfilerAction"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/profiler/profiler.py#L427"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.profiler.ProfilerAction" title="Permalink to this definition">¶</a></dt>
 <dd><p>Profiler actions that can be taken at the specified intervals</p>
 </dd></dl>
 
@@ -969,7 +969,7 @@ <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.profiler.schedule">
-<span class="sig-prename descclassname"><span class="pre">torch.profiler.</span></span><span class="sig-name descname"><span class="pre">schedule</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">wait</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">warmup</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">active</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">repeat</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">skip_first</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">skip_first_wait</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/profiler/profiler.html#schedule"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/profiler/profiler.py#L438"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.profiler.schedule" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.profiler.</span></span><span class="sig-name descname"><span class="pre">schedule</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">wait</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">warmup</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">active</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">repeat</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">skip_first</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">skip_first_wait</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/profiler/profiler.html#schedule"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/profiler/profiler.py#L438"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.profiler.schedule" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns a callable that can be used as profiler <code class="docutils literal notranslate"><span class="pre">schedule</span></code> argument. The profiler will skip
 the first <code class="docutils literal notranslate"><span class="pre">skip_first</span></code> steps, then wait for <code class="docutils literal notranslate"><span class="pre">wait</span></code> steps, then do the warmup for the next <code class="docutils literal notranslate"><span class="pre">warmup</span></code> steps,
 then do the active recording for the next <code class="docutils literal notranslate"><span class="pre">active</span></code> steps and then repeat the cycle starting with <code class="docutils literal notranslate"><span class="pre">wait</span></code> steps.
@@ -990,7 +990,7 @@ <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.profiler.tensorboard_trace_handler">
-<span class="sig-prename descclassname"><span class="pre">torch.profiler.</span></span><span class="sig-name descname"><span class="pre">tensorboard_trace_handler</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dir_name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">worker_name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_gzip</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/profiler/profiler.html#tensorboard_trace_handler"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/profiler/profiler.py#L502"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.profiler.tensorboard_trace_handler" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.profiler.</span></span><span class="sig-name descname"><span class="pre">tensorboard_trace_handler</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dir_name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">worker_name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_gzip</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/profiler/profiler.html#tensorboard_trace_handler"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/profiler/profiler.py#L502"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.profiler.tensorboard_trace_handler" title="Permalink to this definition">¶</a></dt>
 <dd><p>Outputs tracing files to directory of <code class="docutils literal notranslate"><span class="pre">dir_name</span></code>, then that directory can be
 directly delivered to tensorboard as logdir.
 <code class="docutils literal notranslate"><span class="pre">worker_name</span></code> should be unique for each worker in distributed scenario,
@@ -1004,13 +1004,13 @@ <h2>API Reference<a class="headerlink" href="#api-reference" title="Permalink to
 <h2>Intel Instrumentation and Tracing Technology APIs<a class="headerlink" href="#intel-instrumentation-and-tracing-technology-apis" title="Permalink to this heading">¶</a></h2>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.profiler.itt.is_available">
-<span class="sig-prename descclassname"><span class="pre">torch.profiler.itt.</span></span><span class="sig-name descname"><span class="pre">is_available</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/profiler/itt.html#is_available"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/profiler/itt.py#L30"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.profiler.itt.is_available" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.profiler.itt.</span></span><span class="sig-name descname"><span class="pre">is_available</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/profiler/itt.html#is_available"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/profiler/itt.py#L30"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.profiler.itt.is_available" title="Permalink to this definition">¶</a></dt>
 <dd><p>Check if ITT feature is available or not</p>
 </dd></dl>
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.profiler.itt.mark">
-<span class="sig-prename descclassname"><span class="pre">torch.profiler.itt.</span></span><span class="sig-name descname"><span class="pre">mark</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">msg</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/profiler/itt.html#mark"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/profiler/itt.py#L56"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.profiler.itt.mark" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.profiler.itt.</span></span><span class="sig-name descname"><span class="pre">mark</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">msg</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/profiler/itt.html#mark"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/profiler/itt.py#L56"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.profiler.itt.mark" title="Permalink to this definition">¶</a></dt>
 <dd><p>Describe an instantaneous event that occurred at some point.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -1021,7 +1021,7 @@ <h2>Intel Instrumentation and Tracing Technology APIs<a class="headerlink" href=
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.profiler.itt.range_push">
-<span class="sig-prename descclassname"><span class="pre">torch.profiler.itt.</span></span><span class="sig-name descname"><span class="pre">range_push</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">msg</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/profiler/itt.html#range_push"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/profiler/itt.py#L37"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.profiler.itt.range_push" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.profiler.itt.</span></span><span class="sig-name descname"><span class="pre">range_push</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">msg</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/profiler/itt.html#range_push"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/profiler/itt.py#L37"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.profiler.itt.range_push" title="Permalink to this definition">¶</a></dt>
 <dd><p>Pushes a range onto a stack of nested range span.  Returns zero-based
 depth of the range that is started.</p>
 <dl class="field-list simple">
@@ -1033,7 +1033,7 @@ <h2>Intel Instrumentation and Tracing Technology APIs<a class="headerlink" href=
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.profiler.itt.range_pop">
-<span class="sig-prename descclassname"><span class="pre">torch.profiler.itt.</span></span><span class="sig-name descname"><span class="pre">range_pop</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/profiler/itt.html#range_pop"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/profiler/itt.py#L48"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.profiler.itt.range_pop" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.profiler.itt.</span></span><span class="sig-name descname"><span class="pre">range_pop</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/profiler/itt.html#range_pop"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/profiler/itt.py#L48"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.profiler.itt.range_pop" title="Permalink to this definition">¶</a></dt>
 <dd><p>Pops a range off of a stack of nested range spans. Returns the
 zero-based depth of the range that is ended.</p>
 </dd></dl>
diff --git a/main/quantization-accuracy-debugging.html b/main/quantization-accuracy-debugging.html
index 3fc6f14a92e..3d28a750612 100644
--- a/main/quantization-accuracy-debugging.html
+++ b/main/quantization-accuracy-debugging.html
@@ -300,7 +300,7 @@
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     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/quantization-backend-configuration.html b/main/quantization-backend-configuration.html
index 24c8df7b40e..00de31fea00 100644
--- a/main/quantization-backend-configuration.html
+++ b/main/quantization-backend-configuration.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -631,7 +631,7 @@ <h2>Default values for native configurations<a class="headerlink" href="#default
   &#39;observation_type&#39;: ObservationType.OUTPUT_SHARE_OBSERVER_WITH_INPUT,
 },
 {
-  &#39;pattern&#39;: &lt;built-in method adaptive_avg_pool1d of type object at 0x7f5c26368780&gt;,
+  &#39;pattern&#39;: &lt;built-in method adaptive_avg_pool1d of type object at 0x7fb912d9b7c0&gt;,
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@@ -651,7 +651,7 @@ <h2>Default values for native configurations<a class="headerlink" href="#default
   &#39;observation_type&#39;: ObservationType.OUTPUT_SHARE_OBSERVER_WITH_INPUT,
 },
 {
-  &#39;pattern&#39;: &lt;function adaptive_avg_pool2d at 0x7f5bfeb17280&gt;,
+  &#39;pattern&#39;: &lt;function adaptive_avg_pool2d at 0x7fb8eb54a160&gt;,
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     {
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@@ -671,7 +671,7 @@ <h2>Default values for native configurations<a class="headerlink" href="#default
   &#39;observation_type&#39;: ObservationType.OUTPUT_SHARE_OBSERVER_WITH_INPUT,
 },
 {
-  &#39;pattern&#39;: &lt;function adaptive_avg_pool3d at 0x7f5bfeb17310&gt;,
+  &#39;pattern&#39;: &lt;function adaptive_avg_pool3d at 0x7fb8eb54a1f0&gt;,
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@@ -696,7 +696,7 @@ <h2>Default values for native configurations<a class="headerlink" href="#default
   &#39;observation_type&#39;: ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
 },
 {
-  &#39;pattern&#39;: &lt;built-in method add of type object at 0x7f5c26368780&gt;,
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@@ -721,7 +721,7 @@ <h2>Default values for native configurations<a class="headerlink" href="#default
   &#39;observation_type&#39;: ObservationType.OUTPUT_SHARE_OBSERVER_WITH_INPUT,
 },
 {
-  &#39;pattern&#39;: &lt;built-in method avg_pool1d of type object at 0x7f5c26368780&gt;,
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@@ -782,7 +782,7 @@ <h2>Default values for native configurations<a class="headerlink" href="#default
   ],
   &#39;observation_type&#39;: ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
   &#39;fused_module&#39;: &lt;class &#39;torch.ao.nn.intrinsic.modules.fused.ConvBn1d&#39;&gt;,
-  &#39;fuser_method&#39;: &lt;function fuse_conv_bn at 0x7f5bfdfef820&gt;,
+  &#39;fuser_method&#39;: &lt;function fuse_conv_bn at 0x7fb8eaa1e790&gt;,
 },
 {
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@@ -797,7 +797,7 @@ <h2>Default values for native configurations<a class="headerlink" href="#default
   &#39;observation_type&#39;: ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
   &#39;root_module&#39;: &lt;class &#39;torch.nn.modules.conv.ConvTranspose1d&#39;&gt;,
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-  &#39;fuser_method&#39;: &lt;function fuse_convtranspose_bn at 0x7f5bfdfef9d0&gt;,
+  &#39;fuser_method&#39;: &lt;function fuse_convtranspose_bn at 0x7fb8eaa1e940&gt;,
 },
 {
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@@ -825,7 +825,7 @@ <h2>Default values for native configurations<a class="headerlink" href="#default
   ],
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-  &#39;fuser_method&#39;: &lt;function fuse_linear_bn at 0x7f5bfdfef940&gt;,
+  &#39;fuser_method&#39;: &lt;function fuse_linear_bn at 0x7fb8eaa1e8b0&gt;,
 },
 {
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@@ -839,7 +839,7 @@ <h2>Default values for native configurations<a class="headerlink" href="#default
   ],
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-  &#39;fuser_method&#39;: &lt;function fuse_conv_bn at 0x7f5bfdfef820&gt;,
+  &#39;fuser_method&#39;: &lt;function fuse_conv_bn at 0x7fb8eaa1e790&gt;,
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 {
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@@ -854,7 +854,7 @@ <h2>Default values for native configurations<a class="headerlink" href="#default
   &#39;observation_type&#39;: ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
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-  &#39;fuser_method&#39;: &lt;function fuse_convtranspose_bn at 0x7f5bfdfef9d0&gt;,
+  &#39;fuser_method&#39;: &lt;function fuse_convtranspose_bn at 0x7fb8eaa1e940&gt;,
 },
 {
   &#39;pattern&#39;: &lt;class &#39;torch.nn.modules.batchnorm.BatchNorm2d&#39;&gt;,
@@ -878,7 +878,7 @@ <h2>Default values for native configurations<a class="headerlink" href="#default
   ],
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   &#39;fused_module&#39;: &lt;class &#39;torch.ao.nn.intrinsic.modules.fused.ConvBn3d&#39;&gt;,
-  &#39;fuser_method&#39;: &lt;function fuse_conv_bn at 0x7f5bfdfef820&gt;,
+  &#39;fuser_method&#39;: &lt;function fuse_conv_bn at 0x7fb8eaa1e790&gt;,
 },
 {
   &#39;pattern&#39;: (&lt;class &#39;torch.nn.modules.conv.ConvTranspose3d&#39;&gt;, &lt;class &#39;torch.nn.modules.batchnorm.BatchNorm3d&#39;&gt;),
@@ -893,7 +893,7 @@ <h2>Default values for native configurations<a class="headerlink" href="#default
   &#39;observation_type&#39;: ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
   &#39;root_module&#39;: &lt;class &#39;torch.nn.modules.conv.ConvTranspose3d&#39;&gt;,
   &#39;reference_quantized_module_for_root&#39;: &lt;class &#39;torch.ao.nn.quantized.reference.modules.conv.ConvTranspose3d&#39;&gt;,
-  &#39;fuser_method&#39;: &lt;function fuse_convtranspose_bn at 0x7f5bfdfef9d0&gt;,
+  &#39;fuser_method&#39;: &lt;function fuse_convtranspose_bn at 0x7fb8eaa1e940&gt;,
 },
 {
   &#39;pattern&#39;: &lt;class &#39;torch.nn.modules.batchnorm.BatchNorm3d&#39;&gt;,
@@ -926,7 +926,7 @@ <h2>Default values for native configurations<a class="headerlink" href="#default
   &#39;observation_type&#39;: ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
 },
 {
-  &#39;pattern&#39;: &lt;built-in method cat of type object at 0x7f5c26368780&gt;,
+  &#39;pattern&#39;: &lt;built-in method cat of type object at 0x7fb912d9b7c0&gt;,
   &#39;dtype_configs&#39;: [
     {
       &#39;input_dtype&#39;: DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
@@ -936,7 +936,7 @@ <h2>Default values for native configurations<a class="headerlink" href="#default
   &#39;observation_type&#39;: ObservationType.OUTPUT_SHARE_OBSERVER_WITH_INPUT,
 },
 {
-  &#39;pattern&#39;: &lt;built-in method clamp of type object at 0x7f5c26368780&gt;,
+  &#39;pattern&#39;: &lt;built-in method clamp of type object at 0x7fb912d9b7c0&gt;,
   &#39;dtype_configs&#39;: [
     {
       &#39;input_dtype&#39;: DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
@@ -995,7 +995,7 @@ <h2>Default values for native configurations<a class="headerlink" href="#default
   &#39;reference_quantized_module_for_root&#39;: &lt;class &#39;torch.ao.nn.quantized.reference.modules.conv.Conv1d&#39;&gt;,
 },
 {
-  &#39;pattern&#39;: &lt;built-in method conv1d of type object at 0x7f5c26368780&gt;,
+  &#39;pattern&#39;: &lt;built-in method conv1d of type object at 0x7fb912d9b7c0&gt;,
   &#39;dtype_configs&#39;: [
     {
       &#39;input_dtype&#39;: DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
@@ -1037,7 +1037,7 @@ <h2>Default values for native configurations<a class="headerlink" href="#default
   &#39;reference_quantized_module_for_root&#39;: &lt;class &#39;torch.ao.nn.quantized.reference.modules.conv.Conv2d&#39;&gt;,
 },
 {
-  &#39;pattern&#39;: &lt;built-in method conv2d of type object at 0x7f5c26368780&gt;,
+  &#39;pattern&#39;: &lt;built-in method conv2d of type object at 0x7fb912d9b7c0&gt;,
   &#39;dtype_configs&#39;: [
     {
       &#39;input_dtype&#39;: DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
@@ -1079,7 +1079,7 @@ <h2>Default values for native configurations<a class="headerlink" href="#default
   &#39;reference_quantized_module_for_root&#39;: &lt;class &#39;torch.ao.nn.quantized.reference.modules.conv.Conv3d&#39;&gt;,
 },
 {
-  &#39;pattern&#39;: &lt;built-in method conv3d of type object at 0x7f5c26368780&gt;,
+  &#39;pattern&#39;: &lt;built-in method conv3d of type object at 0x7fb912d9b7c0&gt;,
   &#39;dtype_configs&#39;: [
     {
       &#39;input_dtype&#39;: DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
@@ -1349,7 +1349,7 @@ <h2>Default values for native configurations<a class="headerlink" href="#default
   &#39;reference_quantized_module_for_root&#39;: &lt;class &#39;torch.ao.nn.quantized.reference.modules.conv.ConvTranspose1d&#39;&gt;,
 },
 {
-  &#39;pattern&#39;: &lt;built-in method conv_transpose1d of type object at 0x7f5c26368780&gt;,
+  &#39;pattern&#39;: &lt;built-in method conv_transpose1d of type object at 0x7fb912d9b7c0&gt;,
   &#39;dtype_configs&#39;: [
     {
       &#39;input_dtype&#39;: DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
@@ -1376,7 +1376,7 @@ <h2>Default values for native configurations<a class="headerlink" href="#default
   &#39;reference_quantized_module_for_root&#39;: &lt;class &#39;torch.ao.nn.quantized.reference.modules.conv.ConvTranspose2d&#39;&gt;,
 },
 {
-  &#39;pattern&#39;: &lt;built-in method conv_transpose2d of type object at 0x7f5c26368780&gt;,
+  &#39;pattern&#39;: &lt;built-in method conv_transpose2d of type object at 0x7fb912d9b7c0&gt;,
   &#39;dtype_configs&#39;: [
     {
       &#39;input_dtype&#39;: DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
@@ -1403,7 +1403,7 @@ <h2>Default values for native configurations<a class="headerlink" href="#default
   &#39;reference_quantized_module_for_root&#39;: &lt;class &#39;torch.ao.nn.quantized.reference.modules.conv.ConvTranspose3d&#39;&gt;,
 },
 {
-  &#39;pattern&#39;: &lt;built-in method conv_transpose3d of type object at 0x7f5c26368780&gt;,
+  &#39;pattern&#39;: &lt;built-in method conv_transpose3d of type object at 0x7fb912d9b7c0&gt;,
   &#39;dtype_configs&#39;: [
     {
       &#39;input_dtype&#39;: DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
@@ -1446,7 +1446,7 @@ <h2>Default values for native configurations<a class="headerlink" href="#default
   &#39;observation_type&#39;: ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
 },
 {
-  &#39;pattern&#39;: &lt;function dropout at 0x7f5bfeb173a0&gt;,
+  &#39;pattern&#39;: &lt;function dropout at 0x7fb8eb54a280&gt;,
   &#39;dtype_configs&#39;: [
     {
       &#39;input_dtype&#39;: DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
@@ -1466,7 +1466,7 @@ <h2>Default values for native configurations<a class="headerlink" href="#default
   &#39;observation_type&#39;: ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
 },
 {
-  &#39;pattern&#39;: &lt;function elu at 0x7f5bfeb179d0&gt;,
+  &#39;pattern&#39;: &lt;function elu at 0x7fb8eb54a8b0&gt;,
   &#39;dtype_configs&#39;: [
     {
       &#39;input_dtype&#39;: DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
@@ -1550,7 +1550,7 @@ <h2>Default values for native configurations<a class="headerlink" href="#default
   &#39;reference_quantized_module_for_root&#39;: &lt;class &#39;torch.ao.nn.quantized.reference.modules.sparse.EmbeddingBag&#39;&gt;,
 },
 {
-  &#39;pattern&#39;: &lt;built-in method flatten of type object at 0x7f5c26368780&gt;,
+  &#39;pattern&#39;: &lt;built-in method flatten of type object at 0x7fb912d9b7c0&gt;,
   &#39;dtype_configs&#39;: [
     {
       &#39;input_dtype&#39;: DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
@@ -1570,7 +1570,7 @@ <h2>Default values for native configurations<a class="headerlink" href="#default
   &#39;observation_type&#39;: ObservationType.OUTPUT_SHARE_OBSERVER_WITH_INPUT,
 },
 {
-  &#39;pattern&#39;: &lt;function group_norm at 0x7f5bfeb1c940&gt;,
+  &#39;pattern&#39;: &lt;function group_norm at 0x7fb8eb54f820&gt;,
   &#39;dtype_configs&#39;: [
     {
       &#39;input_dtype&#39;: DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
@@ -1635,7 +1635,7 @@ <h2>Default values for native configurations<a class="headerlink" href="#default
   &#39;observation_type&#39;: ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
 },
 {
-  &#39;pattern&#39;: &lt;function hardsigmoid at 0x7f5bfeb1c1f0&gt;,
+  &#39;pattern&#39;: &lt;function hardsigmoid at 0x7fb8eb54f0d0&gt;,
   &#39;dtype_configs&#39;: [
     {
       &#39;input_dtype&#39;: DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=0, quant_max_upper_bound=255, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=0.00390625, zero_point_exact_match=0),
@@ -1675,7 +1675,7 @@ <h2>Default values for native configurations<a class="headerlink" href="#default
   &#39;observation_type&#39;: ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
 },
 {
-  &#39;pattern&#39;: &lt;function hardswish at 0x7f5bfeb1c3a0&gt;,
+  &#39;pattern&#39;: &lt;function hardswish at 0x7fb8eb54f280&gt;,
   &#39;dtype_configs&#39;: [
     {
       &#39;input_dtype&#39;: DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
@@ -1695,7 +1695,7 @@ <h2>Default values for native configurations<a class="headerlink" href="#default
   &#39;observation_type&#39;: ObservationType.OUTPUT_SHARE_OBSERVER_WITH_INPUT,
 },
 {
-  &#39;pattern&#39;: &lt;function hardtanh at 0x7f5bfeb178b0&gt;,
+  &#39;pattern&#39;: &lt;function hardtanh at 0x7fb8eb54a790&gt;,
   &#39;dtype_configs&#39;: [
     {
       &#39;input_dtype&#39;: DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
@@ -1725,7 +1725,7 @@ <h2>Default values for native configurations<a class="headerlink" href="#default
   &#39;observation_type&#39;: ObservationType.OUTPUT_SHARE_OBSERVER_WITH_INPUT,
 },
 {
-  &#39;pattern&#39;: &lt;function instance_norm at 0x7f5bfeb1c790&gt;,
+  &#39;pattern&#39;: &lt;function instance_norm at 0x7fb8eb54f670&gt;,
   &#39;dtype_configs&#39;: [
     {
       &#39;input_dtype&#39;: DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
@@ -1766,7 +1766,7 @@ <h2>Default values for native configurations<a class="headerlink" href="#default
   &#39;observation_type&#39;: ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
 },
 {
-  &#39;pattern&#39;: &lt;function interpolate at 0x7f5bfeaad8b0&gt;,
+  &#39;pattern&#39;: &lt;function interpolate at 0x7fb8eb45e790&gt;,
   &#39;dtype_configs&#39;: [
     {
       &#39;input_dtype&#39;: DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
@@ -1788,7 +1788,7 @@ <h2>Default values for native configurations<a class="headerlink" href="#default
   &#39;observation_type&#39;: ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
 },
 {
-  &#39;pattern&#39;: &lt;function layer_norm at 0x7f5bfeb1c820&gt;,
+  &#39;pattern&#39;: &lt;function layer_norm at 0x7fb8eb54f700&gt;,
   &#39;dtype_configs&#39;: [
     {
       &#39;input_dtype&#39;: DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
@@ -1811,7 +1811,7 @@ <h2>Default values for native configurations<a class="headerlink" href="#default
   &#39;observation_type&#39;: ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
 },
 {
-  &#39;pattern&#39;: &lt;function leaky_relu at 0x7f5bfeb17b80&gt;,
+  &#39;pattern&#39;: &lt;function leaky_relu at 0x7fb8eb54aa60&gt;,
   &#39;dtype_configs&#39;: [
     {
       &#39;input_dtype&#39;: DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
@@ -2063,7 +2063,7 @@ <h2>Default values for native configurations<a class="headerlink" href="#default
   &#39;reference_quantized_module_for_root&#39;: &lt;class &#39;torch.ao.nn.quantized.reference.modules.rnn.LSTMCell&#39;&gt;,
 },
 {
-  &#39;pattern&#39;: &lt;built-in method matmul of type object at 0x7f5c26368780&gt;,
+  &#39;pattern&#39;: &lt;built-in method matmul of type object at 0x7fb912d9b7c0&gt;,
   &#39;dtype_configs&#39;: [
     {
       &#39;input_dtype&#39;: DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
@@ -2133,7 +2133,7 @@ <h2>Default values for native configurations<a class="headerlink" href="#default
   &#39;observation_type&#39;: ObservationType.OUTPUT_SHARE_OBSERVER_WITH_INPUT,
 },
 {
-  &#39;pattern&#39;: &lt;built-in method mean of type object at 0x7f5c26368780&gt;,
+  &#39;pattern&#39;: &lt;built-in method mean of type object at 0x7fb912d9b7c0&gt;,
   &#39;dtype_configs&#39;: [
     {
       &#39;input_dtype&#39;: DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
@@ -2168,7 +2168,7 @@ <h2>Default values for native configurations<a class="headerlink" href="#default
   &#39;observation_type&#39;: ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
 },
 {
-  &#39;pattern&#39;: &lt;built-in method mul of type object at 0x7f5c26368780&gt;,
+  &#39;pattern&#39;: &lt;built-in method mul of type object at 0x7fb912d9b7c0&gt;,
   &#39;dtype_configs&#39;: [
     {
       &#39;input_dtype&#39;: DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
@@ -2183,7 +2183,7 @@ <h2>Default values for native configurations<a class="headerlink" href="#default
   &#39;observation_type&#39;: ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
 },
 {
-  &#39;pattern&#39;: &lt;built-in method narrow of type object at 0x7f5c26368780&gt;,
+  &#39;pattern&#39;: &lt;built-in method narrow of type object at 0x7fb912d9b7c0&gt;,
   &#39;dtype_configs&#39;: [
     {
       &#39;input_dtype&#39;: DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
@@ -2213,7 +2213,7 @@ <h2>Default values for native configurations<a class="headerlink" href="#default
   &#39;observation_type&#39;: ObservationType.OUTPUT_SHARE_OBSERVER_WITH_INPUT,
 },
 {
-  &#39;pattern&#39;: &lt;built-in method pixel_shuffle of type object at 0x7f5c26368780&gt;,
+  &#39;pattern&#39;: &lt;built-in method pixel_shuffle of type object at 0x7fb912d9b7c0&gt;,
   &#39;dtype_configs&#39;: [
     {
       &#39;input_dtype&#39;: DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
@@ -2233,7 +2233,7 @@ <h2>Default values for native configurations<a class="headerlink" href="#default
   &#39;observation_type&#39;: ObservationType.OUTPUT_SHARE_OBSERVER_WITH_INPUT,
 },
 {
-  &#39;pattern&#39;: &lt;built-in method pixel_unshuffle of type object at 0x7f5c26368780&gt;,
+  &#39;pattern&#39;: &lt;built-in method pixel_unshuffle of type object at 0x7fb912d9b7c0&gt;,
   &#39;dtype_configs&#39;: [
     {
       &#39;input_dtype&#39;: DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
@@ -2264,10 +2264,10 @@ <h2>Default values for native configurations<a class="headerlink" href="#default
   ],
   &#39;observation_type&#39;: ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
   &#39;fused_module&#39;: &lt;class &#39;torch.ao.nn.intrinsic.modules.fused.ConvReLU1d&#39;&gt;,
-  &#39;fuser_method&#39;: &lt;function _sequential_wrapper2.&lt;locals&gt;.fuser_method at 0x7f5c2d69e310&gt;,
+  &#39;fuser_method&#39;: &lt;function _sequential_wrapper2.&lt;locals&gt;.fuser_method at 0x7fb91a0d1310&gt;,
 },
 {
-  &#39;pattern&#39;: (&lt;class &#39;torch.nn.modules.conv.Conv1d&#39;&gt;, &lt;function relu at 0x7f5bfeb17790&gt;),
+  &#39;pattern&#39;: (&lt;class &#39;torch.nn.modules.conv.Conv1d&#39;&gt;, &lt;function relu at 0x7fb8eb54a670&gt;),
   &#39;dtype_configs&#39;: [
     {
       &#39;input_dtype&#39;: DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
@@ -2278,10 +2278,10 @@ <h2>Default values for native configurations<a class="headerlink" href="#default
   ],
   &#39;observation_type&#39;: ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
   &#39;fused_module&#39;: &lt;class &#39;torch.ao.nn.intrinsic.modules.fused.ConvReLU1d&#39;&gt;,
-  &#39;fuser_method&#39;: &lt;function _sequential_wrapper2.&lt;locals&gt;.fuser_method at 0x7f5bfce47940&gt;,
+  &#39;fuser_method&#39;: &lt;function _sequential_wrapper2.&lt;locals&gt;.fuser_method at 0x7fb8e9876a60&gt;,
 },
 {
-  &#39;pattern&#39;: (&lt;built-in method conv1d of type object at 0x7f5c26368780&gt;, &lt;class &#39;torch.nn.modules.activation.ReLU&#39;&gt;),
+  &#39;pattern&#39;: (&lt;built-in method conv1d of type object at 0x7fb912d9b7c0&gt;, &lt;class &#39;torch.nn.modules.activation.ReLU&#39;&gt;),
   &#39;dtype_configs&#39;: [
     {
       &#39;input_dtype&#39;: DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
@@ -2293,7 +2293,7 @@ <h2>Default values for native configurations<a class="headerlink" href="#default
   &#39;observation_type&#39;: ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
 },
 {
-  &#39;pattern&#39;: (&lt;built-in method conv1d of type object at 0x7f5c26368780&gt;, &lt;function relu at 0x7f5bfeb17790&gt;),
+  &#39;pattern&#39;: (&lt;built-in method conv1d of type object at 0x7fb912d9b7c0&gt;, &lt;function relu at 0x7fb8eb54a670&gt;),
   &#39;dtype_configs&#39;: [
     {
       &#39;input_dtype&#39;: DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
@@ -2316,10 +2316,10 @@ <h2>Default values for native configurations<a class="headerlink" href="#default
   ],
   &#39;observation_type&#39;: ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
   &#39;fused_module&#39;: &lt;class &#39;torch.ao.nn.intrinsic.modules.fused.ConvBnReLU1d&#39;&gt;,
-  &#39;fuser_method&#39;: &lt;function fuse_conv_bn_relu at 0x7f5bfdfef8b0&gt;,
+  &#39;fuser_method&#39;: &lt;function fuse_conv_bn_relu at 0x7fb8eaa1e820&gt;,
 },
 {
-  &#39;pattern&#39;: (&lt;class &#39;torch.nn.modules.conv.Conv1d&#39;&gt;, &lt;class &#39;torch.nn.modules.batchnorm.BatchNorm1d&#39;&gt;, &lt;function relu at 0x7f5bfeb17790&gt;),
+  &#39;pattern&#39;: (&lt;class &#39;torch.nn.modules.conv.Conv1d&#39;&gt;, &lt;class &#39;torch.nn.modules.batchnorm.BatchNorm1d&#39;&gt;, &lt;function relu at 0x7fb8eb54a670&gt;),
   &#39;dtype_configs&#39;: [
     {
       &#39;input_dtype&#39;: DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
@@ -2331,7 +2331,7 @@ <h2>Default values for native configurations<a class="headerlink" href="#default
   &#39;observation_type&#39;: ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
   &#39;root_module&#39;: &lt;class &#39;torch.nn.modules.conv.Conv1d&#39;&gt;,
   &#39;fused_module&#39;: &lt;class &#39;torch.ao.nn.intrinsic.modules.fused.ConvBnReLU1d&#39;&gt;,
-  &#39;fuser_method&#39;: &lt;function fuse_conv_bn_relu at 0x7f5bfdfef8b0&gt;,
+  &#39;fuser_method&#39;: &lt;function fuse_conv_bn_relu at 0x7fb8eaa1e820&gt;,
 },
 {
   &#39;pattern&#39;: (&lt;class &#39;torch.nn.modules.conv.Conv2d&#39;&gt;, &lt;class &#39;torch.nn.modules.activation.ReLU&#39;&gt;),
@@ -2345,10 +2345,10 @@ <h2>Default values for native configurations<a class="headerlink" href="#default
   ],
   &#39;observation_type&#39;: ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
   &#39;fused_module&#39;: &lt;class &#39;torch.ao.nn.intrinsic.modules.fused.ConvReLU2d&#39;&gt;,
-  &#39;fuser_method&#39;: &lt;function _sequential_wrapper2.&lt;locals&gt;.fuser_method at 0x7f5bfce479d0&gt;,
+  &#39;fuser_method&#39;: &lt;function _sequential_wrapper2.&lt;locals&gt;.fuser_method at 0x7fb8e9876af0&gt;,
 },
 {
-  &#39;pattern&#39;: (&lt;class &#39;torch.nn.modules.conv.Conv2d&#39;&gt;, &lt;function relu at 0x7f5bfeb17790&gt;),
+  &#39;pattern&#39;: (&lt;class &#39;torch.nn.modules.conv.Conv2d&#39;&gt;, &lt;function relu at 0x7fb8eb54a670&gt;),
   &#39;dtype_configs&#39;: [
     {
       &#39;input_dtype&#39;: DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
@@ -2359,10 +2359,10 @@ <h2>Default values for native configurations<a class="headerlink" href="#default
   ],
   &#39;observation_type&#39;: ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
   &#39;fused_module&#39;: &lt;class &#39;torch.ao.nn.intrinsic.modules.fused.ConvReLU2d&#39;&gt;,
-  &#39;fuser_method&#39;: &lt;function _sequential_wrapper2.&lt;locals&gt;.fuser_method at 0x7f5bfce47a60&gt;,
+  &#39;fuser_method&#39;: &lt;function _sequential_wrapper2.&lt;locals&gt;.fuser_method at 0x7fb8e9876b80&gt;,
 },
 {
-  &#39;pattern&#39;: (&lt;built-in method conv2d of type object at 0x7f5c26368780&gt;, &lt;class &#39;torch.nn.modules.activation.ReLU&#39;&gt;),
+  &#39;pattern&#39;: (&lt;built-in method conv2d of type object at 0x7fb912d9b7c0&gt;, &lt;class &#39;torch.nn.modules.activation.ReLU&#39;&gt;),
   &#39;dtype_configs&#39;: [
     {
       &#39;input_dtype&#39;: DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
@@ -2374,7 +2374,7 @@ <h2>Default values for native configurations<a class="headerlink" href="#default
   &#39;observation_type&#39;: ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
 },
 {
-  &#39;pattern&#39;: (&lt;built-in method conv2d of type object at 0x7f5c26368780&gt;, &lt;function relu at 0x7f5bfeb17790&gt;),
+  &#39;pattern&#39;: (&lt;built-in method conv2d of type object at 0x7fb912d9b7c0&gt;, &lt;function relu at 0x7fb8eb54a670&gt;),
   &#39;dtype_configs&#39;: [
     {
       &#39;input_dtype&#39;: DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
@@ -2397,10 +2397,10 @@ <h2>Default values for native configurations<a class="headerlink" href="#default
   ],
   &#39;observation_type&#39;: ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
   &#39;fused_module&#39;: &lt;class &#39;torch.ao.nn.intrinsic.modules.fused.ConvBnReLU2d&#39;&gt;,
-  &#39;fuser_method&#39;: &lt;function fuse_conv_bn_relu at 0x7f5bfdfef8b0&gt;,
+  &#39;fuser_method&#39;: &lt;function fuse_conv_bn_relu at 0x7fb8eaa1e820&gt;,
 },
 {
-  &#39;pattern&#39;: (&lt;class &#39;torch.nn.modules.conv.Conv2d&#39;&gt;, &lt;class &#39;torch.nn.modules.batchnorm.BatchNorm2d&#39;&gt;, &lt;function relu at 0x7f5bfeb17790&gt;),
+  &#39;pattern&#39;: (&lt;class &#39;torch.nn.modules.conv.Conv2d&#39;&gt;, &lt;class &#39;torch.nn.modules.batchnorm.BatchNorm2d&#39;&gt;, &lt;function relu at 0x7fb8eb54a670&gt;),
   &#39;dtype_configs&#39;: [
     {
       &#39;input_dtype&#39;: DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
@@ -2412,7 +2412,7 @@ <h2>Default values for native configurations<a class="headerlink" href="#default
   &#39;observation_type&#39;: ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
   &#39;root_module&#39;: &lt;class &#39;torch.nn.modules.conv.Conv2d&#39;&gt;,
   &#39;fused_module&#39;: &lt;class &#39;torch.ao.nn.intrinsic.modules.fused.ConvBnReLU2d&#39;&gt;,
-  &#39;fuser_method&#39;: &lt;function fuse_conv_bn_relu at 0x7f5bfdfef8b0&gt;,
+  &#39;fuser_method&#39;: &lt;function fuse_conv_bn_relu at 0x7fb8eaa1e820&gt;,
 },
 {
   &#39;pattern&#39;: (&lt;class &#39;torch.nn.modules.conv.Conv3d&#39;&gt;, &lt;class &#39;torch.nn.modules.activation.ReLU&#39;&gt;),
@@ -2426,10 +2426,10 @@ <h2>Default values for native configurations<a class="headerlink" href="#default
   ],
   &#39;observation_type&#39;: ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
   &#39;fused_module&#39;: &lt;class &#39;torch.ao.nn.intrinsic.modules.fused.ConvReLU3d&#39;&gt;,
-  &#39;fuser_method&#39;: &lt;function _sequential_wrapper2.&lt;locals&gt;.fuser_method at 0x7f5bfce47af0&gt;,
+  &#39;fuser_method&#39;: &lt;function _sequential_wrapper2.&lt;locals&gt;.fuser_method at 0x7fb8e9876c10&gt;,
 },
 {
-  &#39;pattern&#39;: (&lt;class &#39;torch.nn.modules.conv.Conv3d&#39;&gt;, &lt;function relu at 0x7f5bfeb17790&gt;),
+  &#39;pattern&#39;: (&lt;class &#39;torch.nn.modules.conv.Conv3d&#39;&gt;, &lt;function relu at 0x7fb8eb54a670&gt;),
   &#39;dtype_configs&#39;: [
     {
       &#39;input_dtype&#39;: DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
@@ -2440,10 +2440,10 @@ <h2>Default values for native configurations<a class="headerlink" href="#default
   ],
   &#39;observation_type&#39;: ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
   &#39;fused_module&#39;: &lt;class &#39;torch.ao.nn.intrinsic.modules.fused.ConvReLU3d&#39;&gt;,
-  &#39;fuser_method&#39;: &lt;function _sequential_wrapper2.&lt;locals&gt;.fuser_method at 0x7f5bfce47b80&gt;,
+  &#39;fuser_method&#39;: &lt;function _sequential_wrapper2.&lt;locals&gt;.fuser_method at 0x7fb8e9876ca0&gt;,
 },
 {
-  &#39;pattern&#39;: (&lt;built-in method conv3d of type object at 0x7f5c26368780&gt;, &lt;class &#39;torch.nn.modules.activation.ReLU&#39;&gt;),
+  &#39;pattern&#39;: (&lt;built-in method conv3d of type object at 0x7fb912d9b7c0&gt;, &lt;class &#39;torch.nn.modules.activation.ReLU&#39;&gt;),
   &#39;dtype_configs&#39;: [
     {
       &#39;input_dtype&#39;: DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
@@ -2455,7 +2455,7 @@ <h2>Default values for native configurations<a class="headerlink" href="#default
   &#39;observation_type&#39;: ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
 },
 {
-  &#39;pattern&#39;: (&lt;built-in method conv3d of type object at 0x7f5c26368780&gt;, &lt;function relu at 0x7f5bfeb17790&gt;),
+  &#39;pattern&#39;: (&lt;built-in method conv3d of type object at 0x7fb912d9b7c0&gt;, &lt;function relu at 0x7fb8eb54a670&gt;),
   &#39;dtype_configs&#39;: [
     {
       &#39;input_dtype&#39;: DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
@@ -2478,10 +2478,10 @@ <h2>Default values for native configurations<a class="headerlink" href="#default
   ],
   &#39;observation_type&#39;: ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
   &#39;fused_module&#39;: &lt;class &#39;torch.ao.nn.intrinsic.modules.fused.ConvBnReLU3d&#39;&gt;,
-  &#39;fuser_method&#39;: &lt;function fuse_conv_bn_relu at 0x7f5bfdfef8b0&gt;,
+  &#39;fuser_method&#39;: &lt;function fuse_conv_bn_relu at 0x7fb8eaa1e820&gt;,
 },
 {
-  &#39;pattern&#39;: (&lt;class &#39;torch.nn.modules.conv.Conv3d&#39;&gt;, &lt;class &#39;torch.nn.modules.batchnorm.BatchNorm3d&#39;&gt;, &lt;function relu at 0x7f5bfeb17790&gt;),
+  &#39;pattern&#39;: (&lt;class &#39;torch.nn.modules.conv.Conv3d&#39;&gt;, &lt;class &#39;torch.nn.modules.batchnorm.BatchNorm3d&#39;&gt;, &lt;function relu at 0x7fb8eb54a670&gt;),
   &#39;dtype_configs&#39;: [
     {
       &#39;input_dtype&#39;: DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
@@ -2493,7 +2493,7 @@ <h2>Default values for native configurations<a class="headerlink" href="#default
   &#39;observation_type&#39;: ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
   &#39;root_module&#39;: &lt;class &#39;torch.nn.modules.conv.Conv3d&#39;&gt;,
   &#39;fused_module&#39;: &lt;class &#39;torch.ao.nn.intrinsic.modules.fused.ConvBnReLU3d&#39;&gt;,
-  &#39;fuser_method&#39;: &lt;function fuse_conv_bn_relu at 0x7f5bfdfef8b0&gt;,
+  &#39;fuser_method&#39;: &lt;function fuse_conv_bn_relu at 0x7fb8eaa1e820&gt;,
 },
 {
   &#39;pattern&#39;: (&lt;class &#39;torch.nn.modules.linear.Linear&#39;&gt;, &lt;class &#39;torch.nn.modules.activation.ReLU&#39;&gt;),
@@ -2521,10 +2521,10 @@ <h2>Default values for native configurations<a class="headerlink" href="#default
   ],
   &#39;observation_type&#39;: ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
   &#39;fused_module&#39;: &lt;class &#39;torch.ao.nn.intrinsic.modules.fused.LinearReLU&#39;&gt;,
-  &#39;fuser_method&#39;: &lt;function _sequential_wrapper2.&lt;locals&gt;.fuser_method at 0x7f5bfce47c10&gt;,
+  &#39;fuser_method&#39;: &lt;function _sequential_wrapper2.&lt;locals&gt;.fuser_method at 0x7fb8e9876d30&gt;,
 },
 {
-  &#39;pattern&#39;: (&lt;class &#39;torch.nn.modules.linear.Linear&#39;&gt;, &lt;function relu at 0x7f5bfeb17790&gt;),
+  &#39;pattern&#39;: (&lt;class &#39;torch.nn.modules.linear.Linear&#39;&gt;, &lt;function relu at 0x7fb8eb54a670&gt;),
   &#39;dtype_configs&#39;: [
     {
       &#39;input_dtype&#39;: DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
@@ -2549,7 +2549,7 @@ <h2>Default values for native configurations<a class="headerlink" href="#default
   ],
   &#39;observation_type&#39;: ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
   &#39;fused_module&#39;: &lt;class &#39;torch.ao.nn.intrinsic.modules.fused.LinearReLU&#39;&gt;,
-  &#39;fuser_method&#39;: &lt;function _sequential_wrapper2.&lt;locals&gt;.fuser_method at 0x7f5bfce47ca0&gt;,
+  &#39;fuser_method&#39;: &lt;function _sequential_wrapper2.&lt;locals&gt;.fuser_method at 0x7fb8e9876dc0&gt;,
 },
 {
   &#39;pattern&#39;: (&lt;built-in function linear&gt;, &lt;class &#39;torch.nn.modules.activation.ReLU&#39;&gt;),
@@ -2578,7 +2578,7 @@ <h2>Default values for native configurations<a class="headerlink" href="#default
   &#39;observation_type&#39;: ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
 },
 {
-  &#39;pattern&#39;: (&lt;built-in function linear&gt;, &lt;function relu at 0x7f5bfeb17790&gt;),
+  &#39;pattern&#39;: (&lt;built-in function linear&gt;, &lt;function relu at 0x7fb8eb54a670&gt;),
   &#39;dtype_configs&#39;: [
     {
       &#39;input_dtype&#39;: DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
@@ -2619,7 +2619,7 @@ <h2>Default values for native configurations<a class="headerlink" href="#default
   &#39;observation_type&#39;: ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
 },
 {
-  &#39;pattern&#39;: (&lt;built-in function add&gt;, &lt;function relu at 0x7f5bfeb17790&gt;),
+  &#39;pattern&#39;: (&lt;built-in function add&gt;, &lt;function relu at 0x7fb8eb54a670&gt;),
   &#39;dtype_configs&#39;: [
     {
       &#39;input_dtype&#39;: DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
@@ -2634,7 +2634,7 @@ <h2>Default values for native configurations<a class="headerlink" href="#default
   &#39;observation_type&#39;: ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
 },
 {
-  &#39;pattern&#39;: (&lt;built-in function add&gt;, &lt;built-in method relu of type object at 0x7f5c26368780&gt;),
+  &#39;pattern&#39;: (&lt;built-in function add&gt;, &lt;built-in method relu of type object at 0x7fb912d9b7c0&gt;),
   &#39;dtype_configs&#39;: [
     {
       &#39;input_dtype&#39;: DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
@@ -2649,7 +2649,7 @@ <h2>Default values for native configurations<a class="headerlink" href="#default
   &#39;observation_type&#39;: ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
 },
 {
-  &#39;pattern&#39;: (&lt;built-in method add of type object at 0x7f5c26368780&gt;, &lt;class &#39;torch.nn.modules.activation.ReLU&#39;&gt;),
+  &#39;pattern&#39;: (&lt;built-in method add of type object at 0x7fb912d9b7c0&gt;, &lt;class &#39;torch.nn.modules.activation.ReLU&#39;&gt;),
   &#39;dtype_configs&#39;: [
     {
       &#39;input_dtype&#39;: DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
@@ -2664,7 +2664,7 @@ <h2>Default values for native configurations<a class="headerlink" href="#default
   &#39;observation_type&#39;: ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
 },
 {
-  &#39;pattern&#39;: (&lt;built-in method add of type object at 0x7f5c26368780&gt;, &lt;function relu at 0x7f5bfeb17790&gt;),
+  &#39;pattern&#39;: (&lt;built-in method add of type object at 0x7fb912d9b7c0&gt;, &lt;function relu at 0x7fb8eb54a670&gt;),
   &#39;dtype_configs&#39;: [
     {
       &#39;input_dtype&#39;: DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
@@ -2679,7 +2679,7 @@ <h2>Default values for native configurations<a class="headerlink" href="#default
   &#39;observation_type&#39;: ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
 },
 {
-  &#39;pattern&#39;: (&lt;built-in method add of type object at 0x7f5c26368780&gt;, &lt;built-in method relu of type object at 0x7f5c26368780&gt;),
+  &#39;pattern&#39;: (&lt;built-in method add of type object at 0x7fb912d9b7c0&gt;, &lt;built-in method relu of type object at 0x7fb912d9b7c0&gt;),
   &#39;dtype_configs&#39;: [
     {
       &#39;input_dtype&#39;: DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
@@ -2709,7 +2709,7 @@ <h2>Default values for native configurations<a class="headerlink" href="#default
   &#39;observation_type&#39;: ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
 },
 {
-  &#39;pattern&#39;: (&lt;built-in function mul&gt;, &lt;function relu at 0x7f5bfeb17790&gt;),
+  &#39;pattern&#39;: (&lt;built-in function mul&gt;, &lt;function relu at 0x7fb8eb54a670&gt;),
   &#39;dtype_configs&#39;: [
     {
       &#39;input_dtype&#39;: DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
@@ -2724,7 +2724,7 @@ <h2>Default values for native configurations<a class="headerlink" href="#default
   &#39;observation_type&#39;: ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
 },
 {
-  &#39;pattern&#39;: (&lt;built-in function mul&gt;, &lt;built-in method relu of type object at 0x7f5c26368780&gt;),
+  &#39;pattern&#39;: (&lt;built-in function mul&gt;, &lt;built-in method relu of type object at 0x7fb912d9b7c0&gt;),
   &#39;dtype_configs&#39;: [
     {
       &#39;input_dtype&#39;: DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
@@ -2739,7 +2739,7 @@ <h2>Default values for native configurations<a class="headerlink" href="#default
   &#39;observation_type&#39;: ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
 },
 {
-  &#39;pattern&#39;: (&lt;built-in method mul of type object at 0x7f5c26368780&gt;, &lt;class &#39;torch.nn.modules.activation.ReLU&#39;&gt;),
+  &#39;pattern&#39;: (&lt;built-in method mul of type object at 0x7fb912d9b7c0&gt;, &lt;class &#39;torch.nn.modules.activation.ReLU&#39;&gt;),
   &#39;dtype_configs&#39;: [
     {
       &#39;input_dtype&#39;: DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
@@ -2754,7 +2754,7 @@ <h2>Default values for native configurations<a class="headerlink" href="#default
   &#39;observation_type&#39;: ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
 },
 {
-  &#39;pattern&#39;: (&lt;built-in method mul of type object at 0x7f5c26368780&gt;, &lt;function relu at 0x7f5bfeb17790&gt;),
+  &#39;pattern&#39;: (&lt;built-in method mul of type object at 0x7fb912d9b7c0&gt;, &lt;function relu at 0x7fb8eb54a670&gt;),
   &#39;dtype_configs&#39;: [
     {
       &#39;input_dtype&#39;: DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
@@ -2769,7 +2769,7 @@ <h2>Default values for native configurations<a class="headerlink" href="#default
   &#39;observation_type&#39;: ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
 },
 {
-  &#39;pattern&#39;: (&lt;built-in method mul of type object at 0x7f5c26368780&gt;, &lt;built-in method relu of type object at 0x7f5c26368780&gt;),
+  &#39;pattern&#39;: (&lt;built-in method mul of type object at 0x7fb912d9b7c0&gt;, &lt;built-in method relu of type object at 0x7fb912d9b7c0&gt;),
   &#39;dtype_configs&#39;: [
     {
       &#39;input_dtype&#39;: DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
@@ -2794,7 +2794,7 @@ <h2>Default values for native configurations<a class="headerlink" href="#default
   &#39;observation_type&#39;: ObservationType.OUTPUT_SHARE_OBSERVER_WITH_INPUT,
 },
 {
-  &#39;pattern&#39;: &lt;function relu at 0x7f5bfeb17790&gt;,
+  &#39;pattern&#39;: &lt;function relu at 0x7fb8eb54a670&gt;,
   &#39;dtype_configs&#39;: [
     {
       &#39;input_dtype&#39;: DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
@@ -2833,10 +2833,10 @@ <h2>Default values for native configurations<a class="headerlink" href="#default
   ],
   &#39;observation_type&#39;: ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
   &#39;fused_module&#39;: &lt;class &#39;torch.ao.nn.intrinsic.modules.fused.BNReLU2d&#39;&gt;,
-  &#39;fuser_method&#39;: &lt;function _sequential_wrapper2.&lt;locals&gt;.fuser_method at 0x7f5bfce47d30&gt;,
+  &#39;fuser_method&#39;: &lt;function _sequential_wrapper2.&lt;locals&gt;.fuser_method at 0x7fb8e9876e50&gt;,
 },
 {
-  &#39;pattern&#39;: (&lt;class &#39;torch.nn.modules.batchnorm.BatchNorm2d&#39;&gt;, &lt;function relu at 0x7f5bfeb17790&gt;),
+  &#39;pattern&#39;: (&lt;class &#39;torch.nn.modules.batchnorm.BatchNorm2d&#39;&gt;, &lt;function relu at 0x7fb8eb54a670&gt;),
   &#39;dtype_configs&#39;: [
     {
       &#39;input_dtype&#39;: DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
@@ -2845,7 +2845,7 @@ <h2>Default values for native configurations<a class="headerlink" href="#default
   ],
   &#39;observation_type&#39;: ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
   &#39;fused_module&#39;: &lt;class &#39;torch.ao.nn.intrinsic.modules.fused.BNReLU2d&#39;&gt;,
-  &#39;fuser_method&#39;: &lt;function _sequential_wrapper2.&lt;locals&gt;.fuser_method at 0x7f5bfce47dc0&gt;,
+  &#39;fuser_method&#39;: &lt;function _sequential_wrapper2.&lt;locals&gt;.fuser_method at 0x7fb8e9876ee0&gt;,
 },
 {
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@@ -2857,10 +2857,10 @@ <h2>Default values for native configurations<a class="headerlink" href="#default
   ],
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-  &#39;fuser_method&#39;: &lt;function _sequential_wrapper2.&lt;locals&gt;.fuser_method at 0x7f5bfce47e50&gt;,
+  &#39;fuser_method&#39;: &lt;function _sequential_wrapper2.&lt;locals&gt;.fuser_method at 0x7fb8e9876f70&gt;,
 },
 {
-  &#39;pattern&#39;: (&lt;class &#39;torch.nn.modules.batchnorm.BatchNorm3d&#39;&gt;, &lt;function relu at 0x7f5bfeb17790&gt;),
+  &#39;pattern&#39;: (&lt;class &#39;torch.nn.modules.batchnorm.BatchNorm3d&#39;&gt;, &lt;function relu at 0x7fb8eb54a670&gt;),
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@@ -2869,7 +2869,7 @@ <h2>Default values for native configurations<a class="headerlink" href="#default
   ],
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-  &#39;fuser_method&#39;: &lt;function _sequential_wrapper2.&lt;locals&gt;.fuser_method at 0x7f5bfce47ee0&gt;,
+  &#39;fuser_method&#39;: &lt;function _sequential_wrapper2.&lt;locals&gt;.fuser_method at 0x7fb8e988f040&gt;,
 },
 {
   &#39;pattern&#39;: &lt;class &#39;torch.nn.modules.activation.ReLU6&#39;&gt;,
@@ -2882,7 +2882,7 @@ <h2>Default values for native configurations<a class="headerlink" href="#default
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 },
 {
-  &#39;pattern&#39;: &lt;function relu6 at 0x7f5bfeb17940&gt;,
+  &#39;pattern&#39;: &lt;function relu6 at 0x7fb8eb54a820&gt;,
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@@ -2902,7 +2902,7 @@ <h2>Default values for native configurations<a class="headerlink" href="#default
   &#39;observation_type&#39;: ObservationType.OUTPUT_SHARE_OBSERVER_WITH_INPUT,
 },
 {
-  &#39;pattern&#39;: &lt;built-in method repeat_interleave of type object at 0x7f5c26368780&gt;,
+  &#39;pattern&#39;: &lt;built-in method repeat_interleave of type object at 0x7fb912d9b7c0&gt;,
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     {
       &#39;input_dtype&#39;: DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
@@ -2984,7 +2984,7 @@ <h2>Default values for native configurations<a class="headerlink" href="#default
   &#39;observation_type&#39;: ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
 },
 {
-  &#39;pattern&#39;: &lt;built-in method sigmoid of type object at 0x7f5c26368780&gt;,
+  &#39;pattern&#39;: &lt;built-in method sigmoid of type object at 0x7fb912d9b7c0&gt;,
   &#39;dtype_configs&#39;: [
     {
       &#39;input_dtype&#39;: DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=0, quant_max_upper_bound=255, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=0.00390625, zero_point_exact_match=0),
@@ -3034,7 +3034,7 @@ <h2>Default values for native configurations<a class="headerlink" href="#default
   &#39;observation_type&#39;: ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
 },
 {
-  &#39;pattern&#39;: &lt;built-in method squeeze of type object at 0x7f5c26368780&gt;,
+  &#39;pattern&#39;: &lt;built-in method squeeze of type object at 0x7fb912d9b7c0&gt;,
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     {
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@@ -3064,7 +3064,7 @@ <h2>Default values for native configurations<a class="headerlink" href="#default
   &#39;observation_type&#39;: ObservationType.OUTPUT_SHARE_OBSERVER_WITH_INPUT,
 },
 {
-  &#39;pattern&#39;: &lt;built-in method stack of type object at 0x7f5c26368780&gt;,
+  &#39;pattern&#39;: &lt;built-in method stack of type object at 0x7fb912d9b7c0&gt;,
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@@ -3084,7 +3084,7 @@ <h2>Default values for native configurations<a class="headerlink" href="#default
   &#39;observation_type&#39;: ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
 },
 {
-  &#39;pattern&#39;: &lt;built-in method tanh of type object at 0x7f5c26368780&gt;,
+  &#39;pattern&#39;: &lt;built-in method tanh of type object at 0x7fb912d9b7c0&gt;,
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     {
       &#39;input_dtype&#39;: DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=0, quant_max_upper_bound=255, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=0.0078125, zero_point_exact_match=128),
@@ -3114,7 +3114,7 @@ <h2>Default values for native configurations<a class="headerlink" href="#default
   &#39;observation_type&#39;: ObservationType.OUTPUT_USE_DIFFERENT_OBSERVER_AS_INPUT,
 },
 {
-  &#39;pattern&#39;: &lt;built-in method transpose of type object at 0x7f5c26368780&gt;,
+  &#39;pattern&#39;: &lt;built-in method transpose of type object at 0x7fb912d9b7c0&gt;,
   &#39;dtype_configs&#39;: [
     {
       &#39;input_dtype&#39;: DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
@@ -3134,7 +3134,7 @@ <h2>Default values for native configurations<a class="headerlink" href="#default
   &#39;observation_type&#39;: ObservationType.OUTPUT_SHARE_OBSERVER_WITH_INPUT,
 },
 {
-  &#39;pattern&#39;: &lt;built-in method unsqueeze of type object at 0x7f5c26368780&gt;,
+  &#39;pattern&#39;: &lt;built-in method unsqueeze of type object at 0x7fb912d9b7c0&gt;,
   &#39;dtype_configs&#39;: [
     {
       &#39;input_dtype&#39;: DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=None, quant_max_upper_bound=None, scale_min_lower_bound=None, scale_max_upper_bound=None, scale_exact_match=None, zero_point_exact_match=None),
diff --git a/main/quantization-support.html b/main/quantization-support.html
index 93983c2bf62..fda7e3dfbeb 100644
--- a/main/quantization-support.html
+++ b/main/quantization-support.html
@@ -300,7 +300,7 @@
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     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
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diff --git a/main/quantization.html b/main/quantization.html
index 103659997d6..ec3c15dc83b 100644
--- a/main/quantization.html
+++ b/main/quantization.html
@@ -300,7 +300,7 @@
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     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
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     <div class="searchbox" id="googleSearchBox">
diff --git a/main/random.html b/main/random.html
index ba625309a94..d6a4149d8b3 100644
--- a/main/random.html
+++ b/main/random.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
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     <div id="searchBox">
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@@ -610,7 +610,7 @@
 <span id="torch-random"></span><h1>torch.random<a class="headerlink" href="#module-torch.random" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.random.fork_rng">
-<span class="sig-prename descclassname"><span class="pre">torch.random.</span></span><span class="sig-name descname"><span class="pre">fork_rng</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">devices</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">enabled</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_caller</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'fork_rng'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_devices_kw</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'devices'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device_type</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'cuda'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/random.html#fork_rng"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/random.py#L125"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.random.fork_rng" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.random.</span></span><span class="sig-name descname"><span class="pre">fork_rng</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">devices</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">enabled</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_caller</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'fork_rng'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_devices_kw</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'devices'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device_type</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'cuda'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/random.html#fork_rng"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/random.py#L125"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.random.fork_rng" title="Permalink to this definition">¶</a></dt>
 <dd><p>Forks the RNG, so that when you return, the RNG is reset
 to the state that it was previously in.</p>
 <dl class="field-list simple">
@@ -636,7 +636,7 @@
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.random.get_rng_state">
-<span class="sig-prename descclassname"><span class="pre">torch.random.</span></span><span class="sig-name descname"><span class="pre">get_rng_state</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/random.html#get_rng_state"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/random.py#L22"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.random.get_rng_state" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.random.</span></span><span class="sig-name descname"><span class="pre">get_rng_state</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/random.html#get_rng_state"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/random.py#L22"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.random.get_rng_state" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns the random number generator state as a <cite>torch.ByteTensor</cite>.</p>
 <div class="admonition note">
 <p class="admonition-title">Note</p>
@@ -652,7 +652,7 @@
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.random.initial_seed">
-<span class="sig-prename descclassname"><span class="pre">torch.random.</span></span><span class="sig-name descname"><span class="pre">initial_seed</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/random.html#initial_seed"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/random.py#L113"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.random.initial_seed" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.random.</span></span><span class="sig-name descname"><span class="pre">initial_seed</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/random.html#initial_seed"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/random.py#L113"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.random.initial_seed" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns the initial seed for generating random numbers as a
 Python <cite>long</cite>.</p>
 <div class="admonition note">
@@ -668,7 +668,7 @@
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.random.manual_seed">
-<span class="sig-prename descclassname"><span class="pre">torch.random.</span></span><span class="sig-name descname"><span class="pre">manual_seed</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">seed</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/random.html#manual_seed"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/random.py#L32"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.random.manual_seed" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.random.</span></span><span class="sig-name descname"><span class="pre">manual_seed</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">seed</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/random.html#manual_seed"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/random.py#L32"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.random.manual_seed" title="Permalink to this definition">¶</a></dt>
 <dd><p>Sets the seed for generating random numbers on all devices. Returns a
 <cite>torch.Generator</cite> object.</p>
 <dl class="field-list simple">
@@ -686,7 +686,7 @@
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.random.seed">
-<span class="sig-prename descclassname"><span class="pre">torch.random.</span></span><span class="sig-name descname"><span class="pre">seed</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/random.html#seed"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/random.py#L63"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.random.seed" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.random.</span></span><span class="sig-name descname"><span class="pre">seed</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/random.html#seed"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/random.py#L63"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.random.seed" title="Permalink to this definition">¶</a></dt>
 <dd><p>Sets the seed for generating random numbers to a non-deterministic
 random number on all devices. Returns a 64 bit number used to seed the RNG.</p>
 <dl class="field-list simple">
@@ -698,7 +698,7 @@
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.random.set_rng_state">
-<span class="sig-prename descclassname"><span class="pre">torch.random.</span></span><span class="sig-name descname"><span class="pre">set_rng_state</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">new_state</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/random.html#set_rng_state"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/random.py#L10"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.random.set_rng_state" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.random.</span></span><span class="sig-name descname"><span class="pre">set_rng_state</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">new_state</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/random.html#set_rng_state"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/random.py#L10"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.random.set_rng_state" title="Permalink to this definition">¶</a></dt>
 <dd><p>Sets the random number generator state.</p>
 <div class="admonition note">
 <p class="admonition-title">Note</p>
diff --git a/main/rpc.html b/main/rpc.html
index f68525fce3e..9a216052da8 100644
--- a/main/rpc.html
+++ b/main/rpc.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -693,7 +693,7 @@ <h2>Basics<a class="headerlink" href="#basics" title="Permalink to this heading"
 framework, RRef framework and distributed autograd.</p>
 <span class="target" id="module-torch.distributed.rpc"></span><dl class="py function">
 <dt class="sig sig-object py" id="torch.distributed.rpc.init_rpc">
-<span class="sig-prename descclassname"><span class="pre">torch.distributed.rpc.</span></span><span class="sig-name descname"><span class="pre">init_rpc</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">backend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">rank</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">world_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">rpc_backend_options</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/rpc.html#init_rpc"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/rpc/__init__.py#L85"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.rpc.init_rpc" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributed.rpc.</span></span><span class="sig-name descname"><span class="pre">init_rpc</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">backend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">rank</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">world_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">rpc_backend_options</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/rpc.html#init_rpc"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/rpc/__init__.py#L85"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.rpc.init_rpc" title="Permalink to this definition">¶</a></dt>
 <dd><p>Initializes RPC primitives such as the local RPC agent
 and distributed autograd, which immediately makes the current
 process ready to send and receive RPCs.</p>
@@ -743,7 +743,7 @@ <h2>Basics<a class="headerlink" href="#basics" title="Permalink to this heading"
 </div>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.distributed.rpc.rpc_sync">
-<span class="sig-prename descclassname"><span class="pre">torch.distributed.rpc.</span></span><span class="sig-name descname"><span class="pre">rpc_sync</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">to</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">func</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kwargs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">timeout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-1.0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/rpc/api.html#rpc_sync"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/rpc/api.py#L757"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.rpc.rpc_sync" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributed.rpc.</span></span><span class="sig-name descname"><span class="pre">rpc_sync</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">to</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">func</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kwargs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">timeout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-1.0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/rpc/api.html#rpc_sync"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/rpc/api.py#L757"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.rpc.rpc_sync" title="Permalink to this definition">¶</a></dt>
 <dd><p>Make a blocking RPC call to run function <code class="docutils literal notranslate"><span class="pre">func</span></code> on worker <code class="docutils literal notranslate"><span class="pre">to</span></code>. RPC
 messages are sent and received in parallel to execution of Python code. This
 method is thread-safe.</p>
@@ -818,7 +818,7 @@ <h2>Basics<a class="headerlink" href="#basics" title="Permalink to this heading"
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.distributed.rpc.rpc_async">
-<span class="sig-prename descclassname"><span class="pre">torch.distributed.rpc.</span></span><span class="sig-name descname"><span class="pre">rpc_async</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">to</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">func</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kwargs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">timeout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-1.0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/rpc/api.html#rpc_async"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/rpc/api.py#L831"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.rpc.rpc_async" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributed.rpc.</span></span><span class="sig-name descname"><span class="pre">rpc_async</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">to</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">func</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kwargs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">timeout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-1.0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/rpc/api.html#rpc_async"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/rpc/api.py#L831"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.rpc.rpc_async" title="Permalink to this definition">¶</a></dt>
 <dd><p>Make a non-blocking RPC call to run function <code class="docutils literal notranslate"><span class="pre">func</span></code> on worker <code class="docutils literal notranslate"><span class="pre">to</span></code>. RPC
 messages are sent and received in parallel to execution of Python code. This
 method is thread-safe. This method will immediately return a
@@ -915,7 +915,7 @@ <h2>Basics<a class="headerlink" href="#basics" title="Permalink to this heading"
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.distributed.rpc.remote">
-<span class="sig-prename descclassname"><span class="pre">torch.distributed.rpc.</span></span><span class="sig-name descname"><span class="pre">remote</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">to</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">func</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kwargs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">timeout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-1.0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/rpc/api.html#remote"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/rpc/api.py#L545"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.rpc.remote" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributed.rpc.</span></span><span class="sig-name descname"><span class="pre">remote</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">to</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">func</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">args</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kwargs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">timeout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-1.0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/rpc/api.html#remote"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/rpc/api.py#L545"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.rpc.remote" title="Permalink to this definition">¶</a></dt>
 <dd><p>Make a remote call to run <code class="docutils literal notranslate"><span class="pre">func</span></code> on worker <code class="docutils literal notranslate"><span class="pre">to</span></code> and return an
 <code class="xref py py-class docutils literal notranslate"><span class="pre">RRef</span></code> to the result value immediately.
 Worker <code class="docutils literal notranslate"><span class="pre">to</span></code> will be the owner of the returned
@@ -1024,7 +1024,7 @@ <h2>Basics<a class="headerlink" href="#basics" title="Permalink to this heading"
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.distributed.rpc.get_worker_info">
-<span class="sig-prename descclassname"><span class="pre">torch.distributed.rpc.</span></span><span class="sig-name descname"><span class="pre">get_worker_info</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">worker_name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/rpc/api.html#get_worker_info"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/rpc/api.py#L420"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.rpc.get_worker_info" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributed.rpc.</span></span><span class="sig-name descname"><span class="pre">get_worker_info</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">worker_name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/rpc/api.html#get_worker_info"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/rpc/api.py#L420"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.rpc.get_worker_info" title="Permalink to this definition">¶</a></dt>
 <dd><p>Get <a class="reference internal" href="#torch.distributed.rpc.WorkerInfo" title="torch.distributed.rpc.WorkerInfo"><code class="xref py py-class docutils literal notranslate"><span class="pre">WorkerInfo</span></code></a> of a given worker name.
 Use this <a class="reference internal" href="#torch.distributed.rpc.WorkerInfo" title="torch.distributed.rpc.WorkerInfo"><code class="xref py py-class docutils literal notranslate"><span class="pre">WorkerInfo</span></code></a> to avoid passing an
 expensive string on every invocation.</p>
@@ -1043,7 +1043,7 @@ <h2>Basics<a class="headerlink" href="#basics" title="Permalink to this heading"
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.distributed.rpc.shutdown">
-<span class="sig-prename descclassname"><span class="pre">torch.distributed.rpc.</span></span><span class="sig-name descname"><span class="pre">shutdown</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">graceful</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">timeout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/rpc/api.html#shutdown"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/rpc/api.py#L321"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.rpc.shutdown" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributed.rpc.</span></span><span class="sig-name descname"><span class="pre">shutdown</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">graceful</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">timeout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/rpc/api.html#shutdown"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/rpc/api.py#L321"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.rpc.shutdown" title="Permalink to this definition">¶</a></dt>
 <dd><p>Perform a shutdown of the RPC agent, and then destroy the RPC agent. This
 stops the local agent from accepting outstanding requests, and shuts
 down the RPC framework by terminating all RPC threads. If <code class="docutils literal notranslate"><span class="pre">graceful=True</span></code>,
@@ -1124,7 +1124,7 @@ <h2>Basics<a class="headerlink" href="#basics" title="Permalink to this heading"
 how a given function should be treated on the callee side.</p>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.distributed.rpc.functions.async_execution">
-<span class="sig-prename descclassname"><span class="pre">torch.distributed.rpc.functions.</span></span><span class="sig-name descname"><span class="pre">async_execution</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">fn</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/rpc/functions.html#async_execution"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/rpc/functions.py#L5"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.rpc.functions.async_execution" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.distributed.rpc.functions.</span></span><span class="sig-name descname"><span class="pre">async_execution</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">fn</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/rpc/functions.html#async_execution"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/rpc/functions.py#L5"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.rpc.functions.async_execution" title="Permalink to this definition">¶</a></dt>
 <dd><p>A decorator for a function indicating that the return value of the function
 is guaranteed to be a <a class="reference internal" href="futures.html#torch.futures.Future" title="torch.futures.Future"><code class="xref py py-class docutils literal notranslate"><span class="pre">Future</span></code></a> object and this
 function can run asynchronously on the RPC callee. More specifically, the
@@ -1367,7 +1367,7 @@ <h4>TensorPipe Backend<a class="headerlink" href="#tensorpipe-backend" title="Pe
 </div>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributed.rpc.TensorPipeRpcBackendOptions">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.rpc.</span></span><span class="sig-name descname"><span class="pre">TensorPipeRpcBackendOptions</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_worker_threads</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">16</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">rpc_timeout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">60.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">init_method</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'env://'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device_maps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">devices</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_transports</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_channels</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/rpc/options.html#TensorPipeRpcBackendOptions"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/rpc/options.py#L46"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.rpc.TensorPipeRpcBackendOptions" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.rpc.</span></span><span class="sig-name descname"><span class="pre">TensorPipeRpcBackendOptions</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_worker_threads</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">16</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">rpc_timeout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">60.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">init_method</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'env://'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device_maps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">devices</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_transports</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_channels</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/rpc/options.html#TensorPipeRpcBackendOptions"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/rpc/options.py#L46"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.rpc.TensorPipeRpcBackendOptions" title="Permalink to this definition">¶</a></dt>
 <dd><p>The backend options for
 <code class="xref py py-class docutils literal notranslate"><span class="pre">TensorPipeAgent</span></code>, derived from
 <a class="reference internal" href="#torch.distributed.rpc.RpcBackendOptions" title="torch.distributed.rpc.RpcBackendOptions"><code class="xref py py-class docutils literal notranslate"><span class="pre">RpcBackendOptions</span></code></a>.</p>
@@ -1439,7 +1439,7 @@ <h4>TensorPipe Backend<a class="headerlink" href="#tensorpipe-backend" title="Pe
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.rpc.TensorPipeRpcBackendOptions.set_device_map">
-<span class="sig-name descname"><span class="pre">set_device_map</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">to</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device_map</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/rpc/options.html#TensorPipeRpcBackendOptions.set_device_map"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/rpc/options.py#L107"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.rpc.TensorPipeRpcBackendOptions.set_device_map" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">set_device_map</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">to</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device_map</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/rpc/options.html#TensorPipeRpcBackendOptions.set_device_map"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/rpc/options.py#L107"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.rpc.TensorPipeRpcBackendOptions.set_device_map" title="Permalink to this definition">¶</a></dt>
 <dd><p>Set device mapping between each RPC caller and callee pair. This
 function can be called multiple times to incrementally add
 device placement configurations.</p>
@@ -1490,7 +1490,7 @@ <h4>TensorPipe Backend<a class="headerlink" href="#tensorpipe-backend" title="Pe
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.distributed.rpc.TensorPipeRpcBackendOptions.set_devices">
-<span class="sig-name descname"><span class="pre">set_devices</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">devices</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/rpc/options.html#TensorPipeRpcBackendOptions.set_devices"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/rpc/options.py#L165"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.rpc.TensorPipeRpcBackendOptions.set_devices" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">set_devices</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">devices</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/rpc/options.html#TensorPipeRpcBackendOptions.set_devices"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/rpc/options.py#L165"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.rpc.TensorPipeRpcBackendOptions.set_devices" title="Permalink to this definition">¶</a></dt>
 <dd><p>Set local devices used by the TensorPipe RPC agent. When processing
 CUDA RPC requests, the TensorPipe RPC agent will properly synchronize
 CUDA streams for all devices in this <code class="docutils literal notranslate"><span class="pre">List</span></code>.</p>
@@ -1837,7 +1837,7 @@ <h4>TensorPipe Backend<a class="headerlink" href="#tensorpipe-backend" title="Pe
 asynchronously if needed via additional APIs supported by RemoteModule.</p>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.distributed.nn.api.remote_module.RemoteModule">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.nn.api.remote_module.</span></span><span class="sig-name descname"><span class="pre">RemoteModule</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/nn/api/remote_module.html#RemoteModule"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/distributed/nn/api/remote_module.py#L593"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.nn.api.remote_module.RemoteModule" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.nn.api.remote_module.</span></span><span class="sig-name descname"><span class="pre">RemoteModule</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributed/nn/api/remote_module.html#RemoteModule"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/nn/api/remote_module.py#L593"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.nn.api.remote_module.RemoteModule" title="Permalink to this definition">¶</a></dt>
 <dd><blockquote>
 <div><p>A RemoteModule instance can only be created after RPC initialization.</p>
 <p>It creates a user-specified module on a specified remote node.
@@ -1918,7 +1918,7 @@ <h4>TensorPipe Backend<a class="headerlink" href="#tensorpipe-backend" title="Pe
 </dl>
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 <dt class="sig sig-object py" id="torch.distributed.nn.api.remote_module.RemoteModule.get_module_rref">
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+<span class="sig-name descname"><span class="pre">get_module_rref</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/nn/api/remote_module.py#L292"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.nn.api.remote_module.RemoteModule.get_module_rref" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return an <code class="xref py py-class docutils literal notranslate"><span class="pre">RRef</span></code> (<code class="docutils literal notranslate"><span class="pre">RRef[nn.Module]</span></code>) pointing to the remote module.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -1929,7 +1929,7 @@ <h4>TensorPipe Backend<a class="headerlink" href="#tensorpipe-backend" title="Pe
 
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+<span class="sig-name descname"><span class="pre">remote_parameters</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">recurse</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/nn/api/remote_module.py#L273"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.nn.api.remote_module.RemoteModule.remote_parameters" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return a list of <code class="xref py py-class docutils literal notranslate"><span class="pre">RRef</span></code> pointing to the remote module’s parameters.</p>
 <p>This can typically be used in conjunction
 with <a class="reference internal" href="distributed.optim.html#torch.distributed.optim.DistributedOptimizer" title="torch.distributed.optim.DistributedOptimizer"><code class="xref py py-class docutils literal notranslate"><span class="pre">DistributedOptimizer</span></code></a>.</p>
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+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.distributed.autograd.</span></span><span class="sig-name descname"><span class="pre">context</span></span><a class="reference internal" href="_modules/torch/distributed/autograd.html#context"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/distributed/autograd/__init__.py#L29"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.distributed.autograd.context" title="Permalink to this definition">¶</a></dt>
 <dd><p>Context object to wrap forward and backward passes when using
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1871, 2018, 2100, 2119, 2130, 2135, 2138, 2141, 2149, 2150, 2155, 2159, 2160, 2165, 2166, 2167, 2170, 2172, 2175, 2188, 2194, 2196, 2203], "been": [2, 6, 8, 10, 12, 18, 20, 24, 25, 29, 30, 33, 34, 35, 37, 38, 40, 43, 50, 54, 55, 57, 62, 63, 68, 85, 87, 485, 487, 680, 770, 929, 930, 934, 1006, 1040, 1044, 1049, 1081, 1084, 1144, 1149, 1203, 1318, 1319, 1323, 1325, 1330, 1387, 1444, 1445, 1457, 1533, 1540, 1552, 1598, 1653, 1688, 1772, 1781, 1782, 1792, 1797, 1801, 1803, 1823, 1824, 1839, 1866, 1867, 1868, 1875, 1877, 1895, 1993, 2011, 2039, 2050, 2054, 2073, 2104, 2106, 2108, 2113, 2118, 2119, 2120, 2121, 2126, 2129, 2130, 2132, 2133, 2139, 2141, 2142, 2144, 2146, 2148, 2150, 2153, 2157, 2158, 2165, 2167, 2170, 2175, 2184, 2185, 2188, 2189, 2191, 2194, 2199, 2203, 2204], "longer": [2, 29, 53, 63, 64, 485, 697, 919, 934, 948, 949, 953, 1312, 1319, 1772, 1827, 1830, 2110, 2113, 2130, 2133, 2159, 2165, 2167, 2188, 2203, 2204], "find": [2, 8, 15, 29, 50, 64, 68, 1145, 1222, 1225, 1269, 1337, 1388, 1512, 1513, 1514, 1552, 1837, 1885, 1896, 1931, 1998, 2095, 2108, 2110, 2118, 2120, 2121, 2130, 2132, 2133, 2135, 2136, 2138, 2141, 2146, 2148, 2153, 2155, 2156, 2160, 2165, 2170, 2173, 2175, 2177, 2180, 2190, 2191, 2192, 2193, 2194, 2195, 2196, 2197, 2202, 2203, 2204, 2205, 2207], "quick": [2, 8, 62, 2107, 2144, 2154, 2186, 2201], "guid": [2, 9, 24, 29, 1327, 1827, 2096, 2104, 2133, 2137, 2140, 2158, 2160, 2183, 2201], "var": [2, 43, 48, 50, 54, 1496, 1497, 1498, 1535, 1536, 1544, 1545, 1546, 1554, 1622, 1685, 1899, 2044, 2098, 2100, 2119, 2156, 2190, 2198, 2204], "thing": [2, 4, 8, 9, 29, 35, 55, 57, 64, 68, 1203, 1325, 1417, 1494, 1690, 1969, 2099, 2104, 2130, 2133, 2136, 2137, 2138, 2141, 2146, 2155, 2158, 2160, 2161, 2167, 2188, 2191, 2193, 2194, 2201, 2203, 2204], "detach": [2, 6, 221, 414, 447, 457, 998, 1202, 1315, 1501, 1582, 1672, 1690, 1880, 1973, 2014, 2097, 2098, 2119, 2136, 2138, 2155, 2156, 2162, 2170, 2174, 2176, 2202], "register_hook": [2, 2119, 2130], "name": [2, 3, 4, 15, 20, 25, 29, 31, 33, 35, 37, 38, 40, 43, 44, 47, 48, 50, 53, 55, 56, 59, 68, 70, 71, 72, 73, 74, 75, 76, 78, 79, 80, 81, 82, 83, 680, 707, 757, 758, 767, 804, 828, 829, 830, 831, 842, 868, 883, 887, 961, 964, 966, 1011, 1013, 1056, 1069, 1197, 1202, 1212, 1220, 1222, 1227, 1229, 1313, 1315, 1316, 1323, 1326, 1331, 1332, 1346, 1351, 1352, 1358, 1359, 1361, 1362, 1363, 1364, 1374, 1375, 1377, 1379, 1380, 1387, 1582, 1633, 1641, 1764, 1769, 1770, 1772, 1789, 1790, 1791, 1792, 1794, 1795, 1796, 1797, 1798, 1799, 1800, 1801, 1802, 1803, 1804, 1805, 1806, 1807, 1808, 1809, 1810, 1811, 1812, 1813, 1814, 1815, 1824, 1825, 1827, 1836, 1837, 1839, 1840, 1841, 1842, 1843, 1844, 1845, 1846, 1847, 1849, 1856, 1859, 1860, 1861, 1862, 1863, 1880, 1927, 1937, 2037, 2039, 2042, 2064, 2095, 2096, 2097, 2098, 2100, 2101, 2104, 2106, 2111, 2112, 2113, 2118, 2121, 2128, 2130, 2133, 2135, 2136, 2141, 2143, 2144, 2149, 2152, 2155, 2156, 2158, 2159, 2165, 2166, 2172, 2175, 2176, 2177, 2179, 2180, 2181, 2184, 2185, 2189, 2191, 2192, 2194, 2197, 2200, 2201, 2202, 2203, 2204, 2205, 2206, 2209], "factori": [2, 3, 12, 38, 40, 44, 48, 50, 64, 834, 842, 968, 1145, 1642, 1828, 1837, 1935, 2098, 2104, 2108, 2120, 2133, 2136, 2156, 2173, 2176, 2190, 2193], "ones": [2, 4, 6, 20, 24, 29, 34, 36, 37, 38, 55, 57, 59, 64, 65, 67, 68, 70, 75, 253, 312, 401, 444, 445, 447, 514, 806, 888, 922, 938, 940, 941, 942, 943, 944, 948, 971, 983, 1001, 1128, 1136, 1158, 1202, 1209, 1210, 1213, 1312, 1315, 1327, 1336, 1362, 1378, 1387, 1495, 1515, 1524, 1528, 1535, 1536, 1554, 1582, 1586, 1587, 1589, 1591, 1597, 1634, 1643, 1644, 1645, 1679, 1740, 1758, 1762, 1771, 1772, 1797, 1798, 1799, 1800, 1801, 1802, 1803, 1804, 1806, 1807, 1820, 1822, 1825, 1835, 1836, 1880, 1899, 1916, 1934, 1939, 1964, 2017, 2021, 2039, 2051, 2098, 2100, 2102, 2104, 2108, 2119, 2120, 2130, 2131, 2133, 2136, 2145, 2147, 2148, 2155, 2156, 2157, 2158, 2160, 2165, 2167, 2169, 2171, 2172, 2173, 2176, 2197, 2202, 2203, 2204], "autograd_tensor": 2, "kwarg": [2, 6, 15, 24, 29, 30, 31, 33, 34, 35, 37, 50, 55, 56, 59, 68, 70, 579, 602, 757, 770, 772, 773, 834, 836, 838, 839, 840, 842, 845, 919, 934, 935, 1005, 1040, 1041, 1042, 1044, 1086, 1087, 1105, 1202, 1213, 1214, 1216, 1227, 1229, 1315, 1318, 1320, 1379, 1405, 1543, 1571, 1582, 1613, 1617, 1621, 1623, 1624, 1762, 1772, 1797, 1802, 1806, 1816, 1823, 1825, 1828, 1839, 1840, 1841, 1842, 1843, 1844, 1845, 1846, 1847, 1854, 1855, 1859, 1860, 1861, 1862, 1863, 1880, 1927, 2033, 2048, 2054, 2095, 2100, 2101, 2104, 2136, 2137, 2144, 2152, 2155, 2157, 2165, 2172, 2176, 2181, 2189, 2192, 2202, 2203, 2204, 2205], "base": [2, 4, 8, 10, 13, 15, 16, 21, 24, 29, 30, 31, 33, 34, 35, 36, 37, 38, 40, 43, 48, 50, 51, 53, 55, 56, 57, 59, 64, 68, 680, 803, 808, 814, 816, 834, 835, 837, 838, 839, 840, 842, 845, 848, 862, 890, 891, 929, 932, 934, 945, 946, 955, 1001, 1026, 1106, 1107, 1122, 1143, 1145, 1188, 1235, 1246, 1269, 1279, 1317, 1329, 1341, 1379, 1390, 1392, 1394, 1402, 1495, 1518, 1555, 1582, 1585, 1586, 1587, 1591, 1597, 1599, 1626, 1628, 1630, 1705, 1740, 1771, 1772, 1778, 1779, 1780, 1787, 1789, 1797, 1801, 1803, 1845, 1862, 1863, 1892, 1902, 1927, 1939, 1997, 1998, 2015, 2025, 2027, 2049, 2096, 2098, 2100, 2104, 2121, 2132, 2133, 2135, 2136, 2139, 2144, 2159, 2160, 2165, 2166, 2170, 2171, 2172, 2174, 2175, 2177, 2180, 2181, 2183, 2190, 2191, 2192, 2194, 2196, 2203], "static": [2, 4, 9, 15, 29, 30, 35, 36, 38, 40, 50, 55, 56, 59, 70, 75, 76, 79, 680, 806, 813, 829, 831, 842, 850, 858, 886, 888, 918, 919, 920, 921, 934, 935, 1042, 1222, 1223, 1229, 1235, 1254, 1329, 1626, 1772, 1971, 2097, 2098, 2099, 2100, 2133, 2139, 2143, 2149, 2151, 2152, 2161, 2165, 2172, 2188, 2190, 2192, 2203], "Then": [2, 30, 35, 68, 957, 1528, 1634, 1792, 1837, 2037, 2130, 2131, 2133, 2135, 2136, 2137, 2147, 2148, 2155, 2157, 2158, 2165, 2166, 2180, 2185, 2188, 2191, 2192, 2195], "op": [2, 5, 6, 15, 25, 29, 31, 33, 35, 36, 55, 56, 57, 59, 68, 70, 71, 72, 73, 74, 75, 76, 78, 79, 80, 81, 82, 83, 84, 149, 502, 523, 603, 689, 690, 692, 767, 804, 805, 806, 841, 850, 883, 890, 891, 922, 929, 932, 934, 935, 943, 991, 1001, 1003, 1045, 1061, 1064, 1068, 1069, 1086, 1087, 1114, 1118, 1120, 1203, 1266, 1316, 1329, 1331, 1332, 1387, 1393, 1446, 1450, 1461, 1463, 1465, 1599, 1839, 1840, 1841, 1842, 1843, 1844, 1845, 1846, 1847, 1858, 1859, 1860, 1861, 1862, 1863, 1883, 1941, 2023, 2055, 2058, 2060, 2063, 2064, 2087, 2090, 2091, 2100, 2106, 2107, 2110, 2113, 2115, 2119, 2120, 2132, 2133, 2135, 2136, 2146, 2152, 2158, 2159, 2160, 2161, 2162, 2163, 2170, 2172, 2174, 2176, 2181, 2185, 2188, 2189, 2193, 2194, 2195, 2196, 2198, 2201, 2202, 2204], "directli": [2, 4, 8, 10, 15, 16, 24, 25, 29, 31, 33, 35, 36, 37, 38, 39, 40, 49, 55, 56, 57, 59, 63, 68, 70, 83, 556, 742, 757, 837, 919, 929, 932, 934, 1003, 1014, 1128, 1196, 1203, 1210, 1212, 1225, 1259, 1346, 1588, 1616, 1635, 1699, 1746, 1817, 2021, 2097, 2099, 2100, 2104, 2108, 2113, 2121, 2130, 2133, 2135, 2136, 2137, 2139, 2141, 2143, 2144, 2145, 2146, 2149, 2158, 2159, 2160, 2165, 2170, 2172, 2175, 2177, 2184, 2190, 2192, 2193, 2194, 2196, 2197, 2202, 2204], "ctx": [2, 6, 48, 70, 74, 918, 919, 920, 929, 930, 931, 932, 933, 934, 935, 2104, 2129, 2136, 2137, 2155, 2203], "gradcheck": [2, 1970, 2096, 2104, 2136, 2170], "extend": [2, 24, 29, 31, 35, 38, 42, 53, 68, 890, 919, 921, 929, 932, 934, 935, 1584, 1593, 1970, 2037, 2096, 2130, 2143, 2145, 2146, 2152, 2155, 2156, 2158, 2163, 2170, 2190, 2191, 2205], "staticmethod": [2, 70, 919, 921, 929, 930, 931, 932, 933, 934, 935, 2100, 2129, 2136, 2155, 2165], "result": [2, 4, 5, 6, 8, 9, 10, 13, 15, 19, 20, 24, 25, 29, 31, 35, 36, 38, 40, 43, 48, 51, 55, 56, 57, 59, 64, 65, 67, 68, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 152, 220, 221, 253, 254, 314, 334, 397, 495, 497, 498, 555, 582, 583, 584, 586, 587, 617, 696, 697, 698, 699, 700, 703, 705, 706, 707, 709, 770, 805, 809, 894, 906, 908, 927, 928, 937, 938, 939, 940, 941, 942, 948, 949, 957, 969, 972, 983, 987, 992, 998, 1000, 1001, 1003, 1004, 1013, 1014, 1018, 1022, 1024, 1051, 1053, 1055, 1056, 1086, 1087, 1101, 1124, 1125, 1126, 1127, 1128, 1140, 1145, 1163, 1166, 1172, 1181, 1188, 1190, 1193, 1198, 1199, 1202, 1203, 1204, 1207, 1208, 1213, 1214, 1223, 1227, 1238, 1243, 1252, 1255, 1259, 1277, 1278, 1280, 1292, 1306, 1315, 1318, 1319, 1327, 1331, 1332, 1334, 1336, 1337, 1341, 1355, 1357, 1359, 1360, 1368, 1372, 1374, 1376, 1377, 1378, 1385, 1395, 1396, 1403, 1413, 1415, 1416, 1417, 1418, 1420, 1421, 1473, 1476, 1491, 1492, 1493, 1518, 1520, 1521, 1522, 1526, 1528, 1533, 1547, 1552, 1582, 1588, 1598, 1626, 1627, 1628, 1629, 1630, 1634, 1670, 1688, 1699, 1755, 1758, 1759, 1772, 1789, 1792, 1825, 1828, 1829, 1830, 1831, 1837, 1840, 1875, 1880, 1883, 1885, 1892, 1893, 1896, 1902, 1915, 1921, 1936, 1951, 1963, 1974, 1976, 1978, 1982, 1989, 1991, 1992, 1993, 1996, 1997, 1998, 2010, 2014, 2015, 2020, 2021, 2023, 2024, 2026, 2036, 2039, 2043, 2044, 2048, 2069, 2082, 2097, 2098, 2099, 2100, 2104, 2107, 2108, 2110, 2119, 2120, 2121, 2128, 2129, 2130, 2131, 2132, 2133, 2135, 2136, 2137, 2138, 2141, 2142, 2144, 2146, 2147, 2148, 2149, 2151, 2152, 2153, 2155, 2157, 2159, 2160, 2161, 2162, 2165, 2166, 2169, 2170, 2172, 2173, 2175, 2176, 2177, 2181, 2182, 2184, 2189, 2190, 2191, 2193, 2197, 2200, 2201, 2202, 2203, 2204, 2205], "save_for_backward": [2, 919, 929, 931, 933, 934, 935, 2104, 2129, 2130, 2136, 2137, 2155], "grad_output": [2, 70, 918, 929, 930, 934, 935, 943, 949, 953, 954, 1315, 1582, 1880, 2098, 2130, 2133, 2136, 2137, 2144, 2198], "saved_tensor": [2, 929, 931, 932, 933, 934, 935, 2104, 2129, 2130, 2136, 2137], "inspect": [2, 29, 55, 59, 68, 960, 1315, 1327, 2129, 2136, 2143, 2158, 2161, 2173, 2194, 2196, 2197, 2203, 2205], "cost": [2, 4, 5, 9, 10, 25, 31, 33, 35, 36, 59, 485, 991, 1001, 1371, 1501, 1509, 1510, 1511, 1512, 1513, 1514, 1663, 1664, 1665, 1666, 1667, 1668, 1672, 1740, 1841, 1935, 2133, 2139, 2148, 2161, 2166, 2173, 2188, 2199, 2203, 2204], "both": [2, 3, 4, 15, 20, 24, 25, 26, 29, 30, 31, 33, 38, 43, 48, 49, 50, 51, 55, 56, 57, 59, 67, 68, 70, 78, 79, 96, 338, 498, 739, 778, 779, 782, 783, 784, 805, 834, 880, 921, 928, 934, 935, 938, 939, 940, 941, 942, 969, 1026, 1064, 1101, 1140, 1146, 1147, 1148, 1161, 1191, 1192, 1193, 1202, 1257, 1259, 1269, 1276, 1292, 1305, 1306, 1312, 1315, 1329, 1337, 1338, 1386, 1402, 1410, 1416, 1434, 1435, 1491, 1492, 1496, 1497, 1498, 1506, 1509, 1510, 1511, 1512, 1513, 1514, 1528, 1536, 1542, 1544, 1545, 1546, 1553, 1554, 1555, 1556, 1557, 1558, 1559, 1560, 1561, 1562, 1563, 1564, 1565, 1566, 1575, 1576, 1577, 1582, 1588, 1622, 1630, 1634, 1635, 1638, 1655, 1656, 1657, 1663, 1664, 1665, 1666, 1667, 1668, 1713, 1714, 1715, 1740, 1772, 1783, 1785, 1795, 1825, 1841, 1843, 1844, 1862, 1875, 1880, 1900, 1901, 1939, 1944, 1969, 1971, 1973, 1993, 1997, 2020, 2021, 2060, 2097, 2099, 2100, 2104, 2107, 2119, 2120, 2121, 2122, 2126, 2129, 2130, 2131, 2136, 2137, 2139, 2140, 2141, 2144, 2147, 2148, 2149, 2151, 2155, 2157, 2158, 2160, 2163, 2164, 2165, 2167, 2170, 2171, 2172, 2177, 2180, 2181, 2182, 2188, 2190, 2192, 2193, 2194, 2195, 2196, 2200, 2203, 2204], "cpu": [2, 5, 6, 15, 18, 24, 29, 31, 33, 36, 51, 56, 59, 70, 72, 75, 79, 80, 81, 85, 86, 118, 195, 288, 325, 334, 444, 445, 446, 447, 448, 457, 485, 579, 588, 683, 894, 909, 962, 964, 970, 979, 1024, 1027, 1028, 1039, 1051, 1052, 1053, 1055, 1146, 1148, 1158, 1163, 1181, 1193, 1197, 1199, 1200, 1264, 1266, 1267, 1268, 1273, 1274, 1291, 1315, 1323, 1325, 1326, 1335, 1345, 1347, 1351, 1352, 1353, 1354, 1356, 1358, 1361, 1363, 1364, 1370, 1373, 1376, 1379, 1380, 1386, 1387, 1402, 1405, 1416, 1428, 1444, 1518, 1524, 1582, 1755, 1772, 1778, 1779, 1780, 1787, 1816, 1817, 1831, 1834, 1842, 1880, 1895, 1904, 1906, 1908, 1910, 1911, 1935, 1940, 1941, 1942, 1944, 1949, 1950, 1951, 1952, 1953, 1954, 1955, 1956, 1957, 1958, 1959, 1963, 1973, 1979, 1980, 1981, 1982, 1983, 1984, 1997, 2014, 2015, 2016, 2025, 2027, 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2173, 2185, 2189, 2190, 2191, 2192, 2194, 2195, 2203, 2204, 2206], "test": [2, 4, 15, 19, 20, 29, 48, 50, 51, 68, 70, 78, 79, 703, 709, 1001, 1162, 1164, 1165, 1170, 1171, 1176, 1177, 1178, 1180, 1182, 1229, 1237, 1243, 1248, 1249, 1250, 1255, 1301, 1302, 1306, 1307, 1309, 1310, 1837, 1880, 1960, 2096, 2097, 2101, 2113, 2118, 2130, 2139, 2141, 2148, 2157, 2159, 2175, 2181, 2185, 2193, 2203, 2204, 2205], "slow": [2, 40, 948, 1325, 1329, 1345, 1346, 1778, 1779, 1780, 1787, 1823, 2032, 2133, 2141, 2146, 2175, 2190, 2193, 2204], "import": [2, 3, 4, 6, 8, 10, 13, 15, 18, 19, 21, 24, 25, 29, 30, 31, 34, 35, 37, 38, 39, 42, 44, 47, 48, 53, 55, 56, 57, 59, 62, 63, 64, 65, 67, 68, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 680, 744, 751, 752, 753, 782, 783, 784, 804, 889, 890, 891, 944, 951, 953, 980, 987, 1014, 1026, 1056, 1196, 1199, 1202, 1203, 1204, 1206, 1207, 1208, 1209, 1210, 1212, 1312, 1313, 1316, 1318, 1320, 1321, 1322, 1323, 1325, 1326, 1327, 1331, 1332, 1333, 1367, 1368, 1371, 1372, 1385, 1417, 1582, 1642, 1646, 1772, 1793, 1795, 1797, 1798, 1799, 1800, 1801, 1802, 1803, 1804, 1805, 1806, 1808, 1809, 1810, 1818, 1819, 1820, 1821, 1822, 1823, 1830, 1862, 1863, 1889, 2034, 2097, 2099, 2100, 2101, 2104, 2108, 2109, 2113, 2118, 2121, 2128, 2129, 2130, 2132, 2133, 2135, 2136, 2137, 2138, 2140, 2143, 2144, 2146, 2147, 2148, 2149, 2151, 2152, 2153, 2154, 2155, 2160, 2163, 2165, 2166, 2167, 2170, 2173, 2175, 2176, 2177, 2181, 2182, 2184, 2185, 2188, 2189, 2190, 2191, 2192, 2194, 2196, 2201, 2202, 2203, 2204, 2205, 2211], "myfunc": [2, 2137], "inp": [2, 13, 24, 29, 55, 68, 924, 927, 928, 1634, 1772, 2104, 2188, 2198, 2201, 2203, 2205], "clone": [2, 16, 24, 59, 70, 86, 253, 447, 929, 930, 933, 934, 935, 944, 945, 946, 951, 953, 954, 983, 1319, 1524, 1758, 2014, 2023, 2098, 2104, 2118, 2149, 2156, 2157, 2170, 2172, 2173, 2177, 2188, 2198], "runtimeerror": [2, 15, 29, 34, 64, 68, 86, 232, 582, 707, 929, 930, 934, 944, 982, 1056, 1193, 1300, 1315, 1345, 1346, 1347, 1355, 1356, 1357, 1359, 1363, 1369, 1376, 1381, 1382, 1408, 1468, 1582, 1836, 1880, 1933, 1971, 2036, 2097, 2099, 2100, 2119, 2121, 2126, 2129, 2131, 2138, 2148, 2150, 2155, 2160, 2164, 2170, 2173, 2185, 2188], "run_fn": [2, 6, 886, 892], "recent": [2, 8, 9, 54, 67, 944, 1014, 1300, 1971, 2099, 2100, 2108, 2136, 2147, 2148, 2170, 2177, 2200, 2203, 2204], "last": [2, 6, 7, 12, 24, 25, 30, 33, 34, 36, 37, 38, 40, 50, 55, 56, 67, 68, 314, 616, 702, 770, 849, 906, 944, 968, 970, 979, 985, 995, 1014, 1086, 1128, 1133, 1137, 1162, 1164, 1167, 1168, 1170, 1171, 1174, 1175, 1177, 1178, 1180, 1182, 1184, 1229, 1273, 1274, 1278, 1292, 1300, 1312, 1337, 1371, 1374, 1379, 1416, 1421, 1486, 1491, 1492, 1493, 1499, 1517, 1525, 1527, 1533, 1552, 1554, 1569, 1589, 1592, 1596, 1597, 1598, 1611, 1627, 1628, 1634, 1659, 1680, 1689, 1702, 1726, 1727, 1771, 1772, 1774, 1781, 1782, 1829, 1864, 1865, 1866, 1867, 1868, 1869, 1870, 1871, 1872, 1873, 1874, 1875, 1876, 1877, 1878, 1879, 1881, 1922, 1931, 1939, 1968, 1971, 1979, 1980, 1981, 1983, 1984, 1985, 1993, 1997, 2016, 2018, 2021, 2046, 2047, 2099, 2100, 2108, 2113, 2119, 2130, 2133, 2136, 2141, 2148, 2152, 2158, 2159, 2170, 2176, 2177, 2189, 2191, 2197, 2203, 2204], "stdin": [2, 29, 944, 1971, 2108, 2136, 2148, 2170], "instal": [2, 3, 4, 15, 16, 29, 36, 68, 1145, 1229, 2095, 2144, 2145, 2152, 2153, 2154, 2155, 2158, 2165, 2175, 2184, 2189, 2190, 2191, 2192, 2199, 2203], "_tensor": [2, 36, 153], "py": [2, 5, 15, 19, 29, 34, 35, 38, 42, 44, 49, 51, 56, 57, 59, 68, 889, 1056, 1772, 2095, 2097, 2100, 2106, 2121, 2135, 2141, 2143, 2146, 2155, 2158, 2159, 2160, 2166, 2184, 2185, 2187, 2189, 2190, 2191, 2192, 2193, 2194, 2196, 2197, 2200, 2202, 2203, 2204, 2206], "93": [2, 616, 1127], "retain_graph": [2, 149, 922, 943, 953, 954, 1214, 2048, 2098, 2129, 2130, 2136, 2165], "90": [2, 1129, 1879, 1923, 2104, 2115], "allow_unreach": 2, "76": 2, "_forward_cl": 2, "tmp": [2, 4, 15, 29, 48, 50, 2095, 2133, 2159, 2185, 2188, 2197, 2203], "53": [2, 480], "44": [2, 320, 444, 1148, 1493, 1551, 1577, 1823, 2192], "set_detect_anomali": 2, "behaviour": [2, 55, 703, 704, 709, 1688, 1727, 1875, 1945, 2095, 2147, 2172], "interpos": [2, 2136], "grad_fn": [2, 149, 334, 487, 909, 922, 937, 938, 939, 940, 941, 942, 951, 953, 954, 1202, 1790, 1973, 2121, 2130, 2136, 2144, 2149], "node": [2, 29, 33, 34, 40, 43, 44, 49, 50, 55, 59, 68, 70, 80, 81, 680, 812, 833, 836, 838, 839, 842, 845, 850, 866, 929, 964, 1089, 1212, 1227, 1229, 1247, 1252, 1330, 1501, 1518, 1772, 1837, 2110, 2133, 2155, 2158, 2165, 2166, 2167, 2179, 2181, 2183, 2185, 2190, 2191, 2193, 2194, 2200, 2202, 2204], "grad_mod": [2, 944, 945, 946, 2098], "least": [2, 6, 7, 9, 25, 31, 33, 38, 49, 50, 59, 259, 401, 707, 834, 972, 1133, 1135, 1136, 1145, 1186, 1187, 1197, 1228, 1269, 1277, 1278, 1312, 1338, 1361, 1388, 1410, 1472, 1486, 1772, 1817, 2095, 2100, 2120, 2128, 2130, 2131, 2133, 2138, 2139, 2161, 2167, 2170, 2171, 2172, 2177, 2194, 2196, 2203], "intermediari": [2, 15, 38, 929, 932, 934, 2130, 2141, 2191, 2194], "access": [2, 10, 15, 19, 24, 29, 31, 35, 55, 56, 67, 70, 72, 487, 556, 744, 929, 932, 934, 935, 967, 1048, 1056, 1315, 1318, 1331, 1378, 1486, 1582, 1762, 1775, 1776, 1790, 1795, 1817, 1827, 1839, 1840, 1841, 1842, 1843, 1844, 1845, 1846, 1847, 1858, 1859, 1860, 1861, 1862, 1863, 1864, 1865, 1866, 1867, 1868, 1869, 1870, 1871, 1872, 1873, 1874, 1875, 1876, 1877, 1878, 1879, 1880, 1881, 2070, 2097, 2098, 2100, 2101, 2104, 2110, 2112, 2114, 2116, 2118, 2120, 2121, 2130, 2133, 2138, 2141, 2143, 2144, 2149, 2165, 2170, 2172, 2173, 2174, 2176, 2184, 2188, 2190, 2191, 2192, 2193, 2194, 2196, 2199, 2203, 2209], "isinst": [2, 24, 38, 68, 951, 953, 954, 1302, 1313, 2098, 2100, 2130, 2136, 2144, 2157, 2158, 2170, 2194, 2202, 2204], "dir": [2, 1086, 1837, 2095, 2100, 2158], "__call__": [2, 1315, 1582, 1880, 2191], "__class__": [2, 44, 70], "__delattr__": 2, "__dir__": 2, "__doc__": 2, "__eq__": 2, "__format__": [2, 2101], "__ge__": 2, "__getattribute__": 2, "__gt__": 2, "__hash__": [2, 2101], "__init_subclass__": 2, "__le__": 2, "__lt__": [2, 2100], "__ne__": 2, "__new__": [2, 2099, 2101], "__reduce__": [2, 2158], "__reduce_ex__": 2, "__repr__": [2, 4, 2136], "__setattr__": 2, "__sizeof__": 2, "__str__": [2, 68, 2098, 2100], "__subclasshook__": 2, "_raw_saved_result": 2, "_register_hook_dict": 2, "_saved_result": [2, 2130], "metadata": [2, 4, 6, 13, 31, 43, 44, 55, 57, 70, 78, 79, 955, 1018, 1387, 1839, 1840, 1841, 1842, 1843, 1844, 1845, 1846, 1847, 1856, 1859, 1860, 1861, 1862, 1863, 1927, 2104, 2108, 2121, 2130, 2136, 2137, 2149, 2158, 2159, 2165, 2166, 2170, 2172, 2175, 2193, 2202], "next_funct": 2, "register_prehook": [2, 2130], "allclos": [2, 64, 65, 68, 948, 949, 1086, 1167, 1168, 1174, 1175, 1203, 1204, 1206, 1207, 1208, 1209, 1213, 1214, 1363, 1365, 1376, 1378, 1381, 1382, 1407, 1795, 1822, 1895, 2048, 2098, 2104, 2137, 2156, 2170], "pack": [2, 34, 748, 749, 750, 751, 752, 753, 755, 756, 770, 791, 1331, 1332, 1407, 1518, 1533, 1552, 1598, 1816, 1817, 1818, 1819, 2098, 2110, 2121, 2130, 2138, 2150, 2160, 2191], "unpack": [2, 70, 75, 76, 770, 925, 928, 1331, 1363, 1407, 1518, 1819, 1821, 1828, 1838, 2100, 2101, 2130, 2136, 2138], "hook": [2, 29, 30, 33, 34, 36, 59, 486, 487, 758, 767, 929, 932, 934, 953, 954, 957, 1089, 1315, 1518, 1582, 1763, 1764, 1765, 1766, 1767, 1768, 1769, 1770, 1772, 1791, 1797, 1798, 1799, 1800, 1801, 1802, 1803, 1804, 1808, 1813, 1824, 1827, 1839, 1840, 1841, 1842, 1843, 1844, 1845, 1846, 1847, 1850, 1851, 1852, 1853, 1854, 1855, 1859, 1860, 1861, 1862, 1863, 1880, 2096, 2122, 2135, 2136, 2143, 2151, 2152, 2157, 2158, 2160, 2192, 2193, 2194, 2195], "common": [2, 4, 9, 24, 35, 40, 50, 64, 70, 695, 805, 894, 974, 977, 982, 1003, 1021, 1140, 1145, 1190, 1191, 1192, 1193, 1239, 1257, 1338, 1387, 1467, 1496, 1497, 1498, 1501, 1622, 1670, 1763, 1764, 1766, 1767, 1768, 1769, 1770, 1839, 1840, 1841, 1842, 1843, 1844, 1845, 1846, 1847, 1856, 1859, 1860, 1861, 1862, 1863, 1915, 1927, 1994, 2039, 2100, 2105, 2120, 2121, 2130, 2133, 2136, 2137, 2138, 2144, 2146, 2149, 2158, 2169, 2170, 2171, 2172, 2174, 2177, 2188, 2189, 2190, 2194, 2199, 2201, 2202, 2203, 2204, 2206], "trade": [2, 6, 9, 33, 59, 1379, 1495, 1939, 2132, 2138, 2161], "leav": [2, 9, 40, 51, 57, 149, 922, 1222, 1320, 1333, 1793, 1796, 1839, 1840, 1842, 1843, 1844, 1845, 1847, 1859, 1860, 1861, 1862, 1988, 2097, 2099, 2100, 2108, 2130, 2133, 2152, 2158, 2161, 2191, 2203], "especi": [2, 10, 12, 24, 29, 51, 56, 68, 253, 894, 983, 1001, 1012, 1758, 2099, 2108, 2130, 2136, 2137, 2147, 2149, 2160, 2165, 2170, 2188, 2193, 2194, 2196, 2203], "notic": [2, 13, 29, 56, 69, 699, 833, 1165, 1179, 1223, 1410, 1420, 1494, 1635, 1705, 1865, 1866, 1872, 1873, 1879, 2032, 2096, 2097, 2125, 2130, 2170, 2185, 2191, 2194, 2197, 2199, 2201], "fit": [2, 10, 42, 63, 64, 498, 961, 1312, 1846, 1917, 2133, 2152, 2177, 2204], "evalu": [2, 5, 9, 10, 33, 34, 38, 55, 57, 62, 68, 703, 709, 811, 892, 948, 1013, 1101, 1209, 1210, 1219, 1227, 1229, 1254, 1315, 1486, 1490, 1496, 1497, 1498, 1519, 1536, 1544, 1545, 1546, 1554, 1582, 1596, 1601, 1622, 1732, 1740, 1755, 1793, 1846, 1868, 1880, 1902, 2100, 2101, 2136, 2144, 2151, 2152, 2170, 2179, 2182, 2190, 2192, 2193], "saved_tensors_hook": [2, 929, 932, 934, 2130], "pack_hook": [2, 2130], "unpack_hook": [2, 2130], "pair": [2, 29, 31, 37, 38, 50, 53, 55, 616, 745, 894, 968, 989, 1026, 1179, 1225, 1229, 1329, 1379, 1417, 1574, 1583, 1588, 1592, 1729, 1861, 1911, 2099, 2100, 2106, 2119, 2126, 2130, 2133, 2165, 2166, 2167, 2175, 2177, 2181, 2194, 2201, 2203], "retriev": [2, 24, 25, 29, 34, 35, 50, 68, 86, 536, 918, 919, 934, 935, 1056, 1227, 1246, 1524, 1528, 1634, 1679, 1772, 1817, 1837, 1866, 1971, 2121, 2130, 2143, 2155, 2158, 2165, 2166, 2167, 2184, 2192, 2206], "everytim": 2, "store": [2, 4, 6, 15, 19, 25, 31, 35, 40, 44, 51, 55, 56, 57, 59, 68, 325, 331, 397, 696, 840, 845, 919, 934, 981, 1051, 1053, 1055, 1231, 1259, 1315, 1321, 1323, 1326, 1346, 1357, 1358, 1359, 1388, 1393, 1405, 1496, 1497, 1498, 1524, 1582, 1611, 1772, 1789, 1792, 1805, 1806, 1807, 1809, 1810, 1811, 1812, 1816, 1880, 1939, 1976, 2095, 2097, 2104, 2108, 2120, 2121, 2122, 2135, 2136, 2137, 2138, 2143, 2157, 2158, 2160, 2165, 2166, 2167, 2170, 2172, 2175, 2176, 2181, 2184, 2185, 2191, 2192, 2193, 2196, 2208], "content": [2, 4, 8, 20, 31, 36, 43, 55, 56, 68, 929, 932, 934, 953, 1323, 1326, 1346, 1357, 1359, 1364, 1377, 1839, 1840, 1841, 1842, 1843, 1844, 1845, 1846, 1847, 1856, 1859, 1860, 1861, 1862, 1863, 1988, 2020, 2041, 2095, 2100, 2111, 2118, 2130, 2133, 2146, 2157, 2165, 2174, 2175, 2176, 2188, 2199, 2203, 2204], "equal": [2, 13, 20, 25, 29, 38, 50, 51, 53, 55, 68, 259, 498, 544, 616, 680, 704, 705, 706, 749, 750, 751, 752, 753, 770, 778, 779, 838, 839, 840, 845, 890, 905, 970, 977, 979, 990, 991, 1015, 1026, 1053, 1055, 1072, 1115, 1134, 1150, 1189, 1204, 1205, 1222, 1225, 1229, 1239, 1240, 1255, 1258, 1273, 1274, 1276, 1277, 1278, 1300, 1304, 1340, 1361, 1374, 1378, 1381, 1382, 1405, 1406, 1417, 1439, 1479, 1483, 1484, 1485, 1487, 1488, 1489, 1494, 1495, 1501, 1510, 1511, 1513, 1514, 1518, 1525, 1528, 1529, 1530, 1533, 1535, 1552, 1587, 1588, 1596, 1598, 1634, 1643, 1656, 1657, 1661, 1664, 1667, 1679, 1680, 1683, 1684, 1690, 1697, 1772, 1820, 1880, 1883, 1896, 1952, 1953, 1958, 1985, 1993, 2015, 2031, 2037, 2051, 2098, 2101, 2107, 2119, 2120, 2121, 2130, 2131, 2136, 2141, 2147, 2156, 2157, 2171, 2172, 2175, 2177, 2191, 2202], "term": [2, 9, 10, 38, 50, 56, 57, 68, 557, 744, 773, 804, 991, 1013, 1161, 1162, 1163, 1164, 1165, 1166, 1168, 1176, 1177, 1178, 1181, 1193, 1229, 1236, 1347, 1486, 1494, 1535, 1542, 1552, 1553, 1587, 1596, 1614, 1685, 1697, 1732, 1744, 1772, 1789, 1797, 1798, 1799, 1800, 1801, 1802, 1803, 1804, 1839, 1840, 1841, 1842, 1843, 1844, 1845, 1847, 1859, 1860, 1863, 1915, 1951, 1959, 2096, 2100, 2121, 2129, 2130, 2136, 2137, 2138, 2141, 2146, 2155, 2157, 2158, 2160, 2166, 2170, 2182, 2190, 2191, 2194, 2203], "mulbackward0": [2, 938, 941, 942, 2136], "inplac": [2, 31, 55, 62, 64, 68, 70, 78, 79, 765, 768, 786, 788, 790, 803, 810, 828, 833, 865, 867, 868, 886, 887, 892, 929, 930, 934, 955, 1001, 1203, 1315, 1490, 1500, 1519, 1520, 1521, 1522, 1523, 1526, 1538, 1539, 1540, 1568, 1581, 1582, 1601, 1602, 1603, 1610, 1612, 1625, 1654, 1662, 1673, 1674, 1675, 1676, 1677, 1681, 1692, 1693, 1694, 1703, 1719, 1734, 1735, 1738, 1741, 1743, 1753, 1839, 1840, 1841, 1842, 1843, 1844, 1845, 1846, 1847, 1851, 1852, 1859, 1860, 1861, 1862, 1863, 1880, 2036, 2098, 2107, 2130, 2136, 2155, 2181, 2198, 2202], "lead": [2, 6, 8, 23, 24, 29, 39, 56, 59, 65, 68, 894, 929, 930, 934, 937, 939, 943, 949, 1003, 1196, 1214, 1219, 1229, 1346, 1552, 1598, 1614, 1880, 1982, 1988, 2048, 2100, 2104, 2120, 2121, 2130, 2132, 2135, 2136, 2137, 2139, 2146, 2147, 2150, 2157, 2158, 2160, 2165, 2170, 2175, 2176, 2188, 2195, 2203], "undefin": [2, 29, 36, 39, 43, 318, 470, 616, 907, 929, 933, 934, 935, 948, 949, 977, 985, 1003, 1148, 1198, 1199, 1415, 1494, 1836, 1889, 2104, 2126, 2130, 2133, 2136, 2137, 2148, 2165], "recurs": [2, 33, 38, 55, 57, 59, 68, 1003, 1007, 1137, 1227, 1236, 1255, 1315, 1327, 1329, 1582, 1781, 1782, 1866, 1878, 1880, 2099, 2136, 2144, 2158, 2165, 2191, 2192, 2194, 2195, 2203], "inner": [2, 4, 59, 60, 65, 829, 831, 937, 1128, 1204, 1208, 1213, 1226, 1234, 2021, 2098, 2136, 2156, 2165, 2181, 2203], "save_on_cpu": 2, "pin_memori": [2, 24, 33, 70, 72, 75, 79, 80, 81, 444, 445, 446, 447, 448, 1146, 1148, 1197, 1904, 1908, 1910, 1979, 1980, 1981, 1982, 1983, 1984, 2014, 2097, 2098, 2104, 2121, 2133, 2136, 2156, 2172, 2198], "within": [2, 6, 10, 13, 20, 24, 25, 29, 34, 35, 36, 37, 38, 50, 51, 53, 55, 56, 59, 67, 68, 86, 87, 487, 907, 924, 948, 949, 955, 1001, 1018, 1044, 1045, 1128, 1218, 1219, 1227, 1233, 1315, 1319, 1408, 1445, 1446, 1491, 1492, 1493, 1501, 1520, 1521, 1522, 1526, 1528, 1575, 1576, 1577, 1582, 1588, 1622, 1634, 1688, 1698, 1713, 1714, 1715, 1772, 1793, 1797, 1798, 1799, 1800, 1801, 1802, 1803, 1804, 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2108, 2149, 2152, 2157, 2158, 2160, 2165, 2170, 2175, 2181, 2202, 2204], "bla": [3, 20, 2132], "choos": [3, 10, 16, 20, 68, 921, 934, 935, 961, 1013, 1066, 1361, 1374, 1379, 1494, 1998, 2128, 2129, 2132, 2152, 2158, 2161, 2175, 2184], "cubla": [3, 12, 18, 20, 21, 1057, 2036, 2148, 2188], "cublaslt": [3, 18, 21], "ck": 3, "rocm": [3, 18, 696, 699, 969, 981, 1085, 1410, 1420, 1509, 1510, 1511, 1512, 1513, 1514, 1534, 1553, 1569, 2096], "subject": [3, 4, 12, 19, 20, 24, 29, 31, 34, 36, 37, 59, 67, 68, 69, 232, 894, 1641, 1642, 1643, 1646, 1740, 1772, 2100, 2119, 2120, 2125, 2130, 2136, 2149, 2155, 2159, 2160, 2161, 2165, 2170, 2172, 2173, 2179, 2180, 2181, 2184, 2190, 2200], "hipbla": [3, 18, 20], "hipblaslt": [3, 18, 20], "offer": [3, 29, 31, 33, 36, 59, 62, 1588, 1626, 1627, 1628, 1629, 1630, 1772, 2036, 2133, 2136, 2142, 2153, 2158, 2159, 2165, 2170, 2189, 2194, 2198, 2207], "wherev": [3, 10, 2119], "prefer": [3, 6, 10, 24, 29, 31, 40, 51, 59, 891, 907, 939, 1219, 1242, 1313, 1356, 1369, 1373, 1381, 1388, 1589, 1740, 1931, 1993, 2014, 2097, 2104, 2108, 2130, 2133, 2147, 2155, 2157, 2158, 2170, 2194, 2203], "environ": [3, 4, 8, 15, 16, 19, 20, 23, 25, 31, 35, 36, 38, 40, 43, 48, 54, 55, 57, 68, 680, 1056, 1227, 1229, 1252, 1325, 1331, 1552, 1598, 2036, 2095, 2096, 2097, 2106, 2130, 2132, 2133, 2135, 2142, 2147, 2148, 2150, 2165, 2183, 2184, 2185, 2187, 2189, 2194, 2203, 2204], "variabl": [3, 4, 6, 13, 15, 19, 20, 23, 25, 33, 35, 36, 38, 40, 43, 48, 50, 54, 56, 57, 59, 64, 68, 70, 73, 78, 79, 447, 680, 722, 723, 724, 725, 726, 727, 728, 729, 730, 733, 734, 735, 736, 738, 739, 740, 741, 742, 744, 748, 749, 750, 751, 752, 753, 755, 756, 766, 770, 774, 813, 843, 844, 846, 929, 930, 934, 1003, 1018, 1022, 1026, 1114, 1146, 1214, 1229, 1329, 1331, 1388, 1499, 1509, 1510, 1511, 1512, 1513, 1514, 1518, 1524, 1525, 1533, 1534, 1552, 1553, 1554, 1567, 1569, 1582, 1585, 1590, 1598, 1600, 1772, 1816, 1817, 1818, 1819, 1820, 1821, 1822, 1834, 1838, 1840, 1864, 1865, 1866, 1867, 1869, 1870, 1871, 1872, 1873, 1874, 1875, 1876, 1878, 1879, 1881, 1904, 1906, 1908, 2036, 2048, 2093, 2095, 2096, 2106, 2107, 2120, 2121, 2130, 2132, 2133, 2135, 2138, 2141, 2142, 2147, 2148, 2150, 2152, 2155, 2157, 2160, 2165, 2172, 2175, 2183, 2184, 2185, 2190, 2191, 2192, 2194, 2196, 2203, 2204], "torch_blas_prefer_cublaslt": 3, "global": [3, 4, 6, 9, 12, 13, 21, 24, 29, 31, 34, 36, 37, 38, 40, 51, 59, 64, 67, 68, 70, 73, 78, 79, 883, 890, 894, 902, 957, 970, 979, 1018, 1037, 1063, 1077, 1095, 1103, 1146, 1148, 1158, 1163, 1181, 1197, 1200, 1203, 1273, 1274, 1296, 1303, 1315, 1318, 1331, 1335, 1386, 1402, 1582, 1740, 1764, 1765, 1766, 1767, 1768, 1769, 1770, 1772, 1802, 1806, 1834, 1880, 1904, 1906, 1908, 1911, 1935, 1949, 1950, 1951, 1952, 1953, 1954, 1955, 1956, 1957, 1958, 1959, 2078, 2093, 2097, 2100, 2101, 2104, 2112, 2122, 2129, 2136, 2143, 2144, 2146, 2148, 2160, 2165, 2166, 2167, 2170, 2173, 2175, 2191, 2192, 2193, 2194, 2195, 2202, 2204], "overridden": [3, 6, 15, 68, 918, 919, 920, 934, 935, 1086, 1582, 1971, 2100, 2130, 2136, 2147, 2204, 2205], "achiev": [3, 20, 24, 25, 29, 31, 37, 38, 51, 59, 1145, 1210, 1315, 1495, 1501, 1582, 1588, 1589, 1661, 1690, 1772, 1880, 2114, 2133, 2143, 2146, 2158, 2165, 2167, 2172, 2204], "better": [3, 4, 8, 9, 10, 15, 24, 28, 29, 40, 55, 63, 944, 1001, 1114, 1202, 1206, 1207, 1223, 1302, 1331, 1517, 1616, 1740, 1746, 1772, 1781, 1782, 1875, 1880, 1902, 2100, 2121, 2129, 2130, 2132, 2133, 2139, 2141, 2148, 2150, 2155, 2157, 2159, 2160, 2161, 2170, 2175, 2181, 2184, 2189, 2191, 2192, 2200, 2203], "select": [3, 6, 16, 18, 20, 22, 24, 29, 31, 38, 40, 48, 56, 312, 314, 316, 320, 684, 685, 686, 770, 991, 1028, 1030, 1034, 1045, 1046, 1050, 1058, 1059, 1060, 1061, 1063, 1064, 1072, 1088, 1092, 1094, 1095, 1097, 1099, 1101, 1102, 1103, 1108, 1109, 1110, 1111, 1114, 1115, 1118, 1120, 1122, 1123, 1165, 1388, 1417, 1446, 1447, 1448, 1449, 1450, 1461, 1463, 1465, 1501, 1509, 1510, 1511, 1512, 1513, 1514, 1533, 1552, 1598, 1646, 1663, 1664, 1665, 1666, 1667, 1668, 1672, 1740, 1811, 1812, 1934, 2011, 2039, 2051, 2055, 2056, 2057, 2058, 2060, 2076, 2077, 2078, 2079, 2080, 2081, 2083, 2084, 2087, 2090, 2091, 2097, 2098, 2100, 2118, 2119, 2120, 2121, 2130, 2132, 2133, 2141, 2148, 2151, 2156, 2160, 2170, 2174, 2177, 2192, 2198, 2200, 2203, 2206], "incorrect": [3, 5, 6, 59, 68, 253, 512, 555, 929, 932, 934, 948, 957, 983, 1003, 1013, 1331, 1374, 1387, 1588, 1626, 1627, 1628, 1629, 1630, 1758, 1830, 1982, 1993, 2097, 2100, 2104, 2133, 2147, 2155, 2191, 2207], "_blasbackend": 3, "preferred_rocm_fa_librari": 3, "flash": [3, 1641, 1646], "attent": [3, 8, 36, 37, 745, 1495, 1588, 1626, 1628, 1630, 1641, 1643, 1661, 1740, 2096, 2150, 2174, 2191], "aotriton": 3, "experimeent": 3, "composable_kernel": 3, "torch_rocm_fa_prefer_ck": 3, "_rocmfabackend": 3, "preferred_linalg_librari": [3, 1352], "heurist": [3, 15, 24, 33, 50, 51, 68, 1006, 1145, 2139, 2188, 2197], "cusolv": [3, 1379, 1380, 1997], "magma": [3, 1361, 1405, 1895, 1997, 2150, 2170], "algebra": [3, 10, 1145, 1352, 1373, 2105], "decid": [3, 5, 8, 29, 40, 50, 57, 63, 485, 1225, 1806, 2107, 2155, 2170, 2193, 2194, 2197], "pick": [3, 29, 49, 51, 55, 512, 1001, 2130, 2165, 2190, 2192, 2197], "torch_linalg_prefer_cusolv": 3, "linalg": [3, 12, 405, 991, 992, 993, 994, 1036, 1131, 1259, 1293, 1396, 1405, 1406, 1407, 1411, 1412, 1789, 1790, 1795, 1830, 1882, 1887, 1889, 1895, 1965, 1977, 1997, 1998, 2023, 2045, 2096], "inv": [3, 38, 1293, 1347, 1351, 1357, 1373, 1377, 1381], "inv_ex": [3, 1356], "cholesky_ex": [3, 1345], "lu_factor": [3, 1364, 1365, 1405, 1406, 1407], "lu": [3, 11, 1357, 1363, 1364, 1365, 1406, 1407, 2098], "eigh": [3, 1345, 1351, 1354, 1373, 1379, 2147], "eighval": 3, "svdval": [3, 1347, 1361, 1370, 1379, 1997, 2147], "_linalgbackend": 3, "sdpaparam": 3, "flash_sdp_en": 3, "scale": [3, 8, 24, 35, 38, 40, 49, 51, 96, 172, 472, 474, 616, 695, 699, 700, 701, 748, 749, 750, 751, 752, 753, 754, 759, 760, 761, 762, 763, 764, 765, 766, 769, 780, 782, 783, 784, 785, 787, 790, 791, 807, 813, 816, 834, 836, 837, 838, 839, 840, 845, 853, 969, 1159, 1160, 1163, 1181, 1213, 1214, 1402, 1490, 1519, 1524, 1525, 1526, 1542, 1554, 1610, 1635, 1636, 1637, 1641, 1646, 1679, 1680, 1681, 1697, 1699, 1740, 1741, 1746, 1780, 1840, 1841, 1868, 1897, 1898, 1899, 1900, 1901, 1974, 1975, 1994, 2048, 2096, 2098, 2120, 2121, 2126, 2128, 2133, 2138, 2140, 2144, 2160, 2161, 2163, 2171, 2194], "product": [3, 16, 29, 31, 36, 38, 50, 149, 696, 699, 700, 701, 770, 922, 927, 938, 940, 941, 942, 943, 969, 981, 987, 991, 1022, 1036, 1050, 1103, 1108, 1122, 1123, 1126, 1142, 1145, 1209, 1213, 1214, 1292, 1336, 1348, 1355, 1381, 1382, 1384, 1410, 1417, 1420, 1470, 1533, 1534, 1552, 1553, 1641, 1646, 1687, 1740, 1789, 1883, 1884, 1893, 1939, 2016, 2031, 2045, 2048, 2097, 2119, 2121, 2126, 2130, 2141, 2143, 2158, 2160, 2170, 2191, 2203, 2206], "enable_mem_efficient_sdp": [3, 1740], "mem_efficient_sdp_en": 3, "enable_flash_sdp": [3, 1740], "math_sdp_en": 3, "math": [3, 24, 68, 1155, 1367, 1641, 1646, 1700, 1740, 1885, 1902, 1998, 2097, 2099, 2100, 2115, 2139, 2147, 2170, 2171, 2177, 2185, 2204], "enable_math_sdp": [3, 1740], "fp16_bf16_reduction_math_sdp_allow": 3, "allow_fp16_bf16_reduction_math_sdp": [3, 2147], "cudnn_sdp_en": 3, "enable_cudnn_sdp": 3, "is_flash_attention_avail": 3, "check": [3, 4, 5, 6, 12, 15, 20, 21, 24, 29, 30, 31, 36, 38, 50, 59, 70, 71, 76, 79, 85, 87, 220, 232, 339, 498, 683, 688, 704, 890, 929, 930, 932, 933, 934, 935, 948, 949, 955, 1003, 1014, 1039, 1040, 1044, 1048, 1081, 1101, 1225, 1229, 1237, 1238, 1248, 1249, 1250, 1302, 1312, 1315, 1331, 1332, 1345, 1346, 1351, 1352, 1354, 1357, 1359, 1364, 1370, 1373, 1377, 1379, 1380, 1405, 1432, 1444, 1445, 1555, 1556, 1557, 1558, 1559, 1560, 1561, 1562, 1563, 1564, 1565, 1566, 1567, 1582, 1762, 1772, 1792, 1795, 1808, 1818, 1837, 1838, 1880, 1971, 1979, 1980, 1981, 1982, 1983, 1984, 2037, 2053, 2054, 2081, 2095, 2099, 2100, 2101, 2104, 2110, 2119, 2120, 2129, 2133, 2136, 2137, 2138, 2139, 2143, 2144, 2145, 2148, 2149, 2152, 2155, 2157, 2158, 2159, 2160, 2165, 2170, 2172, 2175, 2177, 2187, 2188, 2190, 2191, 2192, 2193, 2194, 2195, 2196, 2197, 2202, 2203, 2204, 2205, 2208], "flashattent": [3, 1630, 1740], "depend": [3, 5, 6, 13, 15, 24, 29, 31, 33, 34, 35, 36, 38, 40, 50, 51, 53, 56, 59, 68, 85, 314, 795, 907, 953, 1050, 1103, 1108, 1122, 1123, 1166, 1168, 1176, 1177, 1178, 1204, 1208, 1213, 1229, 1239, 1241, 1242, 1243, 1246, 1327, 1331, 1351, 1352, 1379, 1405, 1410, 1444, 1494, 1495, 1515, 1517, 1525, 1528, 1541, 1547, 1548, 1573, 1574, 1585, 1586, 1587, 1589, 1596, 1614, 1615, 1631, 1634, 1635, 1653, 1660, 1661, 1671, 1680, 1688, 1700, 1724, 1732, 1740, 1759, 1762, 1772, 1795, 1796, 1802, 1868, 1883, 1885, 1902, 1918, 1979, 1980, 1981, 1983, 1984, 1997, 2023, 2051, 2095, 2097, 2099, 2100, 2104, 2107, 2108, 2118, 2126, 2130, 2132, 2133, 2136, 2137, 2139, 2141, 2144, 2146, 2151, 2154, 2155, 2159, 2160, 2165, 2170, 2176, 2177, 2182, 2184, 2188, 2189, 2190, 2191, 2193, 2194, 2196, 2199, 2201, 2202, 2206], "can_use_flash_attent": 3, "debug": [3, 5, 6, 9, 20, 21, 24, 35, 41, 47, 55, 680, 812, 833, 851, 854, 858, 872, 948, 949, 1001, 1003, 1011, 1037, 1076, 1119, 1263, 1345, 1765, 1766, 1767, 1768, 1938, 1945, 2099, 2106, 2130, 2132, 2133, 2135, 2142, 2143, 2144, 2148, 2150, 2155, 2158, 2180, 2190, 2191, 2192, 2195, 2196, 2199, 2206, 2207, 2208], "util": [3, 8, 18, 31, 51, 55, 56, 57, 59, 60, 67, 68, 70, 498, 742, 745, 748, 749, 750, 755, 756, 766, 770, 774, 802, 811, 935, 1055, 1146, 1147, 1148, 1196, 1238, 1248, 1249, 1250, 1253, 1315, 1533, 1552, 1582, 1598, 1599, 1763, 1764, 1765, 1766, 1767, 1768, 1769, 1770, 1772, 1792, 1797, 1798, 1799, 1800, 1801, 1802, 1803, 1804, 1816, 1839, 1840, 1841, 1842, 1843, 1844, 1845, 1846, 1847, 1850, 1851, 1852, 1853, 1854, 1855, 1859, 1860, 1861, 1862, 1863, 1868, 1875, 1880, 2001, 2003, 2005, 2006, 2007, 2036, 2096, 2100, 2104, 2108, 2113, 2121, 2129, 2131, 2132, 2133, 2136, 2138, 2140, 2141, 2143, 2144, 2146, 2148, 2153, 2155, 2158, 2184, 2190, 2191, 2194, 2201, 2204, 2207], "_sdpaparam": 3, "kei": [3, 4, 24, 31, 35, 40, 48, 50, 51, 55, 56, 57, 59, 66, 68, 414, 602, 680, 745, 812, 883, 888, 956, 961, 968, 1111, 1202, 1212, 1230, 1315, 1331, 1332, 1387, 1582, 1583, 1588, 1592, 1626, 1627, 1628, 1629, 1630, 1643, 1740, 1809, 1825, 1839, 1840, 1841, 1842, 1843, 1844, 1845, 1846, 1847, 1849, 1859, 1860, 1861, 1862, 1863, 1880, 2037, 2039, 2083, 2084, 2098, 2099, 2100, 2104, 2112, 2126, 2133, 2136, 2143, 2144, 2149, 2155, 2156, 2157, 2159, 2160, 2163, 2165, 2166, 2172, 2175, 2177, 2180, 2182, 2195, 2200, 2201, 2202, 2207], "mask": [3, 48, 398, 399, 400, 401, 402, 543, 745, 948, 949, 1409, 1490, 1501, 1526, 1588, 1626, 1627, 1628, 1629, 1630, 1681, 1740, 1797, 1798, 1799, 1800, 1801, 1802, 1803, 1804, 1805, 1806, 1807, 1809, 1810, 1811, 1812, 1863, 2096, 2098, 2104, 2119, 2120, 2121, 2126, 2170, 2194, 2198], "dropout": [3, 6, 37, 68, 744, 745, 770, 1315, 1490, 1520, 1521, 1522, 1526, 1533, 1552, 1582, 1588, 1598, 1599, 1626, 1628, 1630, 1654, 1674, 1675, 1676, 1681, 1740, 1880, 2098, 2110, 2120, 2130, 2133, 2156, 2162], "causal": [3, 745, 1588, 1626, 1627, 1628, 1629, 1630, 1642, 1643, 1644, 1645, 1740, 2126], "warn": [3, 4, 15, 19, 20, 25, 26, 29, 34, 35, 36, 55, 680, 937, 943, 991, 1050, 1103, 1108, 1119, 1122, 1123, 1214, 1229, 1296, 1312, 1331, 1373, 1387, 1417, 1588, 1626, 1627, 1628, 1629, 1630, 1740, 1772, 1792, 1795, 1938, 1945, 1993, 2036, 2048, 2095, 2106, 2113, 2126, 2131, 2136, 2149, 2152, 2156, 2158, 2164, 2191, 2203, 2204], "why": [3, 4, 8, 24, 55, 64, 68, 1145, 1319, 1740, 1843, 1844, 1862, 2104, 2107, 2137, 2139, 2190, 2191, 2193, 2201, 2206], "could": [3, 5, 6, 8, 9, 18, 20, 24, 29, 35, 36, 37, 38, 40, 50, 57, 64, 67, 68, 582, 583, 584, 586, 587, 812, 957, 1081, 1166, 1168, 1176, 1177, 1178, 1230, 1312, 1315, 1321, 1323, 1346, 1387, 1405, 1434, 1435, 1582, 1781, 1782, 1867, 1880, 1973, 1998, 2032, 2099, 2100, 2107, 2118, 2130, 2133, 2135, 2149, 2150, 2153, 2155, 2158, 2160, 2161, 2165, 2166, 2167, 2170, 2174, 2175, 2189, 2191, 2194, 2195, 2199, 2203, 2204, 2207, 2208], "can_use_efficient_attent": 3, "efficient_attent": [3, 1641, 1646], "can_use_cudnn_attent": 3, "cudnn_attent": [3, 1641], "sdp_kernel": 3, "enable_flash": 3, "enable_math": 3, "enable_mem_effici": 3, "enable_cudnn": 3, "temporarili": [3, 40, 1935, 2104, 2130, 2155, 2159, 2197], "previou": [3, 19, 29, 31, 33, 50, 55, 57, 59, 68, 512, 555, 770, 1001, 1003, 1042, 1077, 1190, 1269, 1326, 1386, 1402, 1533, 1552, 1598, 1646, 1772, 1797, 1798, 1799, 1800, 1801, 1802, 1803, 1804, 1837, 1877, 1902, 1993, 2110, 2130, 2133, 2136, 2144, 2150, 2152, 2153, 2157, 2160, 2167, 2194, 2203], "restor": [3, 6, 31, 34, 36, 68, 86, 1012, 1190, 1386, 1402, 1646, 1827, 1922, 1943, 2144, 2149, 2192], "is_avail": [3, 18, 29, 683, 692, 2039, 2133, 2140, 2142, 2145, 2149, 2159, 2184, 2211], "determinist": [3, 4, 6, 23, 29, 33, 38, 68, 86, 485, 498, 512, 902, 1146, 1147, 1148, 1263, 1296, 1318, 1331, 1332, 1413, 1416, 1418, 1501, 1509, 1510, 1511, 1512, 1513, 1514, 1552, 1598, 1663, 1664, 1665, 1666, 1667, 1668, 1672, 1740, 1932, 1938, 2036, 2096, 2098, 2130, 2139, 2148, 2157, 2164], "algorithm": [3, 4, 8, 12, 20, 22, 25, 30, 34, 38, 40, 59, 86, 680, 770, 789, 795, 991, 1312, 1356, 1363, 1369, 1373, 1379, 1388, 1405, 1501, 1509, 1510, 1511, 1512, 1513, 1514, 1528, 1531, 1533, 1552, 1598, 1634, 1635, 1663, 1664, 1665, 1666, 1667, 1668, 1672, 1688, 1699, 1740, 1759, 1772, 1840, 1841, 1842, 1843, 1844, 1845, 1846, 1847, 1859, 1860, 1861, 1863, 1885, 1924, 1939, 1997, 1998, 2036, 2096, 2130, 2133, 2147, 2165, 2170], "are_deterministic_algorithms_en": [3, 2188], "use_deterministic_algorithm": [3, 28, 498, 902, 1146, 1147, 1148, 1296, 1938, 2148], "benchmark": [3, 20, 680, 2096, 2098, 2133, 2142, 2159, 2188, 2194, 2200, 2203], "fastest": [3, 18, 20, 906, 1772, 1829, 1843, 1844, 1862, 2141, 2148, 2157], "benchmark_limit": 3, "maximum": [3, 28, 38, 50, 51, 55, 498, 705, 707, 781, 807, 838, 839, 840, 845, 850, 903, 1052, 1092, 1094, 1101, 1109, 1110, 1124, 1125, 1146, 1147, 1148, 1191, 1208, 1276, 1277, 1278, 1388, 1413, 1439, 1540, 1679, 1680, 1779, 1841, 1866, 1875, 1902, 1916, 1949, 1950, 1951, 1952, 1953, 1954, 1955, 1956, 1957, 1958, 1959, 2038, 2076, 2077, 2081, 2098, 2106, 2115, 2121, 2128, 2129, 2133, 2146, 2156, 2163, 2177, 2178, 2190, 2198, 2206, 2208], "try": [3, 4, 5, 8, 9, 29, 31, 36, 37, 42, 43, 47, 50, 55, 63, 64, 232, 1001, 1003, 1115, 1199, 1208, 1214, 1229, 1254, 1320, 1322, 1331, 1352, 1439, 1501, 1509, 1510, 1511, 1512, 1513, 1514, 1626, 1627, 1629, 1635, 1641, 1663, 1664, 1665, 1666, 1667, 1668, 1672, 1740, 1772, 1839, 1840, 1842, 1843, 1844, 1845, 1846, 1847, 1859, 1860, 1861, 1862, 2048, 2095, 2097, 2100, 2101, 2104, 2130, 2133, 2136, 2138, 2141, 2146, 2155, 2157, 2158, 2160, 2161, 2165, 2170, 2189, 2190, 2193, 2194, 2196, 2203, 2204, 2206], "dispatch": [3, 15, 29, 55, 59, 68, 1215, 1216, 1230, 1320, 1434, 1435, 2037, 2039, 2100, 2104, 2133, 2136, 2152, 2155, 2194, 2195, 2205], "v8": [3, 21], "api": [3, 4, 6, 9, 10, 15, 21, 25, 29, 30, 31, 33, 37, 40, 42, 43, 47, 48, 49, 50, 51, 53, 59, 60, 63, 67, 69, 70, 75, 79, 80, 81, 232, 814, 842, 890, 924, 925, 926, 927, 928, 940, 945, 946, 990, 1003, 1006, 1013, 1037, 1056, 1077, 1078, 1079, 1086, 1087, 1089, 1118, 1149, 1189, 1203, 1206, 1207, 1209, 1214, 1227, 1239, 1326, 1327, 1329, 1439, 1463, 1772, 1825, 1827, 1828, 1836, 1924, 1935, 2029, 2039, 2048, 2070, 2090, 2095, 2106, 2107, 2108, 2114, 2115, 2118, 2119, 2121, 2125, 2130, 2135, 2140, 2149, 2151, 2161, 2165, 2166, 2170, 2173, 2175, 2179, 2182, 2184, 2185, 2192, 2196, 2203, 2205, 2208], "get_fastpath_en": 3, "fast": [3, 8, 9, 24, 29, 40, 68, 948, 1523, 1544, 1545, 1546, 1614, 1630, 1781, 1782, 1875, 1883, 1902, 1924, 1939, 2115, 2132, 2133, 2136, 2142, 2157, 2165, 2170, 2173, 2174, 2182, 2191, 2193, 2196, 2197, 2206], "path": [3, 4, 5, 9, 15, 21, 29, 31, 40, 48, 50, 53, 54, 55, 57, 59, 68, 744, 958, 959, 960, 1037, 1145, 1238, 1315, 1582, 1599, 1630, 1837, 1880, 2095, 2098, 2099, 2130, 2133, 2136, 2139, 2142, 2149, 2152, 2155, 2157, 2158, 2159, 2170, 2180, 2184, 2188, 2196, 2197], "transformerencod": 3, "multiheadattent": [3, 1626, 1628, 1630, 2160], "fastpath": [3, 1588, 2193], "condit": [3, 6, 13, 22, 24, 55, 56, 57, 64, 68, 70, 78, 79, 616, 619, 624, 704, 770, 938, 948, 949, 1013, 1018, 1312, 1331, 1345, 1347, 1352, 1355, 1361, 1362, 1363, 1374, 1379, 1533, 1552, 1598, 1630, 1818, 1823, 2023, 2051, 2097, 2098, 2099, 2101, 2109, 2130, 2136, 2147, 2175, 2190, 2191, 2192, 2193, 2198, 2203], "met": [3, 13, 616, 1018, 1355, 1362, 1363, 1374, 1388, 1630, 1982], "set_fastpath_en": 3, "verbos": [3, 15, 20, 29, 68, 680, 1229, 1865, 1866, 1867, 1868, 1869, 1870, 1871, 1872, 1873, 1874, 1875, 1876, 1877, 1878, 1879, 2095, 2106, 2115, 2120, 2155, 2175, 2204], "On": [3, 15, 23, 24, 29, 30, 33, 38, 51, 57, 59, 64, 68, 696, 699, 969, 981, 1042, 1096, 1363, 1410, 1420, 1509, 1510, 1511, 1512, 1513, 1514, 1534, 1549, 1550, 1551, 1552, 1553, 1569, 1592, 1598, 1611, 1772, 1843, 1844, 1859, 1862, 2097, 2107, 2130, 2132, 2133, 2136, 2147, 2158, 2165, 2166, 2167, 2170, 2188, 2190, 2191, 2193, 2194, 2203], "demand": [3, 24, 1079, 2099, 2121, 2143, 2146, 2165], "onemkl": 3, "easier": [3, 8, 24, 36, 55, 68, 2097, 2099, 2126, 2130, 2131, 2136, 2137, 2139, 2170, 2194, 2203, 2204], "dump": [3, 68, 1037, 2135, 2150, 2155, 2194, 2204, 2208], "durat": [3, 20, 29, 47, 50, 966, 1434, 1435, 2113, 2152, 2159, 2194], "kernel": [3, 4, 5, 12, 15, 19, 21, 29, 59, 67, 87, 485, 680, 692, 749, 750, 751, 752, 753, 782, 783, 784, 955, 1001, 1035, 1040, 1044, 1045, 1086, 1087, 1121, 1123, 1441, 1445, 1446, 1491, 1492, 1493, 1509, 1510, 1511, 1512, 1513, 1514, 1528, 1529, 1530, 1549, 1550, 1551, 1558, 1559, 1560, 1561, 1562, 1563, 1575, 1576, 1577, 1578, 1579, 1580, 1634, 1663, 1664, 1665, 1666, 1667, 1668, 1683, 1684, 1740, 1781, 1782, 1793, 2037, 2039, 2054, 2055, 2092, 2097, 2104, 2110, 2115, 2121, 2126, 2133, 2136, 2137, 2139, 2145, 2147, 2155, 2157, 2159, 2161, 2170, 2185, 2188, 2191, 2192, 2193, 2194, 2195, 2196, 2203, 2204, 2207], "mkl_verbos": 3, "methodologi": 3, "larg": [3, 4, 8, 9, 24, 29, 35, 37, 68, 680, 887, 1024, 1026, 1044, 1101, 1352, 1361, 1379, 1486, 1528, 1634, 1682, 1772, 1875, 1963, 1976, 1998, 2054, 2081, 2096, 2118, 2120, 2121, 2132, 2133, 2136, 2138, 2144, 2147, 2149, 2152, 2155, 2158, 2161, 2165, 2170, 2173, 2176, 2188, 2194, 2201, 2203, 2204, 2206], "moreov": [3, 31, 57, 512, 1772, 1862, 2207], "investig": [3, 8, 29, 64, 2201, 2203], "singl": [3, 4, 6, 15, 20, 25, 29, 31, 34, 35, 36, 37, 38, 40, 43, 48, 49, 50, 57, 59, 60, 62, 63, 64, 65, 67, 68, 70, 78, 79, 253, 680, 705, 706, 776, 777, 778, 779, 782, 783, 784, 828, 924, 937, 938, 939, 940, 941, 942, 943, 955, 970, 979, 983, 1001, 1003, 1022, 1026, 1086, 1089, 1202, 1204, 1205, 1208, 1210, 1214, 1273, 1274, 1278, 1300, 1314, 1315, 1331, 1332, 1335, 1393, 1417, 1434, 1435, 1484, 1485, 1488, 1489, 1492, 1493, 1495, 1509, 1510, 1511, 1513, 1514, 1517, 1518, 1529, 1530, 1536, 1549, 1550, 1551, 1554, 1576, 1577, 1582, 1590, 1594, 1595, 1597, 1611, 1622, 1647, 1648, 1649, 1650, 1651, 1652, 1655, 1656, 1657, 1663, 1664, 1665, 1666, 1667, 1668, 1683, 1684, 1685, 1713, 1714, 1715, 1758, 1764, 1766, 1769, 1770, 1771, 1772, 1778, 1779, 1780, 1783, 1785, 1787, 1788, 1825, 1826, 1839, 1840, 1841, 1842, 1843, 1844, 1845, 1846, 1847, 1857, 1859, 1860, 1861, 1862, 1863, 1868, 1880, 1892, 1918, 1939, 1985, 1991, 1992, 2032, 2033, 2036, 2043, 2044, 2048, 2097, 2099, 2100, 2107, 2110, 2118, 2120, 2121, 2122, 2126, 2130, 2132, 2133, 2136, 2137, 2139, 2141, 2144, 2146, 2147, 2148, 2149, 2155, 2157, 2158, 2159, 2160, 2166, 2167, 2170, 2172, 2173, 2176, 2177, 2181, 2187, 2188, 2190, 2192, 2193, 2194, 2196, 2202, 2203, 2204], "enough": [3, 9, 25, 36, 57, 68, 1229, 1236, 1327, 1632, 1979, 1980, 1981, 1982, 1983, 1984, 2036, 2099, 2118, 2121, 2133, 2136, 2141, 2157, 2158, 2173, 2191, 2194, 2203, 2205], "scope": 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1396, 1495, 2069, 2100, 2136, 2149, 2157, 2165, 2166, 2190, 2203], "workload": [4, 9, 20, 24, 29, 59, 1089, 2133, 2139, 2143, 2147, 2159, 2165, 2183, 2187, 2201], "good": [4, 8, 9, 15, 68, 1001, 1206, 1236, 1590, 1877, 1959, 2095, 2104, 2118, 2133, 2136, 2139, 2143, 2144, 2146, 2155, 2158, 2160, 2190, 2191, 2192, 2193, 2200, 2203], "intrins": [4, 710, 711, 712, 713, 714, 715, 716, 717, 718, 719, 720, 721, 722, 723, 724, 725, 726, 727, 728, 729, 730, 731, 732, 733, 734, 735, 736, 737, 738, 739, 804, 805, 2161, 2162], "contrast": [4, 38, 40, 55, 57, 770, 1225, 1533, 1841, 1862, 2128, 2130, 2137, 2139, 2170, 2198], "adaptive_autorang": 4, "threshold": [4, 25, 68, 1370, 1373, 1542, 1614, 1619, 1697, 1748, 1754, 1841, 1877, 1943, 2098, 2129, 2133, 2156, 2175], "min_run_tim": 4, "01": [4, 30, 34, 53, 487, 765, 790, 839, 840, 1146, 1188, 1568, 1703, 1704, 1762, 1837, 1839, 1840, 1841, 1842, 1843, 1844, 1845, 1846, 1847, 1856, 1859, 1860, 1861, 1862, 1863, 1868, 1875, 1897, 1950, 1952, 1953, 1958, 1959, 2098, 2128, 2143, 2155, 2157, 2198, 2203], "max_run_tim": 4, "callback": [4, 25, 29, 40, 53, 67, 68, 1772, 2113, 2136, 2143, 2159, 2165], "similar": [4, 8, 10, 12, 21, 24, 29, 33, 35, 36, 38, 47, 56, 59, 64, 67, 68, 492, 514, 722, 723, 724, 725, 726, 727, 730, 740, 741, 742, 743, 755, 756, 766, 774, 816, 906, 921, 934, 935, 982, 987, 1000, 1001, 1191, 1192, 1315, 1515, 1516, 1528, 1541, 1544, 1545, 1546, 1582, 1631, 1670, 1816, 1830, 1831, 1841, 1863, 1880, 1889, 1917, 1924, 1973, 1977, 2011, 2017, 2033, 2099, 2100, 2104, 2118, 2119, 2121, 2130, 2132, 2133, 2136, 2137, 2141, 2147, 2158, 2160, 2165, 2166, 2170, 2171, 2172, 2176, 2190, 2193, 2194, 2196, 2199, 2203, 2204, 2209], "blocked_autorang": 4, "variablil": 4, "until": [4, 6, 8, 24, 25, 29, 31, 34, 40, 50, 55, 59, 67, 68, 85, 87, 485, 1039, 1040, 1044, 1079, 1199, 1336, 1388, 1428, 1434, 1435, 1444, 1445, 1688, 1762, 1772, 1865, 1867, 1872, 2017, 2053, 2054, 2070, 2104, 2112, 2118, 2128, 2133, 2138, 2139, 2149, 2155, 2159, 2165, 2167, 2172, 2188, 2191, 2194], "iqr": 4, "smaller": [4, 20, 24, 33, 68, 498, 544, 1026, 1552, 1772, 1877, 1894, 1939, 1985, 2133, 2149, 2157, 2158, 2189, 2206, 2209], "reach": [4, 8, 9, 10, 24, 25, 29, 30, 40, 50, 55, 60, 1388, 1772, 1865, 1868, 1872, 1873, 2130, 2136, 2146, 2161, 2165, 2192], "At": [4, 6, 7, 8, 16, 18, 20, 24, 33, 35, 1179, 1496, 1497, 1498, 1509, 1510, 1511, 1512, 1513, 1514, 1549, 1550, 1551, 2037, 2041, 2110, 2132, 2141, 2160, 2165, 2171, 2191, 2192, 2194, 2204], "high": [4, 5, 8, 9, 10, 11, 16, 25, 29, 33, 38, 40, 47, 49, 51, 68, 118, 1501, 1629, 1906, 1907, 1939, 2041, 2098, 2108, 2113, 2115, 2118, 2136, 2141, 2142, 2144, 2145, 2146, 2157, 2160, 2161, 2165, 2170, 2175, 2176, 2177, 2181, 2185, 2187, 2188, 2191, 2198, 2203, 2204, 2207, 2208], "pseudo": [4, 86], "block_siz": [4, 834, 844, 847, 2104, 2126], "enough_data": 4, "len": [4, 24, 31, 68, 70, 75, 217, 544, 703, 705, 706, 709, 1164, 1168, 1171, 1175, 1178, 1182, 1213, 1319, 1382, 1403, 1415, 1473, 1476, 1518, 1727, 1801, 1803, 1819, 1867, 1875, 1982, 1985, 1991, 1992, 1996, 2042, 2043, 2044, 2097, 2098, 2100, 2136, 2140, 2156, 2157, 2169, 2170, 2175, 2191, 2202, 2203], "small_iqr": 4, "break": [4, 8, 29, 38, 62, 68, 680, 922, 1001, 1003, 1229, 1315, 1361, 1582, 1880, 1924, 1943, 2096, 2101, 2106, 2108, 2121, 2135, 2157, 2170, 2179, 2184, 2188, 2190, 2192, 2195, 2197, 2199], "stop": [4, 6, 29, 38, 40, 50, 51, 53, 56, 894, 923, 966, 1081, 1229, 1313, 1388, 1501, 1816, 1877, 2097, 2100, 2159, 2165, 2169, 2191, 2195, 2203], "repetit": [4, 1917, 2017], "statist": [4, 25, 29, 38, 813, 814, 838, 839, 840, 842, 845, 1050, 1063, 1092, 1094, 1095, 1097, 1099, 1101, 1102, 1103, 1108, 1109, 1110, 1111, 1122, 1123, 1393, 1453, 1458, 1459, 1460, 1496, 1497, 1498, 1536, 1544, 1545, 1546, 1554, 1555, 1556, 1557, 1564, 1565, 1566, 1622, 1880, 2076, 2077, 2078, 2079, 2080, 2081, 2083, 2084, 2113, 2130, 2138, 2157, 2160, 2163, 2194], "minimum": [4, 15, 20, 25, 50, 55, 706, 707, 781, 807, 838, 839, 840, 845, 850, 904, 972, 1125, 1192, 1276, 1277, 1278, 1418, 1501, 1540, 1632, 1866, 1867, 1875, 1959, 1979, 1980, 1981, 1982, 1983, 1984, 2098, 2121, 2128, 2130, 2141, 2156, 2163, 2173, 2198], "total_tim": 4, "choic": [4, 9, 10, 15, 29, 33, 1331, 1379, 1583, 1592, 1902, 2132, 2155, 2163, 2170, 2190, 2191, 2204], "block": [4, 8, 9, 21, 24, 29, 31, 33, 34, 37, 50, 53, 59, 67, 68, 87, 485, 582, 583, 584, 844, 862, 964, 980, 1039, 1044, 1101, 1336, 1388, 1445, 1509, 1510, 1511, 1512, 1513, 1514, 1528, 1558, 1559, 1560, 1561, 1562, 1563, 1585, 1588, 1626, 1627, 1628, 1629, 1630, 1634, 1682, 1758, 1772, 1979, 1980, 1981, 2054, 2097, 2099, 2100, 2113, 2118, 2121, 2122, 2126, 2130, 2133, 2135, 2139, 2141, 2155, 2160, 2165, 2170, 2182, 2201, 2203, 2206, 2208], "qualiti": [4, 8, 25], "balanc": [4, 1001], "compet": [4, 2146], "amort": 4, "invoc": [4, 6, 68, 870, 1001, 1006, 1227, 1318, 1331, 2018, 2097, 2100, 2133, 2136, 2143, 2165, 2167, 2183, 2188, 2201, 2203], "less": [4, 7, 8, 15, 20, 24, 25, 29, 36, 38, 51, 53, 55, 59, 363, 680, 837, 937, 939, 948, 949, 1026, 1097, 1115, 1145, 1189, 1193, 1202, 1340, 1352, 1388, 1404, 1405, 1486, 1542, 1596, 1605, 1614, 1620, 1819, 1915, 2025, 2027, 2079, 2095, 2098, 2100, 2121, 2133, 2136, 2139, 2146, 2147, 2156, 2160, 2191, 2194, 2201], "bias": [4, 25, 744, 770, 806, 1496, 1497, 1498, 1533, 1534, 1536, 1544, 1545, 1546, 1552, 1553, 1554, 1598, 1600, 1622, 1642, 1643, 2157], "trivial": [4, 33, 36, 40, 43, 57, 745, 991, 1222, 1229, 1789, 1982, 2155, 2167, 2191, 2193], "low": [4, 8, 19, 25, 33, 38, 59, 498, 940, 1239, 1259, 1501, 1885, 1902, 1906, 1907, 1924, 1998, 2098, 2113, 2115, 2118, 2133, 2146, 2147, 2159, 2172, 2177, 2191, 2197, 2198, 2205], "digit": [4, 985, 1943, 2095, 2111, 2143, 2147], "microsecond": [4, 2133], "bia": [4, 10, 29, 35, 55, 70, 722, 723, 724, 725, 726, 727, 728, 729, 730, 735, 736, 737, 738, 739, 740, 741, 742, 743, 744, 745, 748, 749, 750, 751, 752, 753, 759, 761, 762, 763, 764, 766, 770, 771, 774, 775, 782, 783, 784, 791, 804, 1212, 1315, 1325, 1486, 1499, 1509, 1510, 1511, 1512, 1513, 1514, 1533, 1534, 1552, 1553, 1554, 1555, 1556, 1557, 1558, 1559, 1560, 1561, 1562, 1563, 1564, 1565, 1566, 1567, 1569, 1582, 1588, 1598, 1599, 1600, 1626, 1628, 1630, 1643, 1658, 1659, 1663, 1664, 1665, 1666, 1667, 1668, 1689, 1698, 1702, 1705, 1740, 1762, 1772, 1784, 1786, 1789, 1790, 1791, 1805, 1807, 1809, 1824, 1827, 1839, 1840, 1841, 1842, 1843, 1844, 1845, 1846, 1847, 1856, 1859, 1860, 1861, 1862, 1863, 1880, 1899, 2098, 2104, 2108, 2110, 2112, 2121, 2127, 2136, 2144, 2149, 2152, 2157, 2160, 2162, 2170, 2175, 2198], "period": [4, 10, 34, 50, 970, 979, 1050, 1088, 1102, 1103, 1108, 1122, 1123, 1165, 1273, 1274, 1335, 1879, 1949, 1950, 1951, 1952, 1953, 1954, 1955, 1956, 1957, 1958, 1959, 2098, 2113, 2118, 2146, 2208], "overal": [4, 10, 24, 35, 40, 50, 948, 1161, 1162, 1164, 1166, 1167, 1168, 1169, 1170, 1171, 1173, 1174, 1175, 1176, 1177, 1178, 1179, 1180, 1182, 1629, 2130, 2132, 2133, 2146, 2160, 2161, 2197], "main": [4, 8, 24, 25, 29, 30, 31, 36, 38, 40, 41, 42, 43, 48, 50, 51, 53, 54, 55, 68, 259, 495, 834, 1001, 1132, 1133, 1134, 1135, 1136, 1378, 1690, 1976, 2024, 2025, 2026, 2027, 2095, 2097, 2113, 2118, 2120, 2121, 2129, 2130, 2133, 2135, 2136, 2137, 2139, 2144, 2146, 2150, 2151, 2152, 2160, 2165, 2166, 2175, 2176, 2179, 2182, 2184, 2189, 2191, 2194, 2202, 2203], "collect_callgrind": 4, "collect_baselin": 4, "retain_out_fil": 4, "callgrindstat": 4, "tupl": [4, 6, 13, 15, 20, 24, 29, 31, 35, 36, 37, 40, 47, 48, 50, 55, 56, 57, 59, 68, 232, 318, 444, 446, 448, 492, 496, 519, 536, 559, 582, 583, 584, 703, 705, 706, 707, 709, 745, 776, 777, 778, 779, 782, 783, 784, 789, 795, 796, 797, 805, 809, 812, 831, 832, 834, 844, 862, 883, 890, 891, 907, 908, 915, 916, 917, 918, 919, 921, 934, 935, 937, 938, 939, 940, 941, 942, 943, 948, 949, 952, 953, 954, 968, 984, 995, 1003, 1011, 1018, 1025, 1041, 1051, 1052, 1055, 1068, 1089, 1095, 1124, 1125, 1143, 1146, 1148, 1162, 1164, 1165, 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1835, 1875, 1905, 1907, 1909, 1936, 1979, 1980, 1981, 1982, 1983, 1984, 1993, 2074, 2094, 2100, 2101, 2120, 2121, 2133, 2135, 2136, 2139, 2144, 2152, 2155, 2158, 2160, 2165, 2167, 2170, 2172, 2173, 2175, 2177, 2190, 2193, 2194, 2202, 2203, 2204, 2211], "jitter": 4, "interpret": [4, 24, 29, 34, 38, 40, 50, 53, 55, 56, 57, 232, 789, 795, 909, 943, 1041, 1100, 1166, 1167, 1168, 1173, 1175, 1176, 1177, 1178, 1199, 1227, 1266, 1278, 1315, 1320, 1327, 1331, 1368, 1388, 1521, 1646, 1688, 1699, 1700, 1759, 1936, 1941, 2095, 2099, 2100, 2104, 2118, 2132, 2133, 2170, 2172, 2174, 2191, 2192, 2203], "ideal": [4, 49, 51, 1331, 1863, 2120, 2121, 2194, 2203], "analysi": [4, 25, 38, 55, 57, 68, 1335, 1885, 1949, 1950, 1951, 1952, 1953, 1954, 1955, 1956, 1957, 1958, 1959, 2101, 2108, 2144, 2151, 2152, 2181, 2189, 2191, 2192, 2193, 2197], "valgrind": 4, "degrad": [4, 15, 59, 2132, 2135, 2136, 2170], "due": [4, 5, 6, 8, 25, 29, 31, 33, 38, 49, 59, 64, 68, 70, 72, 485, 909, 937, 938, 943, 1022, 1219, 1328, 1351, 1352, 1379, 1405, 1521, 1535, 1626, 1635, 1699, 1740, 1823, 1839, 1840, 1841, 1842, 1843, 1844, 1845, 1847, 1859, 1860, 1861, 1862, 1863, 1998, 2097, 2133, 2141, 2142, 2147, 2148, 2149, 2155, 2160, 2161, 2165, 2167, 2188, 2194, 2199, 2203, 2204, 2206, 2208], "amelior": 4, "suffici": [4, 15, 25, 29, 38, 42, 50, 51, 56, 59, 485, 1013, 1843, 1844, 1862, 1998, 2095, 2104, 2140, 2170, 2173, 2194], "callgrind_control": 4, "callgrind_annot": 4, "boundari": [4, 33, 35, 68, 789, 795, 985, 1269, 1503, 1504, 1505, 1506, 1507, 1508, 1604, 1605, 1606, 1607, 1608, 1609, 1635, 1638, 1639, 1640, 1699, 1759, 1868, 1875, 2098, 2121, 2135, 2158, 2165, 2194], "caller": [4, 40, 50, 68, 883, 1315, 1582, 1630, 1880, 1982, 2104, 2130, 2133, 2165, 2167], "structur": [4, 6, 10, 13, 24, 25, 29, 33, 35, 39, 40, 43, 51, 55, 56, 57, 59, 68, 70, 71, 75, 79, 921, 934, 935, 967, 1001, 1018, 1196, 1209, 1214, 1227, 1234, 1235, 1331, 1582, 1642, 1802, 1806, 1838, 1885, 1998, 2048, 2099, 2101, 2104, 2107, 2113, 2126, 2133, 2135, 2136, 2137, 2139, 2146, 2149, 2150, 2155, 2157, 2158, 2165, 2172, 2175, 2176, 2177, 2179, 2181, 2184, 2190, 2191, 2192, 2194, 2195, 2199, 2206], "restrict": [4, 9, 13, 24, 38, 56, 59, 64, 65, 1003, 1018, 1387, 1410, 1517, 2099, 2100, 2101, 2120, 2121, 2130, 2133, 2141, 2149, 2160, 2190], "builtin": [4, 29, 68, 70, 79, 1328, 1387, 1911, 2005, 2097, 2099, 2101, 2165, 2167, 2191, 2194, 2203, 2204], "surpris": [4, 9, 59, 2095, 2141, 2147, 2193], "serial": [4, 16, 24, 29, 31, 34, 50, 1315, 1325, 1326, 1387, 1582, 1762, 1880, 1927, 2095, 2096, 2098, 2111, 2120, 2130, 2133, 2139, 2143, 2144, 2146, 2152, 2155, 2158, 2160, 2165, 2166, 2172], "subsequ": [4, 8, 15, 16, 18, 29, 55, 57, 59, 68, 1001, 1040, 1075, 1315, 1327, 1331, 1509, 1510, 1511, 1512, 1513, 1514, 1582, 1611, 1880, 2121, 2133, 2148, 2155, 2159, 2165, 2170, 2183, 2184, 2188, 2194], "deseri": [4, 31, 1229, 1387, 1762, 2095, 2111, 2149, 2165, 2172], "globalsbridg": 4, "care": [4, 8, 15, 29, 38, 59, 67, 68, 1166, 1168, 1228, 1229, 1582, 2118, 2132, 2133, 2135, 2136, 2138, 2144, 2146, 2149, 2155, 2165, 2170, 2188, 2193, 2194, 2195], "reli": [4, 10, 15, 24, 25, 35, 40, 43, 59, 68, 149, 922, 948, 1145, 1518, 1863, 2109, 2130, 2132, 2135, 2136, 2139, 2148, 2149, 2170, 2174, 2193, 2194, 2195, 2197], "pickl": [4, 24, 25, 29, 1315, 1323, 1387, 1582, 1880, 1927, 2095, 2111, 2118, 2149, 2153, 2158, 2165, 2206], "transfer": [4, 24, 29, 59, 2110, 2118, 2133, 2142, 2144, 2157, 2158, 2165, 2173], "profil": [4, 5, 18, 20, 47, 68, 485, 956, 957, 964, 1001, 1056, 1329, 1460, 1765, 1766, 1767, 1768, 1943, 2096, 2115, 2139, 2155, 2156, 2165, 2182, 2183, 2194, 2203], "empti": [4, 13, 28, 29, 31, 36, 40, 48, 55, 56, 57, 59, 64, 68, 232, 320, 512, 514, 522, 536, 544, 831, 953, 968, 971, 972, 982, 988, 1018, 1145, 1147, 1313, 1315, 1316, 1341, 1361, 1362, 1367, 1374, 1397, 1398, 1399, 1400, 1407, 1415, 1452, 1495, 1517, 1525, 1582, 1589, 1661, 1680, 1727, 1823, 1835, 1836, 1880, 1982, 1997, 2014, 2094, 2097, 2098, 2099, 2100, 2102, 2110, 2112, 2119, 2120, 2128, 2131, 2133, 2136, 2148, 2149, 2156, 2158, 2170, 2171, 2176, 2179, 2190, 2191, 2193, 2198, 2201], "drive": [4, 10, 29, 2130], "facil": [4, 26, 1387, 2118], "analyz": [4, 5, 19, 55, 57, 68, 2135, 2136, 2139, 2147, 2149, 2204], "manipul": [4, 20, 59, 67, 1319, 2129, 2138, 2144, 2163, 2172, 2202], "1000000": [4, 1839], "mirror": [4, 138, 1772], "semant": [4, 10, 12, 18, 29, 36, 37, 50, 51, 55, 56, 59, 66, 68, 87, 149, 806, 903, 904, 905, 922, 943, 983, 1044, 1101, 1203, 1214, 1227, 1242, 1243, 1319, 1326, 1385, 1445, 1495, 1661, 1733, 1781, 1782, 1863, 2033, 2048, 2096, 2099, 2100, 2104, 2107, 2121, 2137, 2144, 2155, 2165, 2170, 2183, 2194], "number_per_run": 4, "raw_tim": 4, "task_spec": 4, "serializ": [4, 25, 2097], "consum": [4, 24, 31, 35, 39, 53, 56, 67, 434, 1145, 2118, 2133, 2139, 2146, 2151, 2155, 2175], "extrapol": 4, "sinc": [4, 8, 15, 24, 25, 29, 31, 33, 37, 38, 43, 51, 53, 55, 57, 59, 68, 314, 487, 533, 757, 795, 805, 822, 823, 833, 991, 1092, 1094, 1097, 1145, 1185, 1186, 1187, 1202, 1312, 1315, 1320, 1326, 1329, 1405, 1494, 1524, 1578, 1579, 1580, 1582, 1635, 1643, 1653, 1688, 1690, 1759, 1762, 1806, 1825, 1830, 1838, 1839, 1840, 1842, 1843, 1844, 1845, 1847, 1859, 1860, 1861, 1862, 1863, 1864, 1865, 1866, 1867, 1868, 1869, 1870, 1871, 1872, 1873, 1874, 1875, 1876, 1877, 1878, 1879, 1880, 1881, 1993, 2076, 2077, 2079, 2097, 2098, 2099, 2100, 2104, 2106, 2110, 2113, 2121, 2129, 2130, 2133, 2136, 2137, 2138, 2139, 2141, 2143, 2144, 2149, 2150, 2151, 2155, 2156, 2158, 2159, 2160, 2164, 2165, 2166, 2167, 2170, 2171, 2173, 2174, 2176, 2177, 2185, 2188, 2191, 2194, 2199, 2202, 2203, 2204], "properti": [4, 24, 29, 30, 31, 34, 35, 36, 38, 42, 47, 50, 55, 56, 59, 68, 804, 909, 935, 952, 962, 1041, 1070, 1161, 1162, 1164, 1166, 1167, 1168, 1176, 1177, 1178, 1315, 1490, 1582, 1616, 1641, 1746, 1762, 1773, 1774, 1775, 1776, 1816, 1880, 2065, 2100, 2104, 2113, 2126, 2130, 2133, 2136, 2141, 2152, 2157, 2159, 2165, 2170, 2172, 2173, 2176, 2179, 2184, 2190, 2191, 2192, 2193, 2205, 2209], "significant_figur": 4, "figur": [4, 8, 9, 31, 36, 68, 957, 2132, 2135, 2136, 2153, 2155, 2167, 2175, 2181, 2191, 2194, 2208], "intend": [4, 31, 50, 55, 57, 68, 919, 929, 932, 934, 1046, 1229, 1335, 1653, 1765, 1766, 1767, 1768, 1839, 1840, 1842, 1843, 1844, 1845, 1847, 1859, 1860, 1861, 1862, 1863, 2100, 2128, 2130, 2144, 2155, 2158, 2203], "interquartil": 4, "mitig": [4, 70, 2139, 2147, 2172], "tail": [4, 24, 48], "645": 4, "conjunct": [4, 24, 29, 34, 59, 834, 842, 1653, 1688, 1772, 2163, 2165], "trim_sigfig": 4, "human": [4, 14, 35, 48, 55, 1088, 1102, 2128, 2155, 2192, 2205], "raw": [4, 68, 1199, 1374, 2104, 2133, 2158, 2159], "built_with_debug_symbol": 4, "baseline_inclusive_stat": 4, "baseline_exclusive_stat": 4, "stmt_inclusive_stat": 4, "stmt_exclusive_stat": 4, "stmt_callgrind_out": 4, "done": [4, 16, 24, 29, 31, 33, 34, 35, 36, 38, 48, 56, 59, 67, 68, 485, 495, 680, 770, 948, 955, 1162, 1164, 1167, 1168, 1170, 1171, 1174, 1175, 1177, 1178, 1180, 1182, 1331, 1405, 1496, 1497, 1498, 1518, 1521, 1533, 1544, 1545, 1546, 1556, 1557, 1564, 1565, 1566, 1567, 1592, 1622, 1628, 1630, 1680, 1772, 1865, 1872, 1880, 1988, 2100, 2110, 2112, 2118, 2130, 2132, 2133, 2135, 2136, 2137, 2138, 2144, 2147, 2152, 2153, 2155, 2157, 2160, 2161, 2165, 2167, 2173, 2189, 2190, 2191, 2193, 2200, 2202], "functioncount": 4, "stat": [4, 62, 812, 834, 850, 863, 864, 1101, 1109, 1110, 1111, 1622, 1772, 2083, 2084, 2113, 2133, 2180, 2194, 2204], "as_standard": 4, "strip": [4, 1690, 1772, 2097, 2156, 2170], "prefix": [4, 29, 31, 40, 50, 59, 68, 758, 767, 1315, 1582, 1772, 1880, 2095, 2106, 2130, 2155, 2158, 2160, 2180, 2201], "stumbl": 4, "filepath": 4, "dif": 4, "compon": [4, 8, 10, 16, 19, 20, 29, 36, 38, 68, 680, 923, 948, 1003, 1165, 1166, 1168, 1176, 1177, 1178, 1355, 1626, 1627, 1628, 1629, 1630, 1762, 1885, 1993, 2046, 2047, 2101, 2106, 2121, 2126, 2130, 2135, 2136, 2143, 2144, 2170, 2172, 2183, 2189, 2195, 2203, 2204], "locat": [4, 10, 15, 29, 31, 35, 38, 47, 55, 57, 153, 253, 485, 512, 680, 908, 983, 985, 1053, 1124, 1125, 1337, 1387, 1413, 1418, 1421, 1468, 1518, 1528, 1617, 1634, 1653, 1688, 1755, 1758, 1772, 1795, 1896, 1931, 1952, 1974, 1978, 2036, 2095, 2097, 2111, 2126, 2133, 2149, 2158, 2159, 2165, 2167, 2170, 2175, 2180, 2184, 2188, 2193, 2203], "someth": [4, 8, 55, 56, 61, 68, 937, 939, 1145, 1229, 1239, 1315, 1331, 1582, 1839, 1840, 1841, 1842, 1843, 1844, 1845, 1846, 1847, 1856, 1859, 1860, 1861, 1862, 1863, 1880, 2008, 2102, 2113, 2118, 2119, 2121, 2130, 2142, 2150, 2155, 2165, 2189, 2190, 2191, 2194, 2196, 2202, 2203, 2204, 2205], "resembl": [4, 16, 2184], "23234231": 4, "first_build_dir": 4, "foo": [4, 15, 29, 47, 48, 55, 67, 68, 70, 78, 79, 834, 842, 883, 888, 1001, 1013, 1202, 1203, 1212, 1313, 1318, 1323, 1326, 1327, 1330, 1331, 1825, 2037, 2039, 2095, 2097, 2099, 2100, 2104, 2108, 2136, 2144, 2155, 2158, 2177, 2188, 2191, 2194, 2203, 2204], "9823794": 4, "bar": [4, 8, 47, 55, 68, 70, 78, 79, 883, 888, 1013, 1026, 1202, 1318, 1326, 1991, 1992, 2043, 2044, 2095, 2097, 2099, 2104, 2111, 2144, 2155, 2158, 2177], "53453": 4, "src": [4, 29, 68, 195, 227, 312, 320, 470, 511, 512, 513, 514, 515, 516, 518, 537, 1136, 1203, 1626, 1629, 1630, 1836, 1928, 1929, 1930, 1934, 1964, 2010, 2036, 2098, 2115, 2121, 2158, 2198], "function_that_actually_chang": 4, "second_build_dir": 4, "cancel": [4, 1312], "site": [4, 8, 2184], "denois": 4, "explan": [4, 10, 21, 26, 43, 891, 1315, 1582, 1880, 2135, 2136, 2144, 2194, 2200, 2204], "delta": [4, 38, 770, 1026, 1533, 1542, 1552, 1614, 1697, 1840, 1991, 1992, 2021, 2043, 2044, 2098, 2128], "inclus": [4, 38, 55, 86, 512, 1233, 1239, 1276, 1278, 1386, 1402, 1408, 1644, 1645, 1906, 1907, 1990, 2164, 2177], "diff": [4, 8, 2097, 2098, 2121, 2156], "One": [4, 9, 15, 29, 31, 55, 56, 62, 64, 68, 582, 980, 1003, 1203, 1214, 1306, 1635, 1726, 1773, 1781, 1782, 1789, 1793, 1863, 1868, 1877, 1906, 1907, 2031, 2048, 2097, 2099, 2100, 2120, 2131, 2132, 2133, 2136, 2143, 2149, 2159, 2167, 2175, 2185, 2189, 2190, 2192, 2193, 2201, 2208], "reason": [4, 9, 10, 24, 29, 31, 33, 40, 55, 59, 64, 680, 812, 870, 934, 935, 1166, 1168, 1315, 1331, 1332, 1351, 1352, 1379, 1416, 1494, 1521, 1582, 1690, 1740, 1795, 1880, 1927, 2097, 2099, 2100, 2104, 2106, 2121, 2130, 2135, 2137, 2139, 2147, 2149, 2155, 2165, 2173, 2189, 2190, 2191, 2193, 2194, 2197, 2203, 2204, 2205], "unit": [4, 12, 15, 38, 40, 51, 59, 68, 770, 771, 1163, 1181, 1490, 1500, 1523, 1526, 1531, 1532, 1533, 1534, 1601, 1602, 1612, 1653, 1677, 1681, 1686, 1687, 1734, 1743, 1799, 1800, 1804, 1807, 1809, 1812, 1908, 2023, 2078, 2114, 2130, 2132, 2139, 2141, 2158, 2187, 2188], "next": [4, 24, 29, 33, 35, 38, 50, 55, 56, 57, 59, 68, 559, 770, 1227, 1319, 1482, 1534, 1553, 1600, 1772, 1818, 2118, 2129, 2130, 2132, 2133, 2136, 2137, 2139, 2144, 2146, 2151, 2159, 2165, 2166, 2170, 2172, 2173, 2175, 2184, 2188, 2191, 2192, 2194], "logic": [4, 6, 13, 15, 24, 37, 41, 54, 57, 59, 68, 973, 975, 976, 978, 1008, 1009, 1010, 1162, 1164, 1167, 1168, 1170, 1171, 1174, 1175, 1177, 1178, 1180, 1182, 1203, 1229, 1319, 1397, 1398, 1399, 1400, 1410, 1588, 1772, 1894, 1921, 2007, 2100, 2101, 2133, 2135, 2136, 2137, 2141, 2160, 2191], "question": [4, 11, 24, 68, 1237, 1827, 2096, 2130, 2139, 2182, 2190, 2193], "involv": [4, 6, 8, 10, 12, 24, 29, 57, 59, 62, 64, 68, 1225, 1772, 1939, 2100, 2119, 2121, 2130, 2133, 2135, 2138, 2144, 2147, 2155, 2160, 2165, 2166, 2167, 2170, 2190, 2193, 2201, 2203], "look": [4, 5, 8, 9, 10, 13, 16, 29, 36, 38, 49, 50, 55, 56, 57, 61, 64, 68, 70, 80, 81, 485, 891, 937, 939, 1018, 1227, 1238, 1315, 1410, 1486, 1582, 1679, 1808, 1839, 1840, 1841, 1842, 1843, 1844, 1845, 1846, 1847, 1856, 1859, 1860, 1861, 1862, 1863, 1880, 1979, 1980, 1981, 1983, 1984, 2097, 2099, 2104, 2119, 2126, 2129, 2130, 2133, 2137, 2143, 2144, 2146, 2150, 2155, 2158, 2160, 2165, 2166, 2181, 2185, 2188, 2189, 2190, 2191, 2194, 2195, 2196, 2197, 2200, 2201, 2202, 2203, 2204, 2206], "autom": [4, 9, 68, 2097, 2160, 2184, 2194], "easili": [4, 8, 9, 12, 25, 29, 31, 35, 55, 1203, 1589, 1688, 1727, 1863, 1924, 2037, 2112, 2121, 2136, 2137, 2141, 2144, 2149, 2152, 2157, 2164, 2165, 2167, 2175, 2194, 2202, 2203, 2204], "exclus": [4, 24, 29, 38, 40, 50, 59, 68, 745, 1278, 1772, 1906, 1907, 1910, 2130, 2177], "basi": [4, 10, 11, 38, 680, 1388, 1868, 2133, 2143, 2160, 2165, 2183, 2188, 2203], "thought": [4, 47, 68, 1163, 1165, 1181, 1233, 2191], "path_and_function_nam": 4, "children": [4, 35, 43, 59, 68, 803, 1315, 1582, 1880, 2118, 2139, 2144, 2158, 2167], "identifi": [4, 8, 10, 29, 31, 40, 44, 47, 50, 51, 53, 68, 85, 485, 831, 1278, 1387, 1444, 1622, 2101, 2104, 2118, 2131, 2143, 2144, 2149, 2158, 2165, 2166, 2167, 2172, 2175, 2183, 2190, 2201, 2203], "hot": [4, 38, 1690, 1726, 2141, 2188], "spot": [4, 1781, 1782], "_data": 4, "truncate_row": 4, "_linewidth": 4, "subtract": [4, 312, 563, 894, 1199, 1690, 1979, 1980, 1981, 1983, 1984, 1994, 2098, 2156, 2170], "index": [4, 16, 20, 24, 25, 29, 31, 35, 38, 48, 55, 57, 68, 190, 207, 280, 311, 312, 313, 314, 315, 316, 318, 320, 321, 470, 471, 511, 512, 513, 514, 515, 516, 517, 518, 683, 684, 685, 689, 690, 691, 705, 706, 831, 883, 906, 921, 923, 934, 935, 966, 985, 1014, 1058, 1061, 1095, 1124, 1125, 1165, 1206, 1207, 1208, 1212, 1214, 1256, 1287, 1288, 1289, 1290, 1313, 1332, 1337, 1345, 1363, 1381, 1395, 1403, 1405, 1409, 1413, 1416, 1417, 1418, 1421, 1447, 1450, 1468, 1474, 1477, 1478, 1486, 1501, 1517, 1524, 1525, 1583, 1584, 1589, 1592, 1593, 1632, 1634, 1679, 1680, 1726, 1801, 1803, 1810, 1811, 1816, 1829, 1865, 1866, 1867, 1868, 1869, 1871, 1872, 1873, 1874, 1875, 1878, 1879, 1881, 1896, 1928, 1929, 1930, 1931, 1933, 1934, 1935, 1964, 1975, 1976, 1979, 1980, 1981, 1983, 1984, 1993, 2010, 2031, 2032, 2034, 2035, 2036, 2039, 2048, 2056, 2058, 2096, 2097, 2098, 2100, 2104, 2107, 2118, 2119, 2120, 2126, 2130, 2133, 2135, 2137, 2140, 2142, 2148, 2156, 2157, 2167, 2169, 2170, 2172, 2173, 2174, 2175, 2176, 2177, 2193, 2194, 2198, 2203, 2204], "cpython": [4, 15, 55, 68, 2182, 2192], "known": [4, 8, 10, 23, 28, 29, 34, 44, 50, 56, 60, 63, 65, 70, 80, 81, 1209, 1210, 1229, 1331, 1332, 1387, 1509, 1510, 1511, 1512, 1513, 1514, 1528, 1552, 1598, 1612, 1614, 1634, 1699, 1743, 1762, 1951, 1952, 1998, 2014, 2098, 2102, 2104, 2113, 2118, 2121, 2128, 2130, 2132, 2148, 2155, 2157, 2165, 2167, 2171, 2182, 2190, 2195, 2204], "quit": [4, 8, 68, 1781, 1782, 2100, 2126, 2136, 2138, 2158, 2165, 2201], "noisi": 4, "higher": [4, 8, 9, 25, 29, 33, 59, 60, 65, 70, 75, 79, 87, 149, 922, 924, 943, 1044, 1089, 1101, 1137, 1204, 1210, 1214, 1223, 1239, 1242, 1276, 1292, 1445, 1475, 1517, 1574, 1589, 1740, 1840, 1896, 1998, 2048, 2054, 2104, 2133, 2136, 2137, 2141, 2143, 2149, 2160, 2161, 2165, 2173, 2190, 2203], "filter": [4, 21, 543, 782, 783, 784, 1312, 1335, 1509, 1510, 1511, 1512, 1513, 1514, 1663, 1664, 1665, 1666, 1667, 1668, 1949, 1950, 1951, 1952, 1953, 1954, 1955, 1956, 1957, 1958, 1959, 1993, 2100, 2158, 2204], "transform": [4, 13, 24, 31, 35, 36, 37, 55, 59, 64, 66, 414, 791, 810, 865, 867, 886, 887, 992, 1161, 1162, 1163, 1164, 1165, 1166, 1167, 1168, 1169, 1170, 1171, 1173, 1174, 1175, 1176, 1177, 1178, 1179, 1180, 1182, 1202, 1203, 1204, 1208, 1213, 1236, 1312, 1325, 1499, 1536, 1544, 1545, 1546, 1554, 1569, 1588, 1611, 1627, 1628, 1629, 1630, 1653, 1659, 1688, 1705, 1725, 1762, 1839, 1840, 1841, 1842, 1843, 1844, 1845, 1846, 1847, 1855, 1859, 1860, 1861, 1862, 1863, 1959, 1993, 2108, 2121, 2137, 2139, 2140, 2160, 2170, 2175, 2181, 2182, 2184, 2192, 2196, 2203], "rather": [4, 9, 10, 15, 29, 40, 43, 53, 55, 57, 59, 68, 70, 79, 789, 795, 961, 991, 1014, 1313, 1316, 1387, 1478, 1653, 1688, 1699, 1759, 1790, 1939, 1943, 2097, 2100, 2120, 2121, 2131, 2133, 2136, 2137, 2155, 2158, 2160, 2165, 2170, 2175, 2188, 2189, 2191, 2193, 2194, 2195, 2196, 2203], "unicod": [4, 2101], "dictionari": [4, 15, 24, 25, 31, 35, 36, 38, 55, 63, 68, 414, 680, 804, 805, 806, 810, 829, 830, 831, 865, 867, 868, 883, 887, 893, 968, 1001, 1101, 1202, 1212, 1222, 1229, 1315, 1316, 1323, 1327, 1331, 1332, 1387, 1388, 1458, 1459, 1464, 1524, 1525, 1582, 1583, 1592, 1679, 1806, 1839, 1840, 1841, 1842, 1843, 1844, 1845, 1846, 1847, 1856, 1859, 1860, 1861, 1862, 1863, 1880, 2063, 2081, 2082, 2099, 2101, 2136, 2144, 2155, 2157, 2165, 2175, 2180, 2199, 2205, 2206], "lookup": [4, 31, 38, 50, 1524, 1679, 2097, 2101, 2132, 2166, 2197], "map": [4, 15, 29, 31, 36, 38, 40, 47, 48, 50, 51, 55, 56, 57, 59, 64, 65, 68, 70, 79, 232, 694, 745, 804, 805, 809, 810, 813, 828, 829, 831, 837, 867, 868, 883, 884, 885, 886, 887, 893, 914, 982, 1044, 1197, 1203, 1214, 1225, 1227, 1229, 1234, 1269, 1278, 1323, 1326, 1387, 1388, 1512, 1513, 1514, 1520, 1521, 1522, 1526, 1528, 1578, 1579, 1580, 1583, 1592, 1674, 1675, 1676, 1681, 1789, 1806, 1839, 1840, 1841, 1842, 1843, 1844, 1845, 1846, 1847, 1856, 1859, 1860, 1861, 1862, 1863, 1896, 1898, 2032, 2033, 2048, 2054, 2101, 2104, 2112, 2120, 2122, 2130, 2133, 2135, 2136, 2137, 2145, 2150, 2152, 2155, 2157, 2158, 2160, 2163, 2165, 2166, 2167, 2170, 2172, 2177, 2192, 2193, 2202, 2205], "agnost": [4, 17, 50, 1027, 1028, 1029, 1030, 1031, 1032, 1033, 1034, 1035, 1688, 1781, 1782, 2104, 2120], "reliabl": 4, "warrant": 4, "except": [4, 6, 8, 10, 15, 29, 30, 31, 33, 36, 38, 40, 42, 43, 47, 51, 55, 56, 59, 67, 68, 232, 582, 586, 587, 616, 683, 694, 703, 705, 706, 709, 770, 833, 914, 947, 948, 949, 988, 995, 999, 1043, 1053, 1055, 1088, 1102, 1137, 1143, 1145, 1191, 1192, 1212, 1214, 1231, 1279, 1315, 1323, 1331, 1333, 1337, 1371, 1387, 1403, 1413, 1415, 1416, 1418, 1421, 1443, 1473, 1476, 1478, 1533, 1552, 1582, 1598, 1635, 1726, 1765, 1772, 1790, 1816, 1823, 1824, 1828, 1830, 1880, 1893, 1917, 1933, 1936, 1969, 1977, 1982, 1989, 1991, 1992, 1996, 2021, 2043, 2044, 2048, 2049, 2096, 2097, 2099, 2100, 2101, 2102, 2104, 2107, 2118, 2133, 2136, 2139, 2144, 2147, 2150, 2158, 2165, 2167, 2170, 2172, 2173, 2177, 2189, 2191, 2193, 2201, 2202, 2206, 2208], "filter_fn": 4, "map_fn": 4, "coalesc": [4, 322, 327, 543, 613, 1052, 1978, 1982, 2031, 2098, 2135, 2156, 2170, 2194], "entri": [4, 20, 29, 30, 34, 38, 40, 41, 48, 50, 51, 314, 745, 887, 888, 961, 1086, 1087, 1135, 1186, 1187, 1290, 1315, 1336, 1524, 1525, 1582, 1588, 1679, 1680, 1801, 1802, 1810, 1839, 1840, 1841, 1842, 1843, 1844, 1845, 1846, 1847, 1856, 1859, 1860, 1861, 1862, 1863, 1864, 1865, 1866, 1867, 1869, 1870, 1871, 1872, 1873, 1874, 1875, 1876, 1878, 1879, 1880, 1881, 1975, 2097, 2100, 2101, 2107, 2121, 2130, 2135, 2136, 2141, 2144, 2149, 2163, 2170, 2175, 2180, 2189, 2190, 2191, 2192], "color": [4, 68, 1546, 2099, 2100, 2175, 2195, 2203], "rowwis": [4, 37], "columnwis": 4, "extend_result": 4, "highlight_warn": 4, "highlight": [4, 56, 740, 741, 2100], "trim_significant_figur": 4, "trim": [4, 970, 979, 1161, 1162, 1164, 1166, 1167, 1168, 1169, 1170, 1171, 1173, 1174, 1175, 1176, 1177, 1178, 1179, 1180, 1182, 1273, 1274, 1312], "h": [5, 11, 15, 35, 38, 486, 487, 751, 752, 753, 770, 993, 994, 1345, 1352, 1355, 1365, 1379, 1484, 1485, 1492, 1493, 1495, 1497, 1498, 1510, 1511, 1513, 1521, 1522, 1524, 1526, 1533, 1534, 1545, 1546, 1552, 1553, 1554, 1565, 1566, 1576, 1577, 1594, 1595, 1598, 1600, 1617, 1633, 1636, 1637, 1653, 1661, 1688, 1724, 1730, 1731, 1740, 1789, 1790, 1824, 1836, 1885, 1997, 1998, 2098, 2115, 2119, 2120, 2126, 2130, 2133, 2135, 2136, 2138, 2141, 2155, 2174, 2175, 2176, 2184, 2190, 2200], "finit": [5, 38, 948, 949, 1304, 1305, 1351, 1352, 1362, 1363, 1379, 1405, 1468, 1472, 1494, 1997, 2136, 2141, 2177], "natur": [5, 8, 9, 12, 31, 38, 59, 70, 72, 948, 949, 1344, 1349, 1375, 1389, 1391, 1396, 1486, 1630, 1740, 2139, 2141, 2151, 2152, 2170, 2171], "against": [5, 6, 15, 29, 40, 50, 57, 806, 894, 948, 949, 1173, 1174, 1175, 1179, 1180, 1182, 1256, 1306, 1315, 1331, 1332, 1582, 1588, 1837, 1880, 2095, 2100, 2158, 2181, 2191, 2200], "cprofil": 5, "mode": [5, 9, 24, 25, 29, 31, 33, 38, 50, 53, 55, 57, 64, 65, 68, 220, 221, 487, 756, 757, 782, 783, 784, 789, 795, 796, 797, 829, 831, 841, 850, 888, 889, 918, 920, 924, 927, 928, 929, 934, 935, 937, 938, 939, 940, 944, 945, 946, 948, 1001, 1006, 1014, 1037, 1076, 1119, 1120, 1149, 1161, 1162, 1164, 1166, 1167, 1168, 1169, 1170, 1171, 1173, 1174, 1175, 1176, 1177, 1178, 1179, 1180, 1182, 1203, 1206, 1207, 1208, 1209, 1212, 1213, 1215, 1216, 1223, 1228, 1263, 1298, 1299, 1313, 1315, 1316, 1319, 1331, 1333, 1374, 1387, 1417, 1434, 1435, 1465, 1496, 1497, 1498, 1509, 1510, 1511, 1525, 1536, 1544, 1545, 1546, 1554, 1555, 1556, 1557, 1564, 1565, 1566, 1582, 1599, 1622, 1635, 1636, 1663, 1664, 1665, 1680, 1688, 1699, 1727, 1740, 1759, 1760, 1761, 1774, 1783, 1785, 1790, 1795, 1828, 1868, 1877, 1880, 1895, 1938, 1940, 2036, 2098, 2104, 2110, 2119, 2128, 2133, 2135, 2137, 2138, 2139, 2140, 2144, 2151, 2152, 2156, 2159, 2161, 2162, 2163, 2165, 2176, 2179, 2184, 2188, 2192, 2193, 2194, 2195, 2196, 2198, 2203, 2204, 2205], "correct": [5, 6, 7, 8, 25, 29, 30, 31, 35, 36, 38, 50, 55, 57, 87, 204, 208, 220, 554, 579, 602, 603, 614, 622, 929, 930, 934, 949, 1026, 1166, 1168, 1176, 1177, 1178, 1234, 1290, 1313, 1316, 1331, 1332, 1351, 1445, 1518, 1535, 1547, 1762, 1772, 1816, 1942, 1991, 1992, 2043, 2044, 2097, 2098, 2099, 2104, 2108, 2119, 2120, 2128, 2133, 2136, 2141, 2157, 2172, 2191, 2198, 2202, 2203], "launch": [5, 15, 24, 32, 35, 36, 40, 42, 43, 48, 49, 50, 54, 67, 1040, 1075, 1772, 2130, 2132, 2133, 2135, 2136, 2165, 2188, 2196], "spent": [5, 29, 962, 1875, 2132, 2144, 2194, 2201, 2204], "appear": [5, 25, 29, 38, 55, 56, 68, 883, 964, 1089, 1145, 1186, 1187, 1214, 1387, 1421, 1423, 1773, 1774, 1917, 1945, 1949, 1950, 1951, 1952, 1953, 1954, 1955, 1956, 1957, 1958, 1959, 2048, 2097, 2100, 2119, 2120, 2136, 2137, 2144, 2155, 2158, 2191, 2194, 2201], "extrem": [5, 29, 1772, 2130, 2155, 2193], "expens": [5, 24, 38, 59, 1789, 2126, 2133, 2141, 2143, 2159, 2165, 2176, 2190, 2194, 2197, 2200, 2204], "bound": [5, 16, 25, 29, 56, 59, 480, 807, 985, 996, 1159, 1160, 1229, 1233, 1238, 1315, 1491, 1492, 1493, 1575, 1576, 1577, 1582, 1601, 1688, 1877, 1880, 1910, 1931, 2100, 2101, 2128, 2130, 2139, 2158, 2160, 2171, 2190, 2191, 2193, 2203, 2204], "greater": [5, 20, 29, 50, 70, 71, 291, 616, 680, 977, 990, 991, 996, 1161, 1162, 1164, 1166, 1167, 1168, 1169, 1170, 1171, 1173, 1174, 1175, 1176, 1177, 1178, 1179, 1180, 1182, 1258, 1272, 1370, 1439, 1494, 1631, 1688, 1699, 1726, 1740, 1756, 1759, 1790, 1824, 1877, 2036, 2098, 2121, 2130, 2133, 2148, 2156, 2170, 2171], "spend": [5, 8, 20, 35, 1388, 2108, 2197], "sens": [5, 38, 50, 68, 1806, 2032, 2033, 2100, 2130, 2139], "respons": [5, 8, 10, 15, 29, 31, 34, 35, 36, 38, 40, 47, 53, 59, 67, 1003, 1040, 1075, 1231, 1570, 1706, 1772, 1982, 2069, 2104, 2130, 2133, 2136, 2137, 2144, 2165, 2193, 2203], "Of": [5, 1829, 2095, 2135, 2136, 2182, 2191, 2194], "cours": [5, 20, 68, 1217, 2095, 2135, 2136, 2165, 2191, 2194], "realiti": [5, 2139, 2203], "complic": [5, 25, 37, 68, 805, 1939, 2104, 2120, 2131, 2158, 2165, 2167, 2191, 2193, 2203], "account": [5, 35, 48, 68, 1495, 1772, 2126, 2128, 2132, 2139, 2170, 2188], "heavili": [5, 1846, 2132, 2136, 2158], "similarli": [5, 8, 29, 31, 35, 55, 67, 68, 770, 802, 806, 887, 992, 1165, 1315, 1371, 1582, 1630, 1797, 1798, 1799, 1800, 1801, 1802, 1803, 1804, 1813, 1880, 2024, 2025, 2026, 2027, 2099, 2104, 2130, 2136, 2138, 2141, 2147, 2170, 2193, 2196, 2201], "platform": [5, 9, 10, 15, 29, 42, 43, 47, 1362, 1363, 1374, 1895, 1997, 2110, 2142, 2147, 2148, 2160, 2184], "startup": [5, 20], "slower": [5, 15, 29, 35, 905, 938, 1185, 1186, 1187, 1345, 1352, 1707, 1789, 1840, 1841, 2136, 2148, 2157, 2171, 2193, 2203], "rerun": [6, 29, 2133], "segment": [6, 1101, 1199, 1218, 1614, 2133, 2158, 2194, 2199, 2203, 2206], "persist": [6, 22, 31, 55, 59, 70, 71, 72, 73, 74, 75, 76, 78, 79, 80, 81, 82, 83, 770, 829, 830, 831, 1315, 1533, 1552, 1582, 1598, 1773, 1775, 1880, 2144, 2149, 2150, 2172, 2183, 2188, 2193, 2197], "rng": [6, 24, 1073, 1116, 1429, 1440, 1454, 1462, 1932, 2067, 2088, 2133, 2148, 2164], "advanc": [6, 16, 24, 25, 40, 1829, 1998, 2039, 2104, 2107, 2120, 2133, 2136, 2142, 2146, 2174, 2175, 2194, 2203], "juggl": 6, "moder": 6, "hit": [6, 9, 15, 57, 1231, 1772, 2095, 2121, 2133, 2188, 2191, 2194, 2197, 2203, 2204], "preserve_rng_st": 6, "checkpoint_sequenti": 6, "omit": [6, 15, 29, 149, 1179, 1180, 1182, 1229, 1535, 1596, 2150, 2155, 2165, 2177, 2191, 2195], "exclud": [6, 10, 25, 33, 50, 68, 1011, 1278, 1525, 1680, 1729, 1837, 1939, 2024, 2025, 2026, 2027, 2130, 2142, 2149, 2158, 2166, 2192, 2194], "_infer_device_typ": 6, "remain": [6, 9, 20, 36, 38, 50, 68, 1423, 1524, 1525, 1679, 1680, 1762, 1772, 1781, 1782, 1797, 1798, 1799, 1800, 1801, 1802, 1803, 1804, 1813, 1839, 1840, 1841, 1842, 1843, 1844, 1845, 1846, 1847, 1849, 1859, 1860, 1861, 1862, 1863, 1877, 1896, 1918, 2112, 2113, 2118, 2129, 2138, 2144, 2157, 2172, 2184, 2192], "consequ": [6, 64, 1379, 1552, 1997, 2100, 2130, 2133, 2146, 2148, 2151], "random": [6, 36, 38, 40, 50, 51, 60, 68, 86, 153, 770, 921, 934, 935, 971, 1073, 1074, 1080, 1090, 1091, 1112, 1113, 1116, 1117, 1207, 1214, 1268, 1291, 1388, 1408, 1429, 1430, 1438, 1440, 1454, 1462, 1490, 1501, 1526, 1533, 1552, 1601, 1681, 1732, 1738, 1762, 1790, 1797, 1803, 1804, 1811, 1812, 1831, 1885, 1902, 1904, 1905, 1906, 1907, 1908, 1909, 1910, 1932, 1944, 1998, 2039, 2048, 2067, 2068, 2071, 2074, 2075, 2085, 2086, 2088, 2089, 2095, 2096, 2098, 2104, 2106, 2128, 2137, 2141, 2147, 2155, 2156, 2175, 2194, 2204], "gradient": [6, 12, 24, 25, 29, 33, 34, 35, 36, 38, 59, 60, 63, 149, 220, 221, 289, 334, 486, 487, 494, 512, 705, 706, 909, 918, 920, 922, 925, 926, 927, 928, 929, 930, 931, 932, 934, 935, 937, 938, 939, 940, 941, 942, 943, 944, 945, 948, 949, 953, 954, 972, 998, 1149, 1202, 1203, 1204, 1205, 1209, 1214, 1256, 1315, 1351, 1352, 1355, 1362, 1363, 1379, 1388, 1405, 1407, 1413, 1416, 1418, 1494, 1501, 1512, 1513, 1514, 1517, 1518, 1524, 1525, 1535, 1549, 1550, 1551, 1582, 1589, 1614, 1671, 1672, 1679, 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43, 2097, 2130, 2137, 2141, 2191, 2195], "maker": 10, "strong": 10, "toward": [10, 70, 1140, 1277, 1278, 1388, 1482, 1772, 1872, 1915, 1923, 1924, 2139], "philosophi": [10, 63, 2096], "beyond": [10, 25, 57, 1439, 1517, 1843, 1844, 1859, 1922, 2115, 2138, 2144, 2190], "encourag": [10, 15, 40, 2160, 2170, 2177], "propos": [10, 1839, 1866, 1867, 1880, 2107, 2141, 2157, 2166], "review": [10, 11, 25, 2110, 2158, 2196], "willing": [10, 2183], "invest": 10, "anyon": 10, "ownership": [10, 68], "codebas": [10, 2136, 2203], "strictli": [10, 24, 149, 190, 207, 485, 922, 985, 1278, 1315, 1319, 1582, 1880, 2130, 2171], "compani": 10, "bui": 10, "addition": [10, 15, 24, 25, 29, 31, 36, 38, 40, 55, 57, 59, 149, 512, 616, 922, 957, 1203, 1207, 1208, 1319, 1379, 1486, 1544, 1545, 1546, 1973, 2138, 2179, 2203, 2206], "membership": [10, 40, 49, 50, 2101], "That": [10, 13, 18, 20, 40, 48, 51, 61, 68, 1014, 1018, 1312, 1386, 1402, 2036, 2104, 2110, 2136, 2137, 2138, 2139, 2149, 2158, 2165, 2191, 2194], "seat": 10, "reserv": [10, 33, 47, 1101, 2081, 2101, 2133, 2144, 2206], "emploi": [10, 31, 1862, 2144, 2158, 2184], "directori": [10, 15, 29, 31, 35, 40, 48, 1837, 2095, 2111, 2143, 2155, 2158, 2159, 2175, 2184, 2185, 2194, 2204], "procedur": [10, 38, 888, 1331, 1332, 1388, 1971, 2165, 2204], "disput": 10, "made": [10, 18, 21, 29, 31, 34, 51, 55, 68, 929, 932, 934, 949, 1042, 1315, 1582, 1628, 1630, 1839, 1840, 1841, 1842, 1843, 1844, 1845, 1846, 1847, 1848, 1851, 1853, 1859, 1860, 1861, 1862, 1863, 1880, 1912, 2099, 2107, 2130, 2150, 2158, 2175, 2177, 2188, 2190, 2191, 2203, 2204, 2205], "public": [10, 11, 36, 1203, 2121, 2136, 2203, 2205], "resolut": [10, 1594, 1595, 1653, 1688, 1730, 1731, 2021, 2101, 2158, 2209], "conclus": 10, "publicli": [10, 2205], "vision": [10, 1325, 1517, 1671, 2095, 2196], "roadmap": [10, 11], "parti": [10, 50, 2095, 2097, 2104, 2133, 2137, 2144, 2158, 2191], "triag": [10, 11], "meet": [10, 11, 13, 31, 49, 1013, 1018, 1345, 2133, 2194], "Their": [10, 1137, 2136, 2193], "articul": 10, "cohes": 10, "negoti": [10, 2165], "contenti": 10, "broad": [10, 2144, 2207], "stakehold": 10, "power": [10, 13, 33, 49, 1108, 1161, 1162, 1164, 1166, 1167, 1168, 1169, 1170, 1171, 1173, 1174, 1175, 1176, 1177, 1178, 1179, 1180, 1182, 1188, 1339, 1369, 1383, 1549, 1550, 1551, 1709, 1710, 1711, 1790, 1824, 1839, 1876, 1892, 1916, 2042, 2101, 2133, 2170], "veto": 10, "admin": 10, "amongst": 10, "commonli": [10, 38, 59, 1417, 1841, 2100, 2102, 2121, 2129, 2130, 2139, 2157, 2160, 2173, 2182, 2187, 2190], "merit": 10, "demonstr": [10, 35, 55, 68, 70, 78, 79, 1501, 2097, 2144, 2149, 2152, 2153, 2157, 2165, 2172, 2184, 2192, 2194, 2196, 2201], "expertis": 10, "align": [10, 25, 55, 770, 789, 795, 838, 850, 1001, 1145, 1269, 1371, 1493, 1501, 1517, 1533, 1547, 1552, 1576, 1577, 1598, 1635, 1643, 1644, 1645, 1671, 1672, 1699, 1700, 1740, 1759, 1789, 1830, 1840, 1841, 1842, 1843, 1844, 1845, 1847, 1859, 1860, 1861, 1862, 1866, 2021, 2119, 2126, 2130, 2141, 2149, 2155, 2163, 2171], "continu": [10, 24, 29, 31, 38, 54, 57, 59, 68, 401, 607, 836, 938, 1269, 1351, 1352, 1379, 1500, 1501, 1772, 1877, 2101, 2107, 2130, 2139, 2149, 2159, 2172, 2179, 2189, 2191, 2192, 2194, 2203], "light": [10, 2175, 2203], "mainten": [10, 50, 51], "emeritu": [10, 11], "inact": [10, 1101, 2133, 2206], "contact": [10, 29], "item": [10, 24, 31, 55, 70, 80, 81, 582, 583, 584, 588, 804, 805, 806, 828, 829, 830, 831, 968, 1014, 1517, 1583, 1592, 1795, 1943, 2095, 2097, 2098, 2099, 2100, 2108, 2119, 2121, 2133, 2140, 2149, 2155, 2156, 2157, 2158, 2165, 2175, 2176, 2188, 2190, 2191, 2193, 2198, 2203], "nomine": 10, "breadth": [10, 35], "testimoni": 10, "posit": [10, 24, 31, 35, 36, 38, 55, 68, 86, 401, 470, 544, 745, 886, 890, 892, 970, 972, 979, 992, 993, 994, 1044, 1161, 1162, 1163, 1164, 1165, 1173, 1175, 1179, 1181, 1199, 1209, 1210, 1213, 1227, 1229, 1273, 1274, 1307, 1310, 1315, 1318, 1345, 1346, 1357, 1359, 1370, 1373, 1374, 1388, 1408, 1423, 1472, 1477, 1478, 1495, 1509, 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2098, 2119, 2121, 2128, 2130, 2136, 2137, 2155, 2156, 2164, 2170, 2171, 2190, 2194, 2198], "interact": [10, 16, 24, 29, 36, 68, 891, 944, 1040, 1075, 1079, 2069, 2101, 2104, 2130, 2136, 2158, 2160, 2175, 2188, 2191, 2194, 2197, 2206], "final": [10, 20, 29, 31, 34, 38, 40, 50, 55, 56, 57, 59, 63, 64, 696, 699, 700, 770, 969, 987, 1000, 1145, 1184, 1203, 1242, 1269, 1387, 1405, 1410, 1502, 1533, 1535, 1552, 1598, 1611, 1762, 1841, 1974, 2021, 2097, 2099, 2100, 2101, 2119, 2136, 2139, 2141, 2144, 2147, 2149, 2151, 2152, 2155, 2157, 2158, 2166, 2167, 2188, 2190, 2192, 2203, 2204], "declin": 10, "conflict": [10, 25, 31, 51, 2158], "lack": [10, 12, 35, 968, 1351, 1352, 1379], "unfit": 10, "conduct": [10, 1772, 1885, 1998, 2165, 2184, 2188], "filial": 10, "romant": 10, "strength": 10, "candid": [10, 961, 2158], "letter": [10, 1145], "befit": 10, "candidaci": 10, "behind": [10, 29, 55, 2096, 2149, 2166, 2193], "75": [10, 1277, 1570, 1688, 1706, 1839, 1896, 2098, 2171], "unforeseen": 10, "circumst": [10, 50, 1001, 1239, 1501, 1509, 1510, 1511, 1512, 1513, 1514, 1663, 1664, 1665, 1666, 1667, 1668, 1672, 1740, 2133], "perman": [10, 68, 1797, 1798, 1799, 1800, 1801, 1802, 1803, 1804, 1813, 2130, 2167], "unavail": [10, 1056, 2099], "rank": [10, 24, 25, 29, 30, 31, 34, 35, 36, 37, 38, 40, 44, 48, 50, 51, 54, 56, 59, 70, 79, 1361, 1362, 1363, 1370, 1405, 1574, 1622, 1772, 1795, 1885, 1998, 2098, 2135, 2139, 2146, 2155, 2165, 2166, 2183, 2190, 2203, 2204, 2208], "elect": 10, "invit": [10, 2095], "convinc": 10, "approach": [10, 25, 29, 33, 68, 989, 1214, 1495, 1880, 1939, 1971, 2048, 2097, 2118, 2133, 2136, 2141, 2151, 2152, 2153, 2157, 2160, 2165, 2194, 2203], "interview": 10, "talk": [10, 53, 2104, 2143], "gather": [10, 29, 31, 33, 35, 36, 50, 59, 512, 2011, 2036, 2098, 2136, 2138, 2139, 2143, 2156, 2158, 2198], "read": [10, 13, 19, 20, 24, 29, 31, 36, 39, 40, 43, 50, 56, 59, 63, 68, 447, 955, 1018, 1103, 1128, 1196, 1198, 1199, 1231, 1323, 1387, 1877, 2104, 2119, 2120, 2126, 2130, 2131, 2133, 2135, 2137, 2143, 2147, 2151, 2158, 2160, 2165, 2188, 2191, 2192, 2194, 2196, 2203], "attend": [10, 745, 1588, 1626], "confer": [10, 1535], "pipelin": [10, 31, 68, 2096, 2165, 2194], "world": [10, 29, 33, 34, 40, 48, 50, 51, 55, 59, 1622, 1772, 2130, 2158, 2160, 2190, 2191, 2192, 2203], "cover": [10, 56, 57, 60, 68, 70, 1119, 1145, 1575, 1713, 1714, 1715, 2100, 2102, 2120, 2130, 2136, 2139, 2141, 2143, 2144, 2165, 2192, 2197, 2201, 2207], "push": [10, 42, 1105, 1107, 1214, 1381, 2048, 2149, 2159, 2191, 2203], "codeown": 10, "notifi": [10, 30, 51, 2167], "expert": [10, 33, 2157], "strongli": [10, 29, 40, 51, 57, 1520, 1521, 1522, 1526, 1993, 2095], "failur": [10, 29, 38, 40, 42, 43, 47, 48, 49, 50, 54, 55, 680, 948, 949, 1001, 1003, 1203, 1229, 1331, 1332, 1439, 2100, 2106, 2115, 2118, 2139, 2165, 2167, 2177, 2191, 2194, 2203], "revert": [10, 38, 59, 1619, 1748, 2167], "substanti": [10, 25, 2133, 2183, 2188, 2203], "syntact": [10, 43, 68], "incompat": [10, 15, 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2160, 2190, 2191, 2198, 2203], "quantiz": [12, 28, 55, 68, 218, 325, 337, 471, 472, 473, 474, 475, 477, 710, 711, 712, 713, 714, 715, 716, 717, 718, 719, 720, 721, 722, 723, 724, 725, 726, 727, 728, 729, 730, 733, 734, 735, 736, 737, 738, 739, 740, 741, 742, 743, 744, 745, 746, 747, 748, 749, 750, 751, 752, 753, 754, 755, 756, 757, 758, 759, 760, 761, 762, 763, 764, 765, 766, 767, 768, 769, 770, 771, 772, 773, 774, 775, 776, 777, 778, 779, 780, 781, 782, 783, 784, 785, 786, 787, 788, 789, 790, 791, 792, 793, 794, 795, 796, 797, 798, 799, 800, 801, 802, 803, 804, 805, 806, 807, 808, 809, 810, 811, 812, 813, 814, 815, 816, 817, 818, 819, 820, 821, 822, 823, 824, 825, 826, 827, 828, 829, 830, 831, 832, 833, 834, 835, 836, 837, 838, 839, 840, 841, 842, 843, 844, 845, 846, 847, 848, 849, 850, 851, 852, 853, 854, 855, 856, 857, 858, 859, 860, 861, 862, 863, 864, 865, 866, 867, 868, 869, 870, 871, 872, 873, 874, 875, 876, 877, 878, 879, 880, 881, 882, 883, 884, 885, 887, 888, 889, 890, 891, 892, 893, 961, 1130, 1159, 1160, 1897, 1898, 1899, 1900, 1901, 2036, 2096, 2156, 2173, 2176, 2177, 2180, 2181], "pred": [13, 57, 70, 73, 78, 79, 1018, 1547, 1772, 2145, 2157, 2165, 2198, 2203], "union": [13, 15, 24, 31, 33, 35, 36, 37, 40, 44, 48, 51, 55, 56, 59, 68, 1001, 1018, 1221, 1224, 1225, 1227, 1229, 1255, 1287, 1301, 1327, 1387, 1483, 1484, 1485, 1487, 1488, 1489, 1491, 1492, 1493, 1529, 1530, 1549, 1550, 1551, 1575, 1576, 1577, 1582, 1626, 1628, 1630, 1633, 1646, 1685, 1795, 1816, 1895, 1927, 1988, 2039, 2098, 2099, 2100, 2104, 2121, 2126, 2149, 2155, 2158, 2172, 2175, 2177, 2181], "true_fn": [13, 56, 57, 70, 73, 78, 79, 1018], "false_fn": [13, 56, 57, 70, 73, 78, 79, 1018], "operand": [13, 57, 70, 78, 79, 977, 1018, 1145, 2100, 2101, 2136, 2170, 2173], "Its": [13, 29, 56, 68, 1026, 1053, 1315, 1363, 1381, 1382, 1407, 1582, 1830, 1839, 1840, 1841, 1842, 1843, 1844, 1845, 1846, 1847, 1856, 1859, 1860, 1861, 1862, 1863, 1880, 1977, 2045, 2100, 2135, 2172], "uniqu": [13, 29, 40, 47, 50, 51, 53, 232, 512, 516, 847, 1306, 1351, 1352, 1356, 1362, 1363, 1365, 1374, 1376, 1378, 1379, 1416, 1423, 1973, 1977, 1997, 2033, 2095, 2097, 2106, 2111, 2136, 2158, 2159, 2165, 2166, 2167, 2170, 2172, 2175, 2183, 2188, 2206], "predic": [13, 70, 75, 78, 79, 1229], "unlock": [13, 2107], "architectur": [13, 37, 68, 1067, 1161, 1162, 1164, 1166, 1167, 1168, 1169, 1170, 1171, 1173, 1174, 1175, 1176, 1177, 1178, 1179, 1180, 1182, 1517, 1626, 1671, 1940, 2062, 2133, 2147, 2152, 2155, 2157, 2160, 2170, 2187, 2203], "prototyp": [13, 19, 20, 29, 33, 36, 55, 680, 937, 939, 943, 948, 949, 1018, 1325, 1630, 1642, 1643, 1843, 1844, 2096, 2107, 2113, 2120, 2121, 2126, 2140, 2144, 2149, 2163, 2165, 2170, 2173, 2179, 2180, 2181, 2184], "classif": [13, 1018, 1495, 1501, 1517, 1585, 1587, 1589, 1615, 1672, 2096, 2126, 2128, 2161], "co": [13, 55, 56, 57, 65, 70, 74, 75, 78, 79, 80, 81, 83, 200, 635, 636, 693, 979, 1001, 1018, 1086, 1204, 1207, 1208, 1213, 1273, 1274, 1367, 1515, 1516, 1519, 1866, 1867, 1875, 1881, 1889, 1950, 1954, 1955, 1956, 1957, 1959, 1979, 1980, 1981, 1983, 1984, 2098, 2104, 2119, 2156, 2157, 2170, 2175, 2177, 2189, 2196, 2198], "sin": [13, 15, 55, 56, 57, 64, 65, 70, 74, 75, 78, 79, 80, 81, 531, 669, 670, 910, 1001, 1018, 1086, 1204, 1206, 1207, 1208, 1210, 1213, 1274, 1367, 1417, 1889, 1951, 1957, 2098, 2104, 2119, 2121, 2130, 2156, 2170, 2171, 2175, 2189, 2194, 2196, 2198, 2201, 2203], "dynamicshapecondpred": 13, "dyn_shape_mod": 13, "eagerli": [13, 35, 1013, 1073, 1080, 2067, 2071, 2133, 2190, 2194, 2203], "vari": [13, 55, 1321, 1495, 1614, 1816, 1952, 2018, 2113, 2121, 2126, 2133, 2146, 2147, 2157, 2160, 2161, 2170, 2184, 2188, 2190, 2191, 2193, 2194, 2203, 2204, 2206], "inp2": 13, "assert": [13, 24, 25, 31, 35, 38, 61, 63, 64, 65, 68, 70, 76, 79, 80, 81, 624, 688, 692, 951, 953, 954, 1204, 1206, 1207, 1208, 1209, 1212, 1213, 1214, 1228, 1229, 1313, 1318, 1319, 1325, 1740, 1836, 2048, 2099, 2101, 2104, 2136, 2137, 2144, 2149, 2157, 2158, 2160, 2172, 2177, 2190, 2194, 2202, 2204], "export": [13, 16, 19, 29, 35, 39, 54, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 680, 833, 869, 960, 1003, 1008, 1010, 1018, 1039, 1223, 1233, 1318, 1320, 1321, 1327, 1333, 1817, 1818, 1837, 1838, 2096, 2097, 2099, 2100, 2104, 2136, 2156, 2159, 2165, 2182, 2185, 2190, 2195, 2201, 2202], "deploy": [13, 40, 50, 55, 2096, 2110, 2184, 2195], "dim_batch": 13, "dim": [13, 29, 33, 36, 37, 38, 55, 57, 64, 65, 68, 70, 72, 78, 79, 111, 113, 114, 115, 117, 133, 134, 135, 183, 203, 206, 209, 210, 211, 212, 213, 214, 228, 232, 259, 263, 280, 311, 312, 313, 314, 315, 316, 320, 321, 353, 379, 391, 406, 408, 409, 410, 413, 428, 429, 430, 431, 435, 436, 452, 461, 469, 478, 490, 491, 493, 504, 505, 511, 512, 513, 514, 515, 516, 517, 518, 536, 537, 540, 541, 543, 546, 551, 552, 554, 559, 564, 572, 577, 578, 582, 583, 584, 586, 587, 589, 604, 605, 608, 609, 610, 611, 614, 616, 703, 705, 706, 707, 709, 903, 904, 905, 939, 940, 942, 988, 995, 1016, 1018, 1025, 1036, 1053, 1055, 1124, 1125, 1126, 1127, 1128, 1137, 1143, 1161, 1162, 1164, 1165, 1166, 1167, 1168, 1169, 1170, 1171, 1172, 1173, 1174, 1175, 1176, 1177, 1178, 1179, 1180, 1182, 1184, 1185, 1204, 1214, 1223, 1256, 1269, 1279, 1287, 1288, 1289, 1290, 1292, 1337, 1347, 1348, 1368, 1372, 1382, 1384, 1385, 1395, 1403, 1413, 1415, 1416, 1418, 1421, 1423, 1442, 1473, 1474, 1475, 1476, 1477, 1478, 1486, 1516, 1517, 1518, 1521, 1525, 1527, 1532, 1547, 1553, 1572, 1588, 1589, 1590, 1616, 1618, 1630, 1632, 1633, 1670, 1671, 1687, 1690, 1699, 1707, 1713, 1714, 1715, 1724, 1725, 1727, 1729, 1733, 1740, 1746, 1747, 1755, 1772, 1790, 1791, 1797, 1801, 1803, 1810, 1811, 1824, 1827, 1830, 1841, 1886, 1893, 1896, 1916, 1917, 1922, 1923, 1928, 1929, 1930, 1933, 1934, 1964, 1967, 1968, 1972, 1975, 1978, 1985, 1988, 1990, 1991, 1992, 1993, 1996, 2011, 2015, 2016, 2017, 2018, 2021, 2022, 2030, 2031, 2032, 2033, 2035, 2043, 2044, 2048, 2049, 2097, 2098, 2104, 2120, 2121, 2137, 2138, 2149, 2155, 2156, 2170, 2171, 2173, 2180, 2184, 2190, 2198], "batch": [13, 25, 29, 35, 36, 38, 51, 54, 55, 59, 60, 61, 64, 65, 68, 70, 78, 79, 582, 583, 584, 586, 587, 696, 712, 713, 714, 715, 716, 717, 745, 770, 789, 795, 943, 948, 949, 969, 981, 982, 989, 992, 993, 994, 1001, 1036, 1133, 1135, 1145, 1207, 1208, 1214, 1312, 1345, 1346, 1347, 1348, 1349, 1351, 1352, 1353, 1354, 1355, 1356, 1357, 1358, 1359, 1360, 1361, 1362, 1363, 1364, 1365, 1367, 1368, 1369, 1370, 1371, 1372, 1373, 1374, 1375, 1376, 1377, 1378, 1379, 1380, 1383, 1384, 1385, 1388, 1396, 1405, 1406, 1410, 1494, 1495, 1496, 1497, 1498, 1501, 1509, 1510, 1515, 1517, 1518, 1520, 1521, 1522, 1524, 1525, 1526, 1528, 1533, 1534, 1535, 1536, 1541, 1542, 1544, 1545, 1546, 1547, 1548, 1552, 1553, 1554, 1555, 1556, 1557, 1564, 1565, 1566, 1573, 1574, 1585, 1586, 1587, 1588, 1589, 1591, 1594, 1595, 1596, 1597, 1598, 1600, 1614, 1615, 1622, 1626, 1627, 1628, 1629, 1630, 1631, 1632, 1634, 1653, 1658, 1660, 1661, 1671, 1672, 1674, 1675, 1676, 1679, 1680, 1681, 1682, 1685, 1698, 1699, 1700, 1724, 1732, 1740, 1758, 1759, 1762, 1771, 1772, 1789, 1816, 1817, 1819, 1820, 1822, 1839, 1840, 1842, 1843, 1844, 1845, 1847, 1859, 1860, 1861, 1862, 1867, 1868, 1875, 1880, 1883, 1885, 1895, 1899, 1979, 1980, 1981, 1983, 1984, 1988, 1993, 1997, 1998, 2020, 2023, 2024, 2026, 2045, 2048, 2104, 2119, 2120, 2121, 2126, 2129, 2133, 2137, 2138, 2144, 2146, 2153, 2160, 2165, 2170, 2175, 2176, 2184, 2188, 2190, 2191, 2193, 2194, 2197, 2201, 2204], "min": [13, 24, 29, 36, 43, 50, 55, 68, 115, 184, 185, 186, 187, 299, 705, 706, 707, 766, 768, 780, 781, 790, 816, 836, 838, 839, 840, 845, 904, 996, 997, 1086, 1125, 1159, 1160, 1239, 1276, 1347, 1361, 1362, 1368, 1372, 1374, 1379, 1385, 1405, 1468, 1500, 1540, 1568, 1570, 1590, 1603, 1610, 1662, 1699, 1703, 1733, 1735, 1741, 1759, 1841, 1861, 1866, 1867, 1877, 1883, 1885, 1895, 1997, 1998, 2006, 2024, 2025, 2026, 2027, 2098, 2113, 2119, 2155, 2156, 2160, 2163, 2165, 2184, 2190, 2194, 2198, 2209], "ep": [13, 55, 57, 722, 723, 724, 725, 726, 727, 733, 734, 746, 747, 759, 761, 762, 763, 764, 807, 833, 834, 836, 838, 839, 840, 845, 850, 948, 949, 1325, 1401, 1482, 1496, 1497, 1498, 1516, 1535, 1536, 1544, 1545, 1546, 1554, 1555, 1556, 1557, 1564, 1565, 1566, 1591, 1596, 1597, 1622, 1626, 1628, 1630, 1631, 1658, 1670, 1685, 1689, 1690, 1698, 1702, 1725, 1728, 1732, 1737, 1756, 1771, 1790, 1824, 1840, 1841, 1842, 1843, 1844, 1845, 1847, 1859, 1860, 1863, 1877, 1899, 2098, 2136, 2141, 2171, 2185, 2198, 2209], "dynamic_shap": [13, 55, 70, 72, 78, 79, 1222, 1223, 2152, 2155, 2184, 2200], "graphmodul": [13, 35, 55, 56, 57, 70, 71, 72, 73, 74, 75, 76, 78, 79, 80, 81, 82, 83, 866, 888, 889, 890, 891, 1217, 2160, 2181, 2189, 2192, 2193, 2202, 2203, 2204], "arg0_1": [13, 55, 57], "f32": [13, 55, 57, 70, 71, 72, 73, 74, 75, 76, 78, 79, 80, 81, 82, 83, 2203], "s0": [13, 55, 56, 70, 78, 79, 485, 1219, 1229, 2172, 2190, 2191, 2203, 2204], "sym_siz": [13, 56, 70, 78, 79, 2156, 2198], "sym": [13, 70, 78, 79, 80, 81, 1229, 1949, 1950, 1951, 1952, 1953, 1954, 1955, 1956, 1957, 1958, 1959], "gt": [13, 70, 78, 79, 295, 1270, 2098, 2119, 2141, 2156, 2198], "true_graph_0": [13, 56, 70, 73, 78, 79], "false_graph_0": [13, 56, 70, 73, 78, 79], "symbol": [13, 15, 23, 55, 56, 70, 79, 624, 831, 869, 1003, 1222, 1223, 1225, 1227, 1229, 1231, 1232, 1234, 1235, 1238, 1239, 1243, 1253, 1330, 1384, 1552, 1598, 2023, 2045, 2100, 2104, 2121, 2152, 2188, 2190, 2193, 2194, 2203, 2204], "sub": [13, 29, 37, 43, 59, 67, 70, 73, 78, 79, 561, 828, 890, 891, 1315, 1327, 1331, 1332, 1413, 1416, 1418, 1582, 1594, 1595, 1626, 1627, 1629, 1880, 1916, 1995, 2015, 2032, 2095, 2097, 2098, 2119, 2121, 2130, 2146, 2155, 2156, 2170, 2173, 2191, 2198, 2202, 2203], "datadependentcondpred": 13, "sum_1": [13, 68, 2189, 2191, 2192], "b8": [13, 70, 73, 78], "flatten": [13, 25, 33, 36, 55, 56, 59, 68, 70, 76, 903, 904, 1134, 1278, 1372, 1385, 1634, 1788, 1830, 1838, 1896, 1912, 1917, 1922, 2011, 2031, 2032, 2033, 2034, 2098, 2119, 2120, 2122, 2126, 2128, 2155, 2156, 2162, 2174, 2190], "closur": [13, 34, 70, 78, 79, 1003, 1839, 1840, 1841, 1842, 1843, 1844, 1845, 1846, 1847, 1857, 1859, 1860, 1861, 1862, 1863, 2192, 2195], "flat": [13, 33, 55, 1917, 1959, 2034, 2098, 2139, 2155, 2170], "_higher_order_op": 13, "condition": [13, 50, 945, 1018, 2203], "constraint": [13, 29, 33, 55, 59, 70, 71, 72, 73, 74, 75, 76, 78, 79, 80, 81, 82, 83, 806, 807, 938, 1018, 1077, 1089, 1208, 1222, 1223, 1225, 1228, 1229, 1239, 1740, 1772, 1790, 1816, 1863, 2100, 2137, 2139, 2141, 2144, 2170, 2188, 2190, 2191], "true_branch": [13, 1018], "false_branch": [13, 1018], "consist": [13, 15, 20, 24, 29, 31, 35, 38, 50, 51, 56, 68, 948, 992, 993, 994, 1018, 1237, 1315, 1345, 1351, 1352, 1354, 1356, 1357, 1358, 1359, 1383, 1385, 1415, 1582, 1630, 1792, 1795, 1838, 1880, 1895, 1997, 2097, 2100, 2104, 2107, 2120, 2121, 2136, 2137, 2139, 2140, 2144, 2146, 2148, 2155, 2157, 2159, 2170, 2183, 2189, 2192, 2197, 2204], "possibli": [13, 24, 50, 55, 57, 59, 862, 1018, 1241, 1242, 1315, 1331, 1358, 1363, 1582, 1772, 1880, 2097, 2104, 2121, 2148, 2158, 2193, 2206], "aka": [13, 29, 55, 56, 70, 78, 79, 961, 1018, 1542, 1697, 2121, 2130, 2173, 2190], "add_": [13, 29, 55, 62, 64, 70, 73, 487, 1018, 1203, 2098, 2119, 2131, 2149, 2170, 2188], "parallel_info": [14, 2132], "cppextens": 15, "setuptool": 15, "bare": 15, "pypa": 15, "userguid": 15, "ext_modul": 15, "libtorch_python": 15, "py_limited_api": 15, "libtorch": [15, 16, 2184], "dpy_limited_api": 15, "bdist_wheel": 15, "min_supported_cpython": 15, "enforc": [15, 23, 24, 25, 67, 807, 919, 934, 1315, 1552, 1582, 1598, 1841, 1880, 2100, 2144, 2174], "safeti": [15, 31, 40, 55, 57, 68, 485, 1003, 1004, 1239, 2097, 2100, 2120, 2148, 2195], "saniti": [15, 21, 31, 36, 2137], "practic": [15, 24, 29, 35, 37, 38, 56, 59, 485, 2096, 2097, 2107, 2118, 2120, 2130, 2136, 2139, 2141, 2144, 2149, 2158, 2165, 2172, 2182, 2191, 2193, 2194, 2203], "hexcod": 15, "buildextens": 15, 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2146, 2149, 2152, 2157, 2160, 2180], "comm_hook": 25, "comm_hook_st": 25, "barrier": [25, 40, 50, 2156], "map_loc": [25, 1319, 1323, 1387, 1772, 2095, 2108, 2111, 2158], "new_ddp_model": 25, "load_state_dict": [25, 31, 34, 59, 66, 414, 1315, 1387, 1582, 1762, 1839, 1840, 1841, 1842, 1843, 1844, 1845, 1846, 1847, 1850, 1851, 1859, 1860, 1861, 1862, 1863, 1864, 1865, 1866, 1867, 1868, 1869, 1870, 1871, 1872, 1873, 1874, 1875, 1876, 1877, 1878, 1879, 1880, 1881, 2095, 2144, 2149, 2157, 2160], "n_gpu": 25, "device_count": [25, 33, 2039], "got": [25, 29, 920, 934, 935, 957, 2136, 2149, 2177], "thank": [25, 38, 2136], "author": [25, 50, 59, 1214, 1859, 1902, 2048, 2129, 2184, 2192, 2194, 2196, 2199, 2200, 2201, 2204], "thij": 25, "par": 25, "torch_show_cpp_stacktrac": [26, 29], "torch_cpp_log_level": [26, 29], "c10": [26, 50, 2143, 2184, 2190], "glog": 26, "logger": [26, 29, 40, 812, 866, 2180, 2181], "info": [26, 29, 35, 36, 44, 48, 50, 59, 680, 921, 934, 935, 1039, 1040, 1044, 1229, 1346, 1357, 1359, 1360, 1364, 1377, 1405, 1434, 1435, 1836, 2096, 2098, 2104, 2106, 2135, 2136, 2137, 2155, 2158, 2185, 2190, 2192, 2193, 2203, 2204, 2208], "fatal": [26, 55, 2118, 2146], "torch_log": [26, 35, 36, 70, 72, 79, 680, 1001, 2106, 2135, 2190, 2191, 2192, 2194, 2204], "_log": [26, 35, 36, 2096, 2100, 2184, 2203, 2204, 2207], "home": [27, 2133], "fill_uninitialized_memori": [28, 498, 1146, 1147, 1148, 2036, 2148], "fill": [28, 29, 35, 36, 153, 172, 257, 258, 259, 285, 316, 320, 376, 399, 444, 445, 446, 448, 453, 480, 512, 607, 623, 972, 1133, 1146, 1147, 1148, 1200, 1201, 1346, 1357, 1359, 1525, 1626, 1680, 1727, 1834, 1835, 1904, 1905, 1906, 1907, 1908, 1909, 1997, 2036, 2093, 2094, 2098, 2108, 2120, 2121, 2128, 2133, 2136, 2149, 2156, 2170, 2172, 2177, 2193, 2198], "detriment": [28, 2148], "resize_": [28, 499, 1203, 1337, 2036, 2097, 2098, 2099, 2119, 2148, 2162, 2172, 2198], "empty_strid": [28, 2098, 2102, 2156, 2190, 2198], "empty_permut": [28, 2098, 2156, 2198], "empty_lik": [28, 31, 57, 2098, 2102, 2104, 2108, 2119, 2156, 2170], "brief": [29, 35, 1772, 2118, 2165], "introduct": [29, 38, 1077, 1772, 2097, 2101, 2119, 2131, 2144, 2155, 2165, 2170, 2175], "mpi": [29, 1772], "gloo": [29, 51, 1772, 2135, 2142, 2165], "recv": [29, 35, 1772, 2156, 2166], "broadcast": [29, 31, 34, 36, 37, 38, 59, 64, 96, 195, 397, 399, 400, 401, 512, 514, 516, 565, 695, 696, 697, 698, 699, 700, 701, 707, 745, 913, 969, 974, 977, 981, 982, 983, 984, 1021, 1052, 1086, 1139, 1140, 1145, 1150, 1190, 1191, 1192, 1193, 1256, 1258, 1272, 1282, 1340, 1341, 1348, 1361, 1365, 1370, 1371, 1373, 1376, 1384, 1404, 1409, 1410, 1420, 1467, 1470, 1479, 1482, 1495, 1516, 1535, 1588, 1661, 1670, 1733, 1740, 1772, 1838, 1884, 1892, 1915, 1917, 1994, 1998, 2016, 2021, 2051, 2096, 2100, 2119, 2120, 2126, 2135, 2155, 2156, 2171, 2194, 2198], "all_reduc": [29, 36, 53, 1772, 2156, 2188], "all_gath": [29, 36], "scatter": [29, 33, 34, 36, 37, 59, 512, 514, 516, 1518, 2036, 2098, 2138, 2139, 2156, 2165, 2198], "reduce_scatt": [29, 36, 2156], "all_to_al": [29, 36], "v1": [29, 59, 1384, 1884, 2095, 2135, 2165], "init_method": [29, 1772, 2165], "adher": [29, 1003, 2100, 2121, 2170], "some_fil": 29, "machine_nam": 29, "share_folder_nam": 29, "tcpstore": [29, 50], "past": [29, 55, 68, 85, 1050, 1103, 1108, 1122, 1123, 1444, 1772, 2138, 2196, 2197, 2200, 2203], "ask": [29, 63, 64, 2095, 2096, 2137, 2141, 2182, 2191, 2206], "infiniband": [29, 51, 1772, 2165], "interconnect": [29, 35], "gpudirect": 29, "ethernet": 29, "ip": [29, 50], "ib": 29, "upcom": [29, 2129], "nccl_socket_ifnam": 29, "eth0": 29, "gloo_socket_ifnam": 29, "eth1": 29, "eth2": 29, "eth3": 29, "imper": 29, "nccl_debug": 29, "nccl_debug_subsi": 29, "coll": 29, "hang": [29, 30, 34, 38, 1772, 2109, 2135, 2208], "topologi": [29, 31, 36, 37, 40, 2183], "effort": [29, 55, 2121, 2165, 2191, 2197], "socket": [29, 41, 2118, 2165], "nccl_socket_nthread": 29, "nccl_nsocks_perthread": 29, "cloud": [29, 2170, 2175], "aw": [29, 42, 1026], "gcp": [29, 2200], "primit": [29, 34, 35, 36, 50, 57, 1387, 2097, 2099, 2101, 2132, 2145, 2149, 2155, 2165, 2198], "kind": [29, 44, 55, 68, 70, 71, 72, 73, 74, 75, 76, 78, 79, 80, 81, 82, 83, 680, 1225, 1335, 1773, 1774, 1837, 1894, 1958, 2095, 2104, 2136, 2146, 2148, 2155, 2158, 2160, 2171, 2172, 2173], "connect": [29, 35, 40, 50, 1509, 1510, 1511, 1512, 1513, 1514, 1558, 1559, 1560, 1561, 1562, 1563, 1611, 2118, 2126, 2165, 2201], "advantag": [29, 50, 1495, 1542, 2135, 2138, 2151, 2152, 2165, 2170, 2188, 2203], "redund": [29, 59, 1161, 1162, 1164, 1166, 1180, 1182, 1993, 2152], "elimin": [29, 55, 68, 609, 1222, 2032, 2033, 2148, 2152, 2196, 2202], "thrash": 29, "recurr": [29, 770, 771, 1331, 1501, 1518, 1533, 1534, 1552, 1598, 1793, 1819, 1860, 2133], "device_mesh": [29, 36, 37, 59, 1772], "init_device_mesh": [29, 37], "inconsist": [29, 59, 894, 1911, 2136], "uuid": [29, 2130], "use_distribut": 29, "group_nam": [29, 47], "pg_option": 29, "url": [29, 50, 960, 2095, 2111, 2140, 2165], "encod": [29, 40, 47, 50, 57, 68, 1363, 1387, 1434, 1435, 1494, 1495, 1626, 1627, 1628, 1629, 1630, 1772, 1939, 1979, 1980, 1981, 1983, 1984, 2097, 2100, 2101, 2136, 2149, 2158, 2166, 2170], "ucc": 29, "plugin": [29, 2159, 2175, 2189], "c10d": [29, 40, 49, 51, 54, 680, 1772, 2135, 2156], "registr": [29, 34, 67, 1086, 1764, 1769, 1770, 1772, 1792, 1795, 2104, 2152, 2158, 2189], "lowercas": 29, "deadlock": [29, 1772], "invalid": [29, 43, 48, 1004, 1315, 1582, 1880, 2129, 2130, 2148, 2158, 2185, 2193], "job": [29, 40, 43, 44, 46, 47, 49, 50, 51, 53, 1466, 1772, 1868, 1875, 2133, 2143, 2159, 2175, 2183, 2191, 2203, 2206, 2208], "exchang": [29, 50, 1040, 1075, 1133, 2069, 2133, 2151], "timedelta": [29, 50, 2113], "abort": [29, 2133, 2208], "crash": [29, 43, 50, 1352, 2118, 2130, 2165, 2167, 2175, 2190, 2203], "corrupt": [29, 50, 1518, 2133, 2146, 2149], "torch_nccl_blocking_wait": [29, 2208], "processgroupopt": 29, "processgroupnccl": [29, 2135, 2207], "is_high_priority_stream": 29, "availbl": 29, "deeplearn": 29, "ncclconfig": 29, "ncclcomminit": 29, "lazi": [29, 1019, 1219, 1328, 1555, 1556, 1557, 1558, 1559, 1560, 1561, 1562, 1563, 1564, 1565, 1566, 1567, 1762, 2070, 2190], "ncclcommsplit": 29, "unnecessari": [29, 34, 57, 1781, 1782, 1823, 2100, 2121, 2130, 2133, 2136, 2149, 2158, 2174], "backend_nam": [29, 2039], "custom_backend": 29, "mesh_shap": 29, "mesh_dim_nam": 29, "dimension": [29, 36, 38, 56, 512, 514, 909, 915, 916, 917, 980, 987, 999, 1133, 1135, 1136, 1145, 1161, 1162, 1164, 1165, 1166, 1167, 1168, 1169, 1170, 1171, 1173, 1174, 1175, 1176, 1179, 1180, 1182, 1184, 1186, 1187, 1199, 1269, 1278, 1279, 1280, 1348, 1353, 1354, 1355, 1356, 1381, 1386, 1402, 1410, 1417, 1486, 1503, 1504, 1505, 1506, 1507, 1508, 1517, 1518, 1524, 1525, 1554, 1572, 1589, 1597, 1604, 1605, 1606, 1607, 1608, 1609, 1616, 1618, 1622, 1638, 1639, 1640, 1671, 1724, 1727, 1760, 1761, 1771, 1789, 1829, 1902, 1979, 1980, 1981, 1982, 1983, 1984, 1993, 2011, 2014, 2015, 2021, 2100, 2121, 2128, 2131, 2141, 2147, 2170, 2172, 2173, 2176, 2179, 2190], "layout": [29, 36, 37, 56, 68, 149, 190, 207, 232, 341, 342, 434, 444, 445, 446, 447, 448, 457, 543, 580, 581, 582, 583, 584, 586, 587, 616, 699, 894, 922, 927, 970, 979, 1054, 1146, 1147, 1148, 1158, 1163, 1181, 1197, 1200, 1201, 1227, 1273, 1274, 1335, 1386, 1402, 1410, 1420, 1478, 1705, 1834, 1835, 1863, 1904, 1905, 1906, 1907, 1908, 1909, 1910, 1911, 1949, 1950, 1951, 1952, 1953, 1954, 1955, 1956, 1957, 1958, 1959, 1973, 1974, 1976, 1977, 1978, 1979, 1980, 1981, 1982, 1983, 1984, 2020, 2025, 2027, 2093, 2094, 2095, 2097, 2098, 2099, 2102, 2126, 2133, 2156, 2158, 2170, 2175, 2176, 2177, 2198], "spmd": [29, 31, 36, 2183], "nd": [29, 1371, 2139, 2147], "scene": [29, 2149, 2175], "mesh": [29, 33, 36, 37, 2175], "mesh_1d": 29, "mesh_2d": 29, "dp": 29, "is_initi": 29, "is_mpi_avail": 29, "is_nccl_avail": 29, "is_gloo_avail": 29, "distributed_c10d": [29, 50], "is_xccl_avail": 29, "xccl": 29, "is_torchelastic_launch": 29, "elast": [29, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 52, 53, 54, 2096], "torchelast": [29, 32, 40, 42, 43, 44, 47, 49, 50, 51, 53, 54], "torchelastic_run_id": [29, 51], "proxi": [29, 31, 57, 1003, 1215, 1216, 1219, 2160, 2165, 2191], "rendezv": [29, 32, 40, 44, 47, 49, 54, 2135, 2165], "null": [29, 42, 44, 47, 70, 74], "discoveri": [29, 50, 2133, 2158], "reachabl": 29, "multicast": 29, "20": [29, 34, 38, 320, 695, 730, 738, 739, 744, 748, 749, 750, 751, 752, 753, 756, 766, 770, 771, 772, 773, 774, 775, 782, 1127, 1269, 1313, 1316, 1327, 1338, 1372, 1467, 1490, 1492, 1493, 1495, 1496, 1497, 1498, 1499, 1501, 1508, 1509, 1510, 1511, 1513, 1514, 1519, 1520, 1521, 1522, 1526, 1529, 1530, 1533, 1534, 1536, 1543, 1544, 1545, 1546, 1549, 1550, 1551, 1552, 1553, 1554, 1569, 1575, 1576, 1577, 1579, 1580, 1582, 1598, 1600, 1611, 1619, 1622, 1625, 1626, 1627, 1628, 1640, 1663, 1665, 1666, 1668, 1672, 1683, 1684, 1690, 1748, 1789, 1790, 1791, 1815, 1824, 1827, 1846, 1867, 1881, 1898, 2011, 2097, 2098, 2108, 2121, 2126, 2135, 2136, 2147, 2152, 2156, 2157, 2159, 2165, 2169, 2170, 2191, 2192, 2193], "23456": 29, "clean": [29, 35, 40, 55, 68, 1012, 1081, 2095, 2118, 2158], "fcntl": 29, "nf": 29, "init": [29, 37, 40, 43, 44, 55, 59, 1315, 1582, 1590, 1610, 1823, 1880, 2096, 2102, 2130, 2136, 2143, 2144, 2157, 2190], "brand": [29, 31, 2136], "succe": [29, 50, 55, 64, 2133, 2136, 2149, 2150, 2203, 2204, 2206], "unexpect": [29, 31, 39, 68, 894, 1036, 1196, 1199, 1202, 1315, 1582, 1825, 1838, 1880, 1988, 2097, 2130, 2136, 2147, 2191, 2193, 2194], "unsuccess": 29, "mnt": 29, "sharedfil": 29, "port": [29, 40, 49, 50, 51, 2108, 2142], "backend_str": 29, "uppercas": 29, "classmethod": [29, 31, 50, 70, 72, 742, 748, 749, 750, 755, 756, 766, 774, 804, 805, 806, 829, 830, 831, 834, 842, 883, 957, 1039, 1524, 1525, 1622, 1797, 1798, 1799, 1800, 1801, 1802, 1803, 1804, 1836, 2100, 2101, 2126, 2136, 2160, 2165, 2172, 2205], "register_backend": [29, 2165, 2189], "extended_api": 29, "instanti": [29, 34, 49, 50, 51, 59, 68, 870, 1525, 1680, 1792, 1816, 1823, 2095, 2097, 2099, 2100, 2104, 2121, 2133, 2136, 2144, 2149, 2152, 2172], "3rd": [29, 38, 50, 616, 2131], "processgroup": [29, 31, 34, 35, 36, 59, 1772], "four": [29, 1361, 1510, 2126, 2136, 2141, 2165, 2167], "distributedbackendopt": 29, "get_backend": [29, 50], "get_rank": [29, 59, 1622], "consecut": [29, 59, 609, 1818, 1864, 2021, 2032, 2033, 2120, 2155], "get_world_s": 29, "pattern": [29, 31, 35, 804, 805, 806, 808, 883, 890, 1086, 1518, 1642, 1819, 1974, 2097, 2100, 2121, 2126, 2130, 2133, 2136, 2138, 2142, 2149, 2161, 2162, 2170, 2188, 2190, 2191, 2193, 2202, 2206], "launcher": [29, 35], "pg": [29, 1772], "destructor": [29, 2118, 2167], "ncclcommabort": 29, "gc": [29, 485, 2167], "fault": [29, 32, 40, 49, 50, 1199, 2203], "toler": [29, 32, 40, 49, 50, 68, 704, 948, 949, 1304, 1331, 1332, 1370, 1373, 1388, 1838, 1846, 2097, 2177], "_after_": [29, 2135], "unsupport": [29, 40, 55, 57, 64, 70, 72, 77, 79, 84, 1014, 2097, 2101, 2120, 2121, 2149, 2157, 2165, 2170, 2194, 2199, 2203], "untest": [29, 2120], "grain": [29, 55, 887, 890, 1740, 2130, 2155, 2170, 2182, 2196, 2203, 2204], "plai": [29, 2192, 2194], "new_group": [29, 59, 1622], "opaqu": [29, 39, 1003, 1037, 1077, 1078, 1196, 2104, 2199], "use_local_synchron": 29, "group_desc": 29, "enqueu": [29, 67, 1040, 1044, 1045, 1446, 2055, 2133, 2166], "groupmemb": 29, "non_group_memb": 29, "significantli": [29, 55, 59, 938, 1772, 1839, 1840, 1842, 1843, 1844, 1845, 1847, 1859, 1860, 1861, 1862, 1939, 2152, 2153, 2170, 2189, 2203], "taken": [29, 35, 38, 55, 57, 67, 68, 70, 75, 894, 1166, 1168, 1191, 1192, 1501, 1517, 1525, 1589, 1597, 1672, 1680, 1697, 1720, 1771, 1943, 1976, 2121, 2128, 2130, 2132, 2133, 2136, 2138, 2139, 2143, 2149, 2155, 2158, 2159, 2204, 2206], "get_group_rank": 29, "global_rank": [29, 40], "translat": [29, 1269, 2130, 2151, 2152, 2167], "get_global_rank": 29, "group_rank": [29, 36, 37, 40, 51], "get_process_group_rank": 29, "inter": [29, 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[2, 2100, 2130, 2179, 2200, 2202], "disabl": [2, 24, 1007, 2097, 2130, 2179, 2194, 2195, 2203], "comput": [2, 65, 2130, 2147, 2166, 2179, 2194], "default": [2, 24, 25, 2099, 2130, 2133, 2141, 2162, 2191], "layout": [2, 2121, 2173], "manual": [2, 35], "In": [2, 9, 2130, 2131, 2179], "place": [2, 64, 2119, 2130, 2131, 2155, 2179], "oper": [2, 11, 20, 25, 29, 55, 57, 64, 84, 2099, 2100, 2105, 2107, 2119, 2120, 2121, 2130, 2134, 2136, 2143, 2150, 2155, 2156, 2160, 2170, 2176, 2179, 2193, 2201, 2203], "tensor": [2, 12, 16, 36, 37, 55, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 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"checkpoint": [6, 25, 31], "pytorch": [7, 8, 9, 10, 11, 16, 27, 29, 33, 36, 55, 57, 64, 2096, 2097, 2098, 2102, 2130, 2133, 2134, 2136, 2148, 2149, 2155, 2160, 2163, 2182, 2188, 2199, 2200, 2204, 2208], "govern": [7, 10, 11], "build": [7, 8, 11, 35, 2132, 2143, 2144, 2150], "ci": [7, 11], "how": [7, 25, 35, 62, 2095, 2130, 2136, 2157, 2158, 2192, 2193, 2194, 2200], "add": [7, 10, 96, 695], "new": [7, 8, 10, 2104, 2136], "maintain": [7, 10, 11], "contribut": [8, 2121, 2151], "guid": 8, "process": [8, 10, 24, 48, 2129], "get": [8, 32, 2140, 2149, 2155, 2182, 2194, 2196], "start": [8, 32, 48, 1435, 2140, 2182, 2196, 2204], "propos": 8, "featur": [8, 36, 2143, 2144, 2194], "report": [8, 2138, 2203], "issu": [8, 2097, 2201, 2203], "implement": [8, 35, 40, 50, 53, 57, 2095, 2135, 2141, 2155, 2161, 2163, 2167, 2191, 2193], "fix": [8, 51, 62, 260, 1183], "bug": 8, "ad": [8, 2136, 2155, 2191], "tutori": [8, 11, 2158, 2165], "improv": [8, 2144], "document": [8, 32, 2096], "particip": 8, "onlin": 8, "discuss": 8, "submit": 8, "pull": 8, "request": 8, "open": 8, "review": 8, "code": [8, 68, 2097, 2133, 2140, 2158, 2185, 2194, 2203], "readabl": 8, "test": [8, 2100, 2104, 2136, 2158, 2177, 2200], "case": [8, 2097, 2172], "make": [8, 10, 2188, 2191], "codebas": 8, "more": [8, 55, 60, 2165, 2182], "robust": 8, "triag": 8, "about": [8, 2130, 2165, 2193], "sourc": [8, 2140, 2148, 2150, 2158], "develop": [8, 55, 2096, 2151, 2182], "common": [8, 29, 68, 2143, 2160], "mistak": 8, "To": 8, "avoid": [8, 2146, 2148, 2155, 2158], "frequent": [8, 2097, 2138, 2155, 2160, 2194], "ask": [8, 2097, 2138, 2155, 2160, 2194], "question": [8, 2097, 2138, 2155, 2160, 2194], "On": [8, 25, 2099], "python": [8, 9, 29, 55, 64, 71, 72, 73, 74, 75, 76, 77, 2096, 2097, 2098, 2099, 2100, 2101, 2102, 2104, 2136, 2148, 2155, 2184, 2191], "doc": [8, 11, 2121, 2142], "c": [8, 11, 16, 2104, 2130, 2142, 2149, 2155, 2184], "overview": [8, 19, 20, 55, 68, 2151, 2152, 2158, 2159, 2170, 2189, 2192], "design": [9, 2100, 2135, 2165, 2166, 2167], "philosophi": 9, "principl": [9, 10], "1": [9, 35, 51, 62, 2097, 2137, 2191], "usabl": 9, "perform": [9, 11, 2144, 2149, 2193, 2200, 2201, 2204], "2": [9, 35, 62, 2097, 2133, 2137, 2138, 2160, 2163, 2199, 2200, 2204], "simpl": [9, 2099, 2100, 2144, 2152, 2166, 2203], "easi": 9, "3": [9, 51, 62], "first": [9, 2158], "best": [9, 2133, 2146, 2160, 2187], "class": [9, 36, 43, 68, 2097, 2099, 2100, 2102, 2155, 2157, 2158, 2176], "languag": [9, 2096, 2097, 2099, 2100, 2101], "interoper": 9, "mechan": [10, 2121, 2127, 2130, 2141], "summari": [10, 2160, 2203], "modul": [10, 11, 57, 61, 63, 68, 1582, 1763, 1764, 1765, 1766, 1767, 1768, 1769, 1770, 2097, 2098, 2099, 2100, 2102, 2122, 2130, 2136, 2144, 2149, 2158, 2160, 2199], "core": [10, 11, 2198], "lead": [10, 11], "bdfl": [10, 11], "nomin": [10, 2100], "confirm": 10, "remov": [10, 1813, 2119], "The": [10, 68, 2100, 2130, 2190], "re": [10, 2158], "scope": 10, "project": 10, "decis": 10, "uncontroversi": 10, "chang": [10, 51, 62, 2140, 2194, 2203], "controversi": 10, "gener": [10, 18, 30, 64, 68, 86, 2131, 2137, 2148, 2160, 2161, 2179, 2191, 2192, 2203, 2206, 2211], "polici": [10, 2190], "faq": [10, 2150], "respons": 11, "nn": [11, 61, 63, 1642, 1644, 1645, 1646, 1647, 1648, 1649, 1650, 1651, 1652, 1653, 1654, 1655, 1656, 1657, 1658, 1659, 1660, 1661, 1662, 1663, 1664, 1665, 1666, 1667, 1668, 1669, 1670, 1671, 1672, 1673, 1674, 1675, 1676, 1677, 1678, 1679, 1680, 1681, 1682, 1683, 1684, 1685, 1686, 1687, 1688, 1689, 1690, 1691, 1692, 1693, 1694, 1695, 1696, 1697, 1698, 1699, 1700, 1701, 1702, 1703, 1704, 1705, 1706, 1707, 1708, 1709, 1710, 1711, 1712, 1713, 1714, 1715, 1716, 1717, 1718, 1719, 1720, 1721, 1722, 1723, 1724, 1725, 1726, 1727, 1728, 1729, 1730, 1731, 1732, 1733, 1734, 1735, 1736, 1737, 1738, 1739, 1740, 1741, 1742, 1743, 1744, 1745, 1746, 1747, 1748, 1749, 1750, 1751, 1752, 1753, 1754, 1755, 1756, 1757, 1758, 1759, 1760, 1761, 1763, 1764, 1765, 1766, 1767, 1768, 1769, 1770, 1777, 1778, 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internal API": [[2190, "abridged-internal-api"]], "DimDynamic policy": [[2190, "dimdynamic-policy"]], "Unbacked SymInts": [[2190, "unbacked-symints"]], "CUDAGraph Trees": [[2188, "cudagraph-trees"]], "Background": [[2188, "background"], [2166, "background"], [2167, "background"]], "CUDAGraph": [[2188, "cudagraph"], [1037, "cudagraph"]], "PyTorch CUDAGraph Integration": [[2188, "pytorch-cudagraph-integration"]], "Make Graphed Callables": [[2188, "make-graphed-callables"]], "TorchDynamo Previous CUDA Graphs Integration": [[2188, "torchdynamo-previous-cuda-graphs-integration"]], "CUDAGraph Trees Integration": [[2188, "cudagraph-trees-integration"]], "Input Mutation Support": [[2188, "input-mutation-support"]], "Dynamic Shape Support": [[2188, "dynamic-shape-support"]], "NCCL Support": [[2188, "nccl-support"]], "Reasons for Skipping CUDAGraph": [[2188, "reasons-for-skipping-cudagraph"]], "Limitations": [[2188, "limitations"], [2195, "limitations"], [2155, "limitations"]], "Comparisons": [[2188, "comparisons"], [2100, "comparisons"]], "torch.compiler API reference": [[2186, "torch-compiler-api-reference"]], "AOTInductor: Ahead-Of-Time Compilation for Torch.Export-ed Models": [[2184, "aotinductor-ahead-of-time-compilation-for-torch-export-ed-models"]], "Model Compilation": [[2184, "model-compilation"]], "Inference in Python": [[2184, "inference-in-python"]], "Inference in C++": [[2184, "inference-in-c"]], "Troubleshooting": [[2184, "troubleshooting"], [2121, "troubleshooting"]], "Debugging Tools": [[2184, null], [36, "debugging-tools"]], "TorchInductor GPU Profiling": [[2197, "torchinductor-gpu-profiling"]], "Relevant Environment Variables": [[2197, "relevant-environment-variables"]], "Breakdown Model GPU Time": [[2197, "breakdown-model-gpu-time"]], "Benchmark Individual Triton Kernel": [[2197, "benchmark-individual-triton-kernel"]], "PyTorch 2.0 NNModule Support": [[2199, "pytorch-2-0-nnmodule-support"]], "NNModule Hooks Support": [[2199, "nnmodule-hooks-support"]], "nn.Module.__call__ Hooks Usage and limitations": [[2199, "nn-module-call-hooks-usage-and-limitations"]], "state_dict Hooks": [[2199, "state-dict-hooks"]], "Dynamo Overview": [[2192, "dynamo-overview"]], "Dynamo Internals": [[2192, "dynamo-internals"]], "What is a guard?": [[2192, "what-is-a-guard"]], "What is Dynamo doing?": [[2192, "what-is-dynamo-doing"]], "How to inspect artifacts generated by Dynamo?": [[2192, "how-to-inspect-artifacts-generated-by-dynamo"]], "torch.utils": [[2210, "module-torch.utils"]], "Fake tensor": [[2193, "fake-tensor"]], "Related work": [[2193, "related-work"]], "API: the important bits": [[2193, "api-the-important-bits"]], "Details": [[2193, "details"], [2126, "details"]], "About the tensor subclass": [[2193, "about-the-tensor-subclass"]], "How is each individual operator implemented?": [[2193, "how-is-each-individual-operator-implemented"]], "How does the converter work?": [[2193, "how-does-the-converter-work"]], "Performance characteristics": [[2193, "performance-characteristics"]], "Fake tensor of fake tensor?": [[2193, "fake-tensor-of-fake-tensor"]], "Interaction with dynamic shapes": [[2193, "interaction-with-dynamic-shapes"]], "Other resources": [[2193, "other-resources"]], "Frequently Asked Questions": [[2194, "frequently-asked-questions"], [2097, "frequently-asked-questions"], [2138, "frequently-asked-questions"], [2160, "frequently-asked-questions"], [2155, "frequently-asked-questions"], [8, "frequently-asked-questions"]], "Does torch.compile support training?": [[2194, "does-torch-compile-support-training"]], "Do you support Distributed code?": [[2194, "do-you-support-distributed-code"]], "Do I still need to export whole graphs?": [[2194, "do-i-still-need-to-export-whole-graphs"]], "Why is my code crashing?": [[2194, "why-is-my-code-crashing"]], "Why is compilation slow?": [[2194, "why-is-compilation-slow"]], "Why are you recompiling in production?": [[2194, "why-are-you-recompiling-in-production"]], "How are you speeding up my code?": [[2194, "how-are-you-speeding-up-my-code"]], "Why am I not seeing speedups?": [[2194, "why-am-i-not-seeing-speedups"]], "Graph Breaks": [[2194, "graph-breaks"], [2204, "graph-breaks"], [55, "graph-breaks"]], "Identifying the cause of a graph break": [[2194, "identifying-the-cause-of-a-graph-break"]], "Why didn\u2019t my code recompile when I changed it?": [[2194, "why-didnt-my-code-recompile-when-i-changed-it"]], "Why am I getting incorrect results?": [[2194, "why-am-i-getting-incorrect-results"]], "Why am I getting OOMs?": [[2194, "why-am-i-getting-ooms"]], "Does torch.func work with torch.compile (for grad and vmap transforms)?": [[2194, "does-torch-func-work-with-torch-compile-for-grad-and-vmap-transforms"]], "Calling torch.func transform inside of a function handled with torch.compile": [[2194, "calling-torch-func-transform-inside-of-a-function-handled-with-torch-compile"]], "Compiling torch.func.grad with torch.compile": [[2194, "compiling-torch-func-grad-with-torch-compile"]], "Compiling torch.vmap with torch.compile": [[2194, "compiling-torch-vmap-with-torch-compile"]], "Compiling functions besides the ones which are supported (escape hatch)": [[2194, "compiling-functions-besides-the-ones-which-are-supported-escape-hatch"]], "Does NumPy work with torch.compile?": [[2194, "does-numpy-work-with-torch-compile"]], "Which NumPy features does torch.compile support?": [[2194, "which-numpy-features-does-torch-compile-support"]], "Can I compile NumPy code using torch.compile?": [[2194, "can-i-compile-numpy-code-using-torch-compile"]], "Can I execute NumPy code on CUDA and compute gradients via torch.compile?": [[2194, "can-i-execute-numpy-code-on-cuda-and-compute-gradients-via-torch-compile"]], "How do I debug NumPy code under torch.compile?": [[2194, "how-do-i-debug-numpy-code-under-torch-compile"]], "I torch.compile some NumPy code and I did not see any speed-up.": [[2194, "i-torch-compile-some-numpy-code-and-i-did-not-see-any-speed-up"]], "Which API to use for fine grain tracing?": [[2194, "which-api-to-use-for-fine-grain-tracing"]], "How do I graph break on a function?": [[2194, "how-do-i-graph-break-on-a-function"]], "What\u2019s the difference between torch._dynamo.disable and torch._dynamo.disallow_in_graph": [[2194, "what-s-the-difference-between-torch-dynamo-disable-and-torch-dynamo-disallow-in-graph"]], "What\u2019s the difference between torch._dynamo.disable and torch._dynamo_skip": [[2194, "what-s-the-difference-between-torch-dynamo-disable-and-torch-dynamo-skip"]], "torch.overrides": [[2205, "module-torch.overrides"]], "Functions": [[2205, "functions"], [2099, "functions"], [2097, "functions"], [2171, "functions"], [2155, "functions"]], "PyTorch 2.0 Performance Dashboard": [[2200, "pytorch-2-0-performance-dashboard"]], "How to read the dashboard?": [[2200, "how-to-read-the-dashboard"]], "What is measured on the dashboard?": [[2200, "what-is-measured-on-the-dashboard"]], "Can I check if my PR affects TorchInductor\u2019s performance on the dashboard before merging?": [[2200, "can-i-check-if-my-pr-affects-torchinductor-s-performance-on-the-dashboard-before-merging"]], "How can I run any performance test locally?": [[2200, "how-can-i-run-any-performance-test-locally"]], "Torch Environment Variables": [[2207, "torch-environment-variables"]], "AOTInductor Minifier": [[2185, "aotinductor-minifier"]], "Example Code": [[2185, "example-code"]], "Minifier Launcher": [[2185, "minifier-launcher"]], "Minified Result": [[2185, "minified-result"]], "Getting Started": [[2196, "getting-started"], [8, "getting-started"]], "Using a pretrained model": [[2196, "using-a-pretrained-model"]], "Next Steps": [[2196, "next-steps"]], "Type Info": [[2209, "type-info"]], "torch.finfo": [[2209, "torch-finfo"]], "torch.iinfo": [[2209, "torch-iinfo"]], "Profiling to understand torch.compile performance": [[2201, "profiling-to-understand-torch-compile-performance"]], "What to use torch.profiler for:": [[2201, "what-to-use-torch-profiler-for"]], "Basics of using torch.profiler and viewing traces": [[2201, "basics-of-using-torch-profiler-and-viewing-traces"]], "Working around CUDA Graph profiling issues": [[2201, "working-around-cuda-graph-profiling-issues"]], "Understanding compilation time": [[2201, "understanding-compilation-time"]], "Finding graph breaks: \u201cTorch-Compiled Region\u201d and \u201cCompiledFunction\u201d": [[2201, "finding-graph-breaks-torch-compiled-region-and-compiledfunction"]], "Operator Kernels": [[2201, "operator-kernels"]], "Launch overhead": [[2201, "launch-overhead"]], "Custom Backends": [[2189, "custom-backends"]], "Overview": [[2189, "overview"], [68, "module-torch.fx"], [55, "overview"], [2152, "overview"], [2159, "module-torch.profiler"], [2151, "overview"], [20, "module-torch.cuda.tunable"], [19, "module-torch.cuda._sanitizer"]], "Registering Custom Backends": [[2189, "registering-custom-backends"]], "Custom Backends after AOTAutograd": [[2189, "custom-backends-after-aotautograd"]], "Examples": [[2189, "examples"], [2140, "examples"], [13, "examples"], [45, "examples"]], "Debugging Backend": [[2189, "debugging-backend"]], "Speedy Backend": [[2189, "speedy-backend"]], "Composable Backends": [[2189, "composable-backends"]], "Best Practices for Backends": [[2187, "best-practices-for-backends"]], "x86 CPU": [[2187, "x86-cpu"], [11, "x86-cpu"]], "Dynamo Deep-Dive": [[2191, "dynamo-deep-dive"]], "A Gentle Introduction to Dynamo": [[2191, "a-gentle-introduction-to-dynamo"]], "PEP 523: Adding a frame evaluation API to CPython": [[2191, "pep-523-adding-a-frame-evaluation-api-to-cpython"]], "Implementing CPython in Python": [[2191, "implementing-cpython-in-python"]], "Generating the Output Graph": [[2191, "generating-the-output-graph"]], "Making Dynamo Sound: Guards": [[2191, "making-dynamo-sound-guards"]], "Symbolic Shapes": [[2191, "symbolic-shapes"]], "Static by default": [[2191, "static-by-default"]], "0, 1 are always specialized": [[2191, "are-always-specialized"]], "Duck shaping": [[2191, "duck-shaping"]], "Guards on symbolic ints": [[2191, "guards-on-symbolic-ints"]], "Making Dynamo Complete: Graph Breaks": [[2191, "making-dynamo-complete-graph-breaks"]], "Conclusion": [[2191, "conclusion"]], "Footnotes": [[2191, "footnotes"]], "torch.xpu": [[2211, "module-torch.xpu"]], "Random Number Generator": [[2211, "random-number-generator"], [18, "random-number-generator"]], "Streams and events": [[2211, "streams-and-events"], [2116, "streams-and-events"], [17, "streams-and-events"], [18, "streams-and-events"]], "Memory management": [[2211, "memory-management"], [2142, "memory-management"], [2133, "memory-management"], [18, "memory-management"]], "torch.compile Troubleshooting": [[2203, "torch-compile-troubleshooting"]], "Setting Expectations": [[2203, "setting-expectations"]], "Compile times": [[2203, "compile-times"]], "Terminology": [[2203, "terminology"], [2100, "terminology"]], "Graph break": [[2203, "graph-break"]], "Guards": [[2203, "guards"]], "Recompilation": [[2203, "recompilation"]], "Dynamic Shapes": [[2203, "dynamic-shapes"]], "Logging Tools": [[2203, "logging-tools"]], "tlparse / TORCH_TRACE": [[2203, "tlparse-torch-trace"]], "TORCH_LOGS": [[2203, "torch-logs"]], "tlparse vs. TORCH_LOGS": [[2203, "tlparse-vs-torch-logs"]], "Simple Workarounds": [[2203, "simple-workarounds"]], "Where to apply torch.compile?": [[2203, "where-to-apply-torch-compile"]], "Disabling and Suppressing Errors": [[2203, "disabling-and-suppressing-errors"]], "Resolving graph breaks": [[2203, "resolving-graph-breaks"]], "Data-dependent operations": [[2203, "data-dependent-operations"]], "Custom ops": [[2203, "custom-ops"]], "Printing": [[2203, "printing"]], "Incorrect code": [[2203, "incorrect-code"]], "Dealing with recompilations": [[2203, "dealing-with-recompilations"]], "Is dynamic shapes enabled?": [[2203, "is-dynamic-shapes-enabled"]], "Changing the cache size limit": [[2203, "changing-the-cache-size-limit"]], "Wrapping constants with tensors": [[2203, "wrapping-constants-with-tensors"]], "Reporting Issues": [[2203, "reporting-issues"], [8, "reporting-issues"]], "Ablation": [[2203, "ablation"]], "Bisecting": [[2203, "bisecting"]], "Creating a reproducer": [[2203, "creating-a-reproducer"]], "Minifier": [[2203, "minifier"]], "Debugging Deeper": [[2203, "debugging-deeper"]], "TorchDynamo": [[2203, "torchdynamo"], [11, "torchdynamo"]], "Logging what Dynamo is tracing": [[2203, "logging-what-dynamo-is-tracing"]], "Breakpointing Dynamo tracing": [[2203, "breakpointing-dynamo-tracing"]], "Bytecode generation errors": [[2203, "bytecode-generation-errors"]], "AOTAutograd": [[2203, "aotautograd"]], "Summary of TORCH_LOGS options": [[2203, "summary-of-torch-logs-options"]], "Related Articles": [[2203, "related-articles"]], "PyTorch 2.0 Troubleshooting (old)": [[2204, "pytorch-2-0-troubleshooting-old"]], "Title": [[2204, "id1"]], "Diagnosing Runtime Errors": [[2204, "diagnosing-runtime-errors"]], "Torchdynamo Errors": [[2204, "torchdynamo-errors"]], "Diagnosing TorchInductor Errors": [[2204, "diagnosing-torchinductor-errors"]], "Minifying TorchInductor Errors": [[2204, "minifying-torchinductor-errors"]], "Minifying Backend Compiler Errors": [[2204, "minifying-backend-compiler-errors"]], "Performance Profiling": [[2204, "performance-profiling"]], "Accessing TorchDynamo Profiler": [[2204, "accessing-torchdynamo-profiler"]], "TorchInductor Debugging using TORCH_COMPILE_DEBUG": [[2204, "torchinductor-debugging-using-torch-compile-debug"]], "Identifying the Cause of a Graph Break": [[2204, "identifying-the-cause-of-a-graph-break"]], "Excessive Recompilation": [[2204, "excessive-recompilation"]], "Accuracy Debugging": [[2204, "accuracy-debugging"]], "Extended Debugging": [[2204, "extended-debugging"]], "Cold Start Timing and Cache Corruption Debugging": [[2204, "cold-start-timing-and-cache-corruption-debugging"]], "Understanding CUDA Memory Usage": [[2206, "understanding-cuda-memory-usage"]], "Generating a Snapshot": [[2206, "generating-a-snapshot"]], "Using the visualizer": [[2206, "using-the-visualizer"]], "Active Memory Timeline": [[2206, "active-memory-timeline"]], "Allocator State History": [[2206, "allocator-state-history"]], "Snapshot API Reference": [[2206, "snapshot-api-reference"]], "PYTORCH ProcessGroupNCCL Environment Variables": [[2208, "pytorch-processgroupnccl-environment-variables"]], "TorchDynamo APIs for fine-grained tracing": [[2195, "torchdynamo-apis-for-fine-grained-tracing"]], "TorchDynamo APIs to control fine-grained tracing": [[2195, "id1"]], "torch.compiler.disable": [[2195, "torch-compiler-disable"], [1007, "torch-compiler-disable"]], "torch._dynamo.disallow_in_graph": [[2195, "torch-dynamo-disallow-in-graph"]], "torch.compiler.allow_in_graph": [[2195, "torch-compiler-allow-in-graph"], [1003, "torch-compiler-allow-in-graph"]], "torch.nn.functional.pairwise_distance": [[1728, "torch-nn-functional-pairwise-distance"]], "torch.nn.functional.selu": [[1741, "torch-nn-functional-selu"]], "torch.nn.functional.relu": [[1734, "torch-nn-functional-relu"]], "torch.nn.functional.softplus": [[1748, "torch-nn-functional-softplus"]], "torch.nn.functional.rrelu": [[1738, "torch-nn-functional-rrelu"]], "torch.nn.functional.silu": [[1743, "torch-nn-functional-silu"]], "torch.nn.functional.rms_norm": [[1737, "torch-nn-functional-rms-norm"]], "torch.nn.functional.pixel_shuffle": [[1730, "torch-nn-functional-pixel-shuffle"]], "torch.nn.functional.upsample_nearest": [[1761, "torch-nn-functional-upsample-nearest"]], "torch.nn.functional.softshrink": [[1749, "torch-nn-functional-softshrink"]], "torch.nn.functional.torch.nn.parallel.data_parallel": [[1755, "torch-nn-functional-torch-nn-parallel-data-parallel"]], "torch.nn.modules.module.register_module_full_backward_hook": [[1767, "torch-nn-modules-module-register-module-full-backward-hook"]], "torch.nn.functional.triplet_margin_with_distance_loss": [[1757, "torch-nn-functional-triplet-margin-with-distance-loss"]], "torch.nn.modules.module.register_module_backward_hook": [[1763, "torch-nn-modules-module-register-module-backward-hook"]], "torch.nn.modules.module.register_module_forward_hook": [[1765, "torch-nn-modules-module-register-module-forward-hook"]], "torch.nn.functional.multilabel_margin_loss": [[1722, "torch-nn-functional-multilabel-margin-loss"]], "torch.nn.functional.softsign": [[1750, "torch-nn-functional-softsign"]], "torch.nn.functional.max_unpool1d": [[1716, "torch-nn-functional-max-unpool1d"]], "torch.nn.functional.normalize": [[1725, "torch-nn-functional-normalize"]], "LazyModuleMixin": [[1762, "lazymodulemixin"]], "torch.nn.functional.soft_margin_loss": [[1745, "torch-nn-functional-soft-margin-loss"]], "torch.nn.modules.module.register_module_forward_pre_hook": [[1766, "torch-nn-modules-module-register-module-forward-pre-hook"]], "torch.nn.functional.pad": [[1727, "torch-nn-functional-pad"]], "torch.nn.functional.poisson_nll_loss": [[1732, "torch-nn-functional-poisson-nll-loss"]], "torch.nn.functional.multi_margin_loss": [[1721, "torch-nn-functional-multi-margin-loss"]], "torch.nn.functional.tanh": [[1751, "torch-nn-functional-tanh"]], "torch.nn.functional.sigmoid": [[1742, "torch-nn-functional-sigmoid"]], "torch.nn.functional.mish": [[1719, "torch-nn-functional-mish"]], "torch.nn.functional.relu_": [[1736, "torch-nn-functional-relu"]], "torch.nn.functional.one_hot": [[1726, "torch-nn-functional-one-hot"]], "torch.nn.functional.prelu": [[1733, "torch-nn-functional-prelu"]], "torch.nn.functional.smooth_l1_loss": [[1744, "torch-nn-functional-smooth-l1-loss"]], "torch.nn.functional.upsample_bilinear": [[1760, "torch-nn-functional-upsample-bilinear"]], "torch.nn.functional.scaled_dot_product_attention": [[1740, "torch-nn-functional-scaled-dot-product-attention"]], "torch.nn.functional.tanhshrink": [[1752, "torch-nn-functional-tanhshrink"]], "torch.nn.functional.triplet_margin_loss": [[1756, "torch-nn-functional-triplet-margin-loss"]], "torch.nn.functional.unfold": [[1758, "torch-nn-functional-unfold"]], "torch.nn.functional.multilabel_soft_margin_loss": [[1723, "torch-nn-functional-multilabel-soft-margin-loss"]], "torch.nn.functional.threshold": [[1753, "torch-nn-functional-threshold"]], "torch.nn.functional.max_unpool2d": [[1717, "torch-nn-functional-max-unpool2d"]], "torch.nn.functional.softmin": [[1747, "torch-nn-functional-softmin"]], "torch.nn.functional.pdist": [[1729, "torch-nn-functional-pdist"]], "torch.nn.functional.nll_loss": [[1724, "torch-nn-functional-nll-loss"]], "torch.nn.modules.module.register_module_buffer_registration_hook": [[1764, "torch-nn-modules-module-register-module-buffer-registration-hook"]], "torch.nn.functional.max_unpool3d": [[1718, 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"my-recurrent-network-doesn-t-work-with-data-parallelism"]], "Quantization": [[2160, "module-torch.ao.quantization"]], "Introduction to Quantization": [[2160, "introduction-to-quantization"]], "Quantization API Summary": [[2160, "quantization-api-summary"]], "Eager Mode Quantization": [[2160, "eager-mode-quantization"]], "Post Training Dynamic Quantization": [[2160, "post-training-dynamic-quantization"]], "Post Training Static Quantization": [[2160, "post-training-static-quantization"]], "Quantization Aware Training for Static Quantization": [[2160, "quantization-aware-training-for-static-quantization"]], "Model Preparation for Eager Mode Static Quantization": [[2160, "model-preparation-for-eager-mode-static-quantization"]], "(Prototype - maintenance mode) FX Graph Mode Quantization": [[2160, "prototype-maintenance-mode-fx-graph-mode-quantization"]], "(Prototype) PyTorch 2 Export Quantization": [[2160, "prototype-pytorch-2-export-quantization"]], "Quantization Stack": [[2160, 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[[2160, "quantization-custom-module-api"]], "Best Practices": [[2160, "best-practices"]], "Common Errors": [[2160, "common-errors"]], "Passing a non-quantized Tensor into a quantized kernel": [[2160, "passing-a-non-quantized-tensor-into-a-quantized-kernel"]], "Passing a quantized Tensor into a non-quantized kernel": [[2160, "passing-a-quantized-tensor-into-a-non-quantized-kernel"]], "Saving and Loading Quantized models": [[2160, "saving-and-loading-quantized-models"]], "Symbolic Trace Error when using FX Graph Mode Quantization": [[2160, "symbolic-trace-error-when-using-fx-graph-mode-quantization"]], "torch.Size": [[2169, "torch-size"]], "Getting Started on Intel GPU": [[2140, "getting-started-on-intel-gpu"]], "Hardware Prerequisite": [[2140, "hardware-prerequisite"]], "Software Prerequisite": [[2140, "software-prerequisite"]], "Binaries": [[2140, "binaries"]], "From Source": [[2140, "from-source"]], "Check availability for Intel GPU": [[2140, "check-availability-for-intel-gpu"]], 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[[2182, null]], "HowTo for PyTorch Backend Vendors": [[2182, null]], "MPS backend": [[2145, "mps-backend"]], "torch.optim": [[2157, "module-torch.optim"]], "How to use an optimizer": [[2157, "how-to-use-an-optimizer"]], "Constructing it": [[2157, "constructing-it"]], "Per-parameter options": [[2157, "per-parameter-options"]], "Taking an optimization step": [[2157, "taking-an-optimization-step"]], "optimizer.step()": [[2157, "optimizer-step"]], "optimizer.step(closure)": [[2157, "optimizer-step-closure"]], "Base class": [[2157, "base-class"]], "Algorithms": [[2157, "algorithms"]], "How to adjust learning rate": [[2157, "how-to-adjust-learning-rate"]], "How to utilize named parameters to load optimizer state dict": [[2157, "how-to-utilize-named-parameters-to-load-optimizer-state-dict"]], "Weight Averaging (SWA and EMA)": [[2157, "weight-averaging-swa-and-ema"]], "Constructing averaged models": [[2157, "constructing-averaged-models"]], "Custom averaging strategies": [[2157, 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"Construction of sparse compressed tensors": [[2170, "construction-of-sparse-compressed-tensors"]], "Supported operations": [[2170, "supported-operations"]], "Linear Algebra operations": [[2170, "linear-algebra-operations"]], "Tensor methods and sparse": [[2170, "tensor-methods-and-sparse"]], "Torch functions specific to sparse Tensors": [[2170, "torch-functions-specific-to-sparse-tensors"]], "Other functions": [[2170, "other-functions"]], "Zero-preserving unary functions": [[2170, "zero-preserving-unary-functions"]], "Example: AlexNet from PyTorch to ONNX": [[2155, "example-alexnet-from-pytorch-to-onnx"]], "Tracing vs Scripting": [[2155, "tracing-vs-scripting"]], "Avoiding Pitfalls": [[2155, "avoiding-pitfalls"]], "Avoid NumPy and built-in Python types": [[2155, "avoid-numpy-and-built-in-python-types"]], "Avoid Tensor.data": [[2155, "avoid-tensor-data"]], "Avoid in-place operations when using tensor.shape in tracing mode": [[2155, 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[[2149, "serializing-torch-nn-modules-and-loading-them-in-c"]], "Saving and loading ScriptModules across PyTorch versions": [[2149, "saving-and-loading-scriptmodules-across-pytorch-versions"]], "torch.div performing integer division": [[2149, "torch-div-performing-integer-division"]], "torch.full always inferring a float dtype": [[2149, "torch-full-always-inferring-a-float-dtype"]], "Config": [[2149, "module-torch.utils.serialization"]], "CPU threading and TorchScript inference": [[2132, "cpu-threading-and-torchscript-inference"]], "Build options": [[2132, "build-options"]], "Runtime API": [[2132, "runtime-api"]], "Tuning the number of threads": [[2132, "tuning-the-number-of-threads"]], "CUDA semantics": [[2133, "cuda-semantics"]], "TensorFloat-32 (TF32) on Ampere (and later) devices": [[2133, "tensorfloat-32-tf32-on-ampere-and-later-devices"]], "Reduced Precision Reduction in FP16 GEMMs": [[2133, "reduced-precision-reduction-in-fp16-gemms"]], "Reduced Precision Reduction in BF16 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"Packaging your first model": [[2158, "packaging-your-first-model"]], "How do I\u2026": [[2158, "how-do-i"]], "See what is inside a package?": [[2158, "see-what-is-inside-a-package"]], "Treat the package like a ZIP archive": [[2158, "treat-the-package-like-a-zip-archive"]], "Use the file_structure() API": [[2158, "use-the-file-structure-api"]], "See why a given module was included as a dependency?": [[2158, "see-why-a-given-module-was-included-as-a-dependency"]], "Include arbitrary resources with my package and access them later?": [[2158, "include-arbitrary-resources-with-my-package-and-access-them-later"]], "Customize how a class is packaged?": [[2158, "customize-how-a-class-is-packaged"]], "Test in my source code whether or not it is executing inside a package?": [[2158, "test-in-my-source-code-whether-or-not-it-is-executing-inside-a-package"]], "Patch code into a package?": [[2158, "patch-code-into-a-package"]], "Access package contents from packaged code?": [[2158, "access-package-contents-from-packaged-code"]], "Distinguish between packaged code and non-packaged code?": [[2158, "distinguish-between-packaged-code-and-non-packaged-code"]], "Re-export an imported object?": [[2158, "re-export-an-imported-object"]], "Package a TorchScript module?": [[2158, "package-a-torchscript-module"]], "Explanation": [[2158, "explanation"]], "torch.package Format Overview": [[2158, "torch-package-format-overview"]], "Framework files": [[2158, "framework-files"]], "User files": [[2158, "user-files"]], "How torch.package finds your code\u2019s dependencies": [[2158, "how-torch-package-finds-your-code-s-dependencies"]], "Analyzing an object\u2019s dependencies": [[2158, "analyzing-an-object-s-dependencies"]], "Analyzing a module\u2019s dependencies": [[2158, "analyzing-a-module-s-dependencies"]], "Dependency Management": [[2158, "dependency-management"]], "intern": [[2158, "intern"]], "extern": [[2158, "extern"]], "mock": [[2158, "mock"]], "Refactoring": [[2158, 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1871, 2018, 2100, 2119, 2130, 2135, 2138, 2141, 2149, 2150, 2155, 2159, 2160, 2165, 2166, 2167, 2170, 2172, 2175, 2188, 2194, 2196, 2203], "been": [2, 6, 8, 10, 12, 18, 20, 24, 25, 29, 30, 33, 34, 35, 37, 38, 40, 43, 50, 54, 55, 57, 62, 63, 68, 85, 87, 485, 487, 680, 770, 929, 930, 934, 1006, 1040, 1044, 1049, 1081, 1084, 1144, 1149, 1203, 1318, 1319, 1323, 1325, 1330, 1387, 1444, 1445, 1457, 1533, 1540, 1552, 1598, 1653, 1688, 1772, 1781, 1782, 1792, 1797, 1801, 1803, 1823, 1824, 1839, 1866, 1867, 1868, 1875, 1877, 1895, 1993, 2011, 2039, 2050, 2054, 2073, 2104, 2106, 2108, 2113, 2118, 2119, 2120, 2121, 2126, 2129, 2130, 2132, 2133, 2139, 2141, 2142, 2144, 2146, 2148, 2150, 2153, 2157, 2158, 2165, 2167, 2170, 2175, 2184, 2185, 2188, 2189, 2191, 2194, 2199, 2203, 2204], "longer": [2, 29, 53, 63, 64, 485, 697, 919, 934, 948, 949, 953, 1312, 1319, 1772, 1827, 1830, 2110, 2113, 2130, 2133, 2159, 2165, 2167, 2188, 2203, 2204], "find": [2, 8, 15, 29, 50, 64, 68, 1145, 1222, 1225, 1269, 1337, 1388, 1512, 1513, 1514, 1552, 1837, 1885, 1896, 1931, 1998, 2095, 2108, 2110, 2118, 2120, 2121, 2130, 2132, 2133, 2135, 2136, 2138, 2141, 2146, 2148, 2153, 2155, 2156, 2160, 2165, 2170, 2173, 2175, 2177, 2180, 2190, 2191, 2192, 2193, 2194, 2195, 2196, 2197, 2202, 2203, 2204, 2205, 2207], "quick": [2, 8, 62, 2107, 2144, 2154, 2186, 2201], "guid": [2, 9, 24, 29, 1327, 1827, 2096, 2104, 2133, 2137, 2140, 2158, 2160, 2183, 2201], "var": [2, 43, 48, 50, 54, 1496, 1497, 1498, 1535, 1536, 1544, 1545, 1546, 1554, 1622, 1685, 1899, 2044, 2098, 2100, 2119, 2156, 2190, 2198, 2204], "thing": [2, 4, 8, 9, 29, 35, 55, 57, 64, 68, 1203, 1325, 1417, 1494, 1690, 1969, 2099, 2104, 2130, 2133, 2136, 2137, 2138, 2141, 2146, 2155, 2158, 2160, 2161, 2167, 2188, 2191, 2193, 2194, 2201, 2203, 2204], "detach": [2, 6, 221, 414, 447, 457, 998, 1202, 1315, 1501, 1582, 1672, 1690, 1880, 1973, 2014, 2097, 2098, 2119, 2136, 2138, 2155, 2156, 2162, 2170, 2174, 2176, 2202], "register_hook": [2, 2119, 2130], "name": [2, 3, 4, 15, 20, 25, 29, 31, 33, 35, 37, 38, 40, 43, 44, 47, 48, 50, 53, 55, 56, 59, 68, 70, 71, 72, 73, 74, 75, 76, 78, 79, 80, 81, 82, 83, 680, 707, 757, 758, 767, 804, 828, 829, 830, 831, 842, 868, 883, 887, 961, 964, 966, 1011, 1013, 1056, 1069, 1197, 1202, 1212, 1220, 1222, 1227, 1229, 1313, 1315, 1316, 1323, 1326, 1331, 1332, 1346, 1351, 1352, 1358, 1359, 1361, 1362, 1363, 1364, 1374, 1375, 1377, 1379, 1380, 1387, 1582, 1633, 1641, 1764, 1769, 1770, 1772, 1789, 1790, 1791, 1792, 1794, 1795, 1796, 1797, 1798, 1799, 1800, 1801, 1802, 1803, 1804, 1805, 1806, 1807, 1808, 1809, 1810, 1811, 1812, 1813, 1814, 1815, 1824, 1825, 1827, 1836, 1837, 1839, 1840, 1841, 1842, 1843, 1844, 1845, 1846, 1847, 1849, 1856, 1859, 1860, 1861, 1862, 1863, 1880, 1927, 1937, 2037, 2039, 2042, 2064, 2095, 2096, 2097, 2098, 2100, 2101, 2104, 2106, 2111, 2112, 2113, 2118, 2121, 2128, 2130, 2133, 2135, 2136, 2141, 2143, 2144, 2149, 2152, 2155, 2156, 2158, 2159, 2165, 2166, 2172, 2175, 2176, 2177, 2179, 2180, 2181, 2184, 2185, 2189, 2191, 2192, 2194, 2197, 2200, 2201, 2202, 2203, 2204, 2205, 2206, 2209], "factori": [2, 3, 12, 38, 40, 44, 48, 50, 64, 834, 842, 968, 1145, 1642, 1828, 1837, 1935, 2098, 2104, 2108, 2120, 2133, 2136, 2156, 2173, 2176, 2190, 2193], "ones": [2, 4, 6, 20, 24, 29, 34, 36, 37, 38, 55, 57, 59, 64, 65, 67, 68, 70, 75, 253, 312, 401, 444, 445, 447, 514, 806, 888, 922, 938, 940, 941, 942, 943, 944, 948, 971, 983, 1001, 1128, 1136, 1158, 1202, 1209, 1210, 1213, 1312, 1315, 1327, 1336, 1362, 1378, 1387, 1495, 1515, 1524, 1528, 1535, 1536, 1554, 1582, 1586, 1587, 1589, 1591, 1597, 1634, 1643, 1644, 1645, 1679, 1740, 1758, 1762, 1771, 1772, 1797, 1798, 1799, 1800, 1801, 1802, 1803, 1804, 1806, 1807, 1820, 1822, 1825, 1835, 1836, 1880, 1899, 1916, 1934, 1939, 1964, 2017, 2021, 2039, 2051, 2098, 2100, 2102, 2104, 2108, 2119, 2120, 2130, 2131, 2133, 2136, 2145, 2147, 2148, 2155, 2156, 2157, 2158, 2160, 2165, 2167, 2169, 2171, 2172, 2173, 2176, 2197, 2202, 2203, 2204], "autograd_tensor": 2, "kwarg": [2, 6, 15, 24, 29, 30, 31, 33, 34, 35, 37, 50, 55, 56, 59, 68, 70, 579, 602, 757, 770, 772, 773, 834, 836, 838, 839, 840, 842, 845, 919, 934, 935, 1005, 1040, 1041, 1042, 1044, 1086, 1087, 1105, 1202, 1213, 1214, 1216, 1227, 1229, 1315, 1318, 1320, 1379, 1405, 1543, 1571, 1582, 1613, 1617, 1621, 1623, 1624, 1762, 1772, 1797, 1802, 1806, 1816, 1823, 1825, 1828, 1839, 1840, 1841, 1842, 1843, 1844, 1845, 1846, 1847, 1854, 1855, 1859, 1860, 1861, 1862, 1863, 1880, 1927, 2033, 2048, 2054, 2095, 2100, 2101, 2104, 2136, 2137, 2144, 2152, 2155, 2157, 2165, 2172, 2176, 2181, 2189, 2192, 2202, 2203, 2204, 2205], "base": [2, 4, 8, 10, 13, 15, 16, 21, 24, 29, 30, 31, 33, 34, 35, 36, 37, 38, 40, 43, 48, 50, 51, 53, 55, 56, 57, 59, 64, 68, 680, 803, 808, 814, 816, 834, 835, 837, 838, 839, 840, 842, 845, 848, 862, 890, 891, 929, 932, 934, 945, 946, 955, 1001, 1026, 1106, 1107, 1122, 1143, 1145, 1188, 1235, 1246, 1269, 1279, 1317, 1329, 1341, 1379, 1390, 1392, 1394, 1402, 1495, 1518, 1555, 1582, 1585, 1586, 1587, 1591, 1597, 1599, 1626, 1628, 1630, 1705, 1740, 1771, 1772, 1778, 1779, 1780, 1787, 1789, 1797, 1801, 1803, 1845, 1862, 1863, 1892, 1902, 1927, 1939, 1997, 1998, 2015, 2025, 2027, 2049, 2096, 2098, 2100, 2104, 2121, 2132, 2133, 2135, 2136, 2139, 2144, 2159, 2160, 2165, 2166, 2170, 2171, 2172, 2174, 2175, 2177, 2180, 2181, 2183, 2190, 2191, 2192, 2194, 2196, 2203], "static": [2, 4, 9, 15, 29, 30, 35, 36, 38, 40, 50, 55, 56, 59, 70, 75, 76, 79, 680, 806, 813, 829, 831, 842, 850, 858, 886, 888, 918, 919, 920, 921, 934, 935, 1042, 1222, 1223, 1229, 1235, 1254, 1329, 1626, 1772, 1971, 2097, 2098, 2099, 2100, 2133, 2139, 2143, 2149, 2151, 2152, 2161, 2165, 2172, 2188, 2190, 2192, 2203], "Then": [2, 30, 35, 68, 957, 1528, 1634, 1792, 1837, 2037, 2130, 2131, 2133, 2135, 2136, 2137, 2147, 2148, 2155, 2157, 2158, 2165, 2166, 2180, 2185, 2188, 2191, 2192, 2195], "op": [2, 5, 6, 15, 25, 29, 31, 33, 35, 36, 55, 56, 57, 59, 68, 70, 71, 72, 73, 74, 75, 76, 78, 79, 80, 81, 82, 83, 84, 149, 502, 523, 603, 689, 690, 692, 767, 804, 805, 806, 841, 850, 883, 890, 891, 922, 929, 932, 934, 935, 943, 991, 1001, 1003, 1045, 1061, 1064, 1068, 1069, 1086, 1087, 1114, 1118, 1120, 1203, 1266, 1316, 1329, 1331, 1332, 1387, 1393, 1446, 1450, 1461, 1463, 1465, 1599, 1839, 1840, 1841, 1842, 1843, 1844, 1845, 1846, 1847, 1858, 1859, 1860, 1861, 1862, 1863, 1883, 1941, 2023, 2055, 2058, 2060, 2063, 2064, 2087, 2090, 2091, 2100, 2106, 2107, 2110, 2113, 2115, 2119, 2120, 2132, 2133, 2135, 2136, 2146, 2152, 2158, 2159, 2160, 2161, 2162, 2163, 2170, 2172, 2174, 2176, 2181, 2185, 2188, 2189, 2193, 2194, 2195, 2196, 2198, 2201, 2202, 2204], "directli": [2, 4, 8, 10, 15, 16, 24, 25, 29, 31, 33, 35, 36, 37, 38, 39, 40, 49, 55, 56, 57, 59, 63, 68, 70, 83, 556, 742, 757, 837, 919, 929, 932, 934, 1003, 1014, 1128, 1196, 1203, 1210, 1212, 1225, 1259, 1346, 1588, 1616, 1635, 1699, 1746, 1817, 2021, 2097, 2099, 2100, 2104, 2108, 2113, 2121, 2130, 2133, 2135, 2136, 2137, 2139, 2141, 2143, 2144, 2145, 2146, 2149, 2158, 2159, 2160, 2165, 2170, 2172, 2175, 2177, 2184, 2190, 2192, 2193, 2194, 2196, 2197, 2202, 2204], "ctx": [2, 6, 48, 70, 74, 918, 919, 920, 929, 930, 931, 932, 933, 934, 935, 2104, 2129, 2136, 2137, 2155, 2203], "gradcheck": [2, 1970, 2096, 2104, 2136, 2170], "extend": [2, 24, 29, 31, 35, 38, 42, 53, 68, 890, 919, 921, 929, 932, 934, 935, 1584, 1593, 1970, 2037, 2096, 2130, 2143, 2145, 2146, 2152, 2155, 2156, 2158, 2163, 2170, 2190, 2191, 2205], "staticmethod": [2, 70, 919, 921, 929, 930, 931, 932, 933, 934, 935, 2100, 2129, 2136, 2155, 2165], "result": [2, 4, 5, 6, 8, 9, 10, 13, 15, 19, 20, 24, 25, 29, 31, 35, 36, 38, 40, 43, 48, 51, 55, 56, 57, 59, 64, 65, 67, 68, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 152, 220, 221, 253, 254, 314, 334, 397, 495, 497, 498, 555, 582, 583, 584, 586, 587, 617, 696, 697, 698, 699, 700, 703, 705, 706, 707, 709, 770, 805, 809, 894, 906, 908, 927, 928, 937, 938, 939, 940, 941, 942, 948, 949, 957, 969, 972, 983, 987, 992, 998, 1000, 1001, 1003, 1004, 1013, 1014, 1018, 1022, 1024, 1051, 1053, 1055, 1056, 1086, 1087, 1101, 1124, 1125, 1126, 1127, 1128, 1140, 1145, 1163, 1166, 1172, 1181, 1188, 1190, 1193, 1198, 1199, 1202, 1203, 1204, 1207, 1208, 1213, 1214, 1223, 1227, 1238, 1243, 1252, 1255, 1259, 1277, 1278, 1280, 1292, 1306, 1315, 1318, 1319, 1327, 1331, 1332, 1334, 1336, 1337, 1341, 1355, 1357, 1359, 1360, 1368, 1372, 1374, 1376, 1377, 1378, 1385, 1395, 1396, 1403, 1413, 1415, 1416, 1417, 1418, 1420, 1421, 1473, 1476, 1491, 1492, 1493, 1518, 1520, 1521, 1522, 1526, 1528, 1533, 1547, 1552, 1582, 1588, 1598, 1626, 1627, 1628, 1629, 1630, 1634, 1670, 1688, 1699, 1755, 1758, 1759, 1772, 1789, 1792, 1825, 1828, 1829, 1830, 1831, 1837, 1840, 1875, 1880, 1883, 1885, 1892, 1893, 1896, 1902, 1915, 1921, 1936, 1951, 1963, 1974, 1976, 1978, 1982, 1989, 1991, 1992, 1993, 1996, 1997, 1998, 2010, 2014, 2015, 2020, 2021, 2023, 2024, 2026, 2036, 2039, 2043, 2044, 2048, 2069, 2082, 2097, 2098, 2099, 2100, 2104, 2107, 2108, 2110, 2119, 2120, 2121, 2128, 2129, 2130, 2131, 2132, 2133, 2135, 2136, 2137, 2138, 2141, 2142, 2144, 2146, 2147, 2148, 2149, 2151, 2152, 2153, 2155, 2157, 2159, 2160, 2161, 2162, 2165, 2166, 2169, 2170, 2172, 2173, 2175, 2176, 2177, 2181, 2182, 2184, 2189, 2190, 2191, 2193, 2197, 2200, 2201, 2202, 2203, 2204, 2205], "save_for_backward": [2, 919, 929, 931, 933, 934, 935, 2104, 2129, 2130, 2136, 2137, 2155], "grad_output": [2, 70, 918, 929, 930, 934, 935, 943, 949, 953, 954, 1315, 1582, 1880, 2098, 2130, 2133, 2136, 2137, 2144, 2198], "saved_tensor": [2, 929, 931, 932, 933, 934, 935, 2104, 2129, 2130, 2136, 2137], "inspect": [2, 29, 55, 59, 68, 960, 1315, 1327, 2129, 2136, 2143, 2158, 2161, 2173, 2194, 2196, 2197, 2203, 2205], "cost": [2, 4, 5, 9, 10, 25, 31, 33, 35, 36, 59, 485, 991, 1001, 1371, 1501, 1509, 1510, 1511, 1512, 1513, 1514, 1663, 1664, 1665, 1666, 1667, 1668, 1672, 1740, 1841, 1935, 2133, 2139, 2148, 2161, 2166, 2173, 2188, 2199, 2203, 2204], "both": [2, 3, 4, 15, 20, 24, 25, 26, 29, 30, 31, 33, 38, 43, 48, 49, 50, 51, 55, 56, 57, 59, 67, 68, 70, 78, 79, 96, 338, 498, 739, 778, 779, 782, 783, 784, 805, 834, 880, 921, 928, 934, 935, 938, 939, 940, 941, 942, 969, 1026, 1064, 1101, 1140, 1146, 1147, 1148, 1161, 1191, 1192, 1193, 1202, 1257, 1259, 1269, 1276, 1292, 1305, 1306, 1312, 1315, 1329, 1337, 1338, 1386, 1402, 1410, 1416, 1434, 1435, 1491, 1492, 1496, 1497, 1498, 1506, 1509, 1510, 1511, 1512, 1513, 1514, 1528, 1536, 1542, 1544, 1545, 1546, 1553, 1554, 1555, 1556, 1557, 1558, 1559, 1560, 1561, 1562, 1563, 1564, 1565, 1566, 1575, 1576, 1577, 1582, 1588, 1622, 1630, 1634, 1635, 1638, 1655, 1656, 1657, 1663, 1664, 1665, 1666, 1667, 1668, 1713, 1714, 1715, 1740, 1772, 1783, 1785, 1795, 1825, 1841, 1843, 1844, 1862, 1875, 1880, 1900, 1901, 1939, 1944, 1969, 1971, 1973, 1993, 1997, 2020, 2021, 2060, 2097, 2099, 2100, 2104, 2107, 2119, 2120, 2121, 2122, 2126, 2129, 2130, 2131, 2136, 2137, 2139, 2140, 2141, 2144, 2147, 2148, 2149, 2151, 2155, 2157, 2158, 2160, 2163, 2164, 2165, 2167, 2170, 2171, 2172, 2177, 2180, 2181, 2182, 2188, 2190, 2192, 2193, 2194, 2195, 2196, 2200, 2203, 2204], "cpu": [2, 5, 6, 15, 18, 24, 29, 31, 33, 36, 51, 56, 59, 70, 72, 75, 79, 80, 81, 85, 86, 118, 195, 288, 325, 334, 444, 445, 446, 447, 448, 457, 485, 579, 588, 683, 894, 909, 962, 964, 970, 979, 1024, 1027, 1028, 1039, 1051, 1052, 1053, 1055, 1146, 1148, 1158, 1163, 1181, 1193, 1197, 1199, 1200, 1264, 1266, 1267, 1268, 1273, 1274, 1291, 1315, 1323, 1325, 1326, 1335, 1345, 1347, 1351, 1352, 1353, 1354, 1356, 1358, 1361, 1363, 1364, 1370, 1373, 1376, 1379, 1380, 1386, 1387, 1402, 1405, 1416, 1428, 1444, 1518, 1524, 1582, 1755, 1772, 1778, 1779, 1780, 1787, 1816, 1817, 1831, 1834, 1842, 1880, 1895, 1904, 1906, 1908, 1910, 1911, 1935, 1940, 1941, 1942, 1944, 1949, 1950, 1951, 1952, 1953, 1954, 1955, 1956, 1957, 1958, 1959, 1963, 1973, 1979, 1980, 1981, 1982, 1983, 1984, 1997, 2014, 2015, 2016, 2025, 2027, 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2173, 2185, 2189, 2190, 2191, 2192, 2194, 2195, 2203, 2204, 2206], "test": [2, 4, 15, 19, 20, 29, 48, 50, 51, 68, 70, 78, 79, 703, 709, 1001, 1162, 1164, 1165, 1170, 1171, 1176, 1177, 1178, 1180, 1182, 1229, 1237, 1243, 1248, 1249, 1250, 1255, 1301, 1302, 1306, 1307, 1309, 1310, 1837, 1880, 1960, 2096, 2097, 2101, 2113, 2118, 2130, 2139, 2141, 2148, 2157, 2159, 2175, 2181, 2185, 2193, 2203, 2204, 2205], "slow": [2, 40, 948, 1325, 1329, 1345, 1346, 1778, 1779, 1780, 1787, 1823, 2032, 2133, 2141, 2146, 2175, 2190, 2193, 2204], "import": [2, 3, 4, 6, 8, 10, 13, 15, 18, 19, 21, 24, 25, 29, 30, 31, 34, 35, 37, 38, 39, 42, 44, 47, 48, 53, 55, 56, 57, 59, 62, 63, 64, 65, 67, 68, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 680, 744, 751, 752, 753, 782, 783, 784, 804, 889, 890, 891, 944, 951, 953, 980, 987, 1014, 1026, 1056, 1196, 1199, 1202, 1203, 1204, 1206, 1207, 1208, 1209, 1210, 1212, 1312, 1313, 1316, 1318, 1320, 1321, 1322, 1323, 1325, 1326, 1327, 1331, 1332, 1333, 1367, 1368, 1371, 1372, 1385, 1417, 1582, 1642, 1646, 1772, 1793, 1795, 1797, 1798, 1799, 1800, 1801, 1802, 1803, 1804, 1805, 1806, 1808, 1809, 1810, 1818, 1819, 1820, 1821, 1822, 1823, 1830, 1862, 1863, 1889, 2034, 2097, 2099, 2100, 2101, 2104, 2108, 2109, 2113, 2118, 2121, 2128, 2129, 2130, 2132, 2133, 2135, 2136, 2137, 2138, 2140, 2143, 2144, 2146, 2147, 2148, 2149, 2151, 2152, 2153, 2154, 2155, 2160, 2163, 2165, 2166, 2167, 2170, 2173, 2175, 2176, 2177, 2181, 2182, 2184, 2185, 2188, 2189, 2190, 2191, 2192, 2194, 2196, 2201, 2202, 2203, 2204, 2205, 2211], "myfunc": [2, 2137], "inp": [2, 13, 24, 29, 55, 68, 924, 927, 928, 1634, 1772, 2104, 2188, 2198, 2201, 2203, 2205], "clone": [2, 16, 24, 59, 70, 86, 253, 447, 929, 930, 933, 934, 935, 944, 945, 946, 951, 953, 954, 983, 1319, 1524, 1758, 2014, 2023, 2098, 2104, 2118, 2149, 2156, 2157, 2170, 2172, 2173, 2177, 2188, 2198], "runtimeerror": [2, 15, 29, 34, 64, 68, 86, 232, 582, 707, 929, 930, 934, 944, 982, 1056, 1193, 1300, 1315, 1345, 1346, 1347, 1355, 1356, 1357, 1359, 1363, 1369, 1376, 1381, 1382, 1408, 1468, 1582, 1836, 1880, 1933, 1971, 2036, 2097, 2099, 2100, 2119, 2121, 2126, 2129, 2131, 2138, 2148, 2150, 2155, 2160, 2164, 2170, 2173, 2185, 2188], "run_fn": [2, 6, 886, 892], "recent": [2, 8, 9, 54, 67, 944, 1014, 1300, 1971, 2099, 2100, 2108, 2136, 2147, 2148, 2170, 2177, 2200, 2203, 2204], "last": [2, 6, 7, 12, 24, 25, 30, 33, 34, 36, 37, 38, 40, 50, 55, 56, 67, 68, 314, 616, 702, 770, 849, 906, 944, 968, 970, 979, 985, 995, 1014, 1086, 1128, 1133, 1137, 1162, 1164, 1167, 1168, 1170, 1171, 1174, 1175, 1177, 1178, 1180, 1182, 1184, 1229, 1273, 1274, 1278, 1292, 1300, 1312, 1337, 1371, 1374, 1379, 1416, 1421, 1486, 1491, 1492, 1493, 1499, 1517, 1525, 1527, 1533, 1552, 1554, 1569, 1589, 1592, 1596, 1597, 1598, 1611, 1627, 1628, 1634, 1659, 1680, 1689, 1702, 1726, 1727, 1771, 1772, 1774, 1781, 1782, 1829, 1864, 1865, 1866, 1867, 1868, 1869, 1870, 1871, 1872, 1873, 1874, 1875, 1876, 1877, 1878, 1879, 1881, 1922, 1931, 1939, 1968, 1971, 1979, 1980, 1981, 1983, 1984, 1985, 1993, 1997, 2016, 2018, 2021, 2046, 2047, 2099, 2100, 2108, 2113, 2119, 2130, 2133, 2136, 2141, 2148, 2152, 2158, 2159, 2170, 2176, 2177, 2189, 2191, 2197, 2203, 2204], "stdin": [2, 29, 944, 1971, 2108, 2136, 2148, 2170], "instal": [2, 3, 4, 15, 16, 29, 36, 68, 1145, 1229, 2095, 2144, 2145, 2152, 2153, 2154, 2155, 2158, 2165, 2175, 2184, 2189, 2190, 2191, 2192, 2199, 2203], "_tensor": [2, 36, 153], "py": [2, 5, 15, 19, 29, 34, 35, 38, 42, 44, 49, 51, 56, 57, 59, 68, 889, 1056, 1772, 2095, 2097, 2100, 2106, 2121, 2135, 2141, 2143, 2146, 2155, 2158, 2159, 2160, 2166, 2184, 2185, 2187, 2189, 2190, 2191, 2192, 2193, 2194, 2196, 2197, 2200, 2202, 2203, 2204, 2206], "93": [2, 616, 1127], "retain_graph": [2, 149, 922, 943, 953, 954, 1214, 2048, 2098, 2129, 2130, 2136, 2165], "90": [2, 1129, 1879, 1923, 2104, 2115], "allow_unreach": 2, "76": 2, "_forward_cl": 2, "tmp": [2, 4, 15, 29, 48, 50, 2095, 2133, 2159, 2185, 2188, 2197, 2203], "53": [2, 480], "44": [2, 320, 444, 1148, 1493, 1551, 1577, 1823, 2192], "set_detect_anomali": 2, "behaviour": [2, 55, 703, 704, 709, 1688, 1727, 1875, 1945, 2095, 2147, 2172], "interpos": [2, 2136], "grad_fn": [2, 149, 334, 487, 909, 922, 937, 938, 939, 940, 941, 942, 951, 953, 954, 1202, 1790, 1973, 2121, 2130, 2136, 2144, 2149], "node": [2, 29, 33, 34, 40, 43, 44, 49, 50, 55, 59, 68, 70, 80, 81, 680, 812, 833, 836, 838, 839, 842, 845, 850, 866, 929, 964, 1089, 1212, 1227, 1229, 1247, 1252, 1330, 1501, 1518, 1772, 1837, 2110, 2133, 2155, 2158, 2165, 2166, 2167, 2179, 2181, 2183, 2185, 2190, 2191, 2193, 2194, 2200, 2202, 2204], "grad_mod": [2, 944, 945, 946, 2098], "least": [2, 6, 7, 9, 25, 31, 33, 38, 49, 50, 59, 259, 401, 707, 834, 972, 1133, 1135, 1136, 1145, 1186, 1187, 1197, 1228, 1269, 1277, 1278, 1312, 1338, 1361, 1388, 1410, 1472, 1486, 1772, 1817, 2095, 2100, 2120, 2128, 2130, 2131, 2133, 2138, 2139, 2161, 2167, 2170, 2171, 2172, 2177, 2194, 2196, 2203], "intermediari": [2, 15, 38, 929, 932, 934, 2130, 2141, 2191, 2194], "access": [2, 10, 15, 19, 24, 29, 31, 35, 55, 56, 67, 70, 72, 487, 556, 744, 929, 932, 934, 935, 967, 1048, 1056, 1315, 1318, 1331, 1378, 1486, 1582, 1762, 1775, 1776, 1790, 1795, 1817, 1827, 1839, 1840, 1841, 1842, 1843, 1844, 1845, 1846, 1847, 1858, 1859, 1860, 1861, 1862, 1863, 1864, 1865, 1866, 1867, 1868, 1869, 1870, 1871, 1872, 1873, 1874, 1875, 1876, 1877, 1878, 1879, 1880, 1881, 2070, 2097, 2098, 2100, 2101, 2104, 2110, 2112, 2114, 2116, 2118, 2120, 2121, 2130, 2133, 2138, 2141, 2143, 2144, 2149, 2165, 2170, 2172, 2173, 2174, 2176, 2184, 2188, 2190, 2191, 2192, 2193, 2194, 2196, 2199, 2203, 2209], "isinst": [2, 24, 38, 68, 951, 953, 954, 1302, 1313, 2098, 2100, 2130, 2136, 2144, 2157, 2158, 2170, 2194, 2202, 2204], "dir": [2, 1086, 1837, 2095, 2100, 2158], "__call__": [2, 1315, 1582, 1880, 2191], "__class__": [2, 44, 70], "__delattr__": 2, "__dir__": 2, "__doc__": 2, "__eq__": 2, "__format__": [2, 2101], "__ge__": 2, "__getattribute__": 2, "__gt__": 2, "__hash__": [2, 2101], "__init_subclass__": 2, "__le__": 2, "__lt__": [2, 2100], "__ne__": 2, "__new__": [2, 2099, 2101], "__reduce__": [2, 2158], "__reduce_ex__": 2, "__repr__": [2, 4, 2136], "__setattr__": 2, "__sizeof__": 2, "__str__": [2, 68, 2098, 2100], "__subclasshook__": 2, "_raw_saved_result": 2, "_register_hook_dict": 2, "_saved_result": [2, 2130], "metadata": [2, 4, 6, 13, 31, 43, 44, 55, 57, 70, 78, 79, 955, 1018, 1387, 1839, 1840, 1841, 1842, 1843, 1844, 1845, 1846, 1847, 1856, 1859, 1860, 1861, 1862, 1863, 1927, 2104, 2108, 2121, 2130, 2136, 2137, 2149, 2158, 2159, 2165, 2166, 2170, 2172, 2175, 2193, 2202], "next_funct": 2, "register_prehook": [2, 2130], "allclos": [2, 64, 65, 68, 948, 949, 1086, 1167, 1168, 1174, 1175, 1203, 1204, 1206, 1207, 1208, 1209, 1213, 1214, 1363, 1365, 1376, 1378, 1381, 1382, 1407, 1795, 1822, 1895, 2048, 2098, 2104, 2137, 2156, 2170], "pack": [2, 34, 748, 749, 750, 751, 752, 753, 755, 756, 770, 791, 1331, 1332, 1407, 1518, 1533, 1552, 1598, 1816, 1817, 1818, 1819, 2098, 2110, 2121, 2130, 2138, 2150, 2160, 2191], "unpack": [2, 70, 75, 76, 770, 925, 928, 1331, 1363, 1407, 1518, 1819, 1821, 1828, 1838, 2100, 2101, 2130, 2136, 2138], "hook": [2, 29, 30, 33, 34, 36, 59, 486, 487, 758, 767, 929, 932, 934, 953, 954, 957, 1089, 1315, 1518, 1582, 1763, 1764, 1765, 1766, 1767, 1768, 1769, 1770, 1772, 1791, 1797, 1798, 1799, 1800, 1801, 1802, 1803, 1804, 1808, 1813, 1824, 1827, 1839, 1840, 1841, 1842, 1843, 1844, 1845, 1846, 1847, 1850, 1851, 1852, 1853, 1854, 1855, 1859, 1860, 1861, 1862, 1863, 1880, 2096, 2122, 2135, 2136, 2143, 2151, 2152, 2157, 2158, 2160, 2192, 2193, 2194, 2195], "common": [2, 4, 9, 24, 35, 40, 50, 64, 70, 695, 805, 894, 974, 977, 982, 1003, 1021, 1140, 1145, 1190, 1191, 1192, 1193, 1239, 1257, 1338, 1387, 1467, 1496, 1497, 1498, 1501, 1622, 1670, 1763, 1764, 1766, 1767, 1768, 1769, 1770, 1839, 1840, 1841, 1842, 1843, 1844, 1845, 1846, 1847, 1856, 1859, 1860, 1861, 1862, 1863, 1915, 1927, 1994, 2039, 2100, 2105, 2120, 2121, 2130, 2133, 2136, 2137, 2138, 2144, 2146, 2149, 2158, 2169, 2170, 2171, 2172, 2174, 2177, 2188, 2189, 2190, 2194, 2199, 2201, 2202, 2203, 2204, 2206], "trade": [2, 6, 9, 33, 59, 1379, 1495, 1939, 2132, 2138, 2161], "leav": [2, 9, 40, 51, 57, 149, 922, 1222, 1320, 1333, 1793, 1796, 1839, 1840, 1842, 1843, 1844, 1845, 1847, 1859, 1860, 1861, 1862, 1988, 2097, 2099, 2100, 2108, 2130, 2133, 2152, 2158, 2161, 2191, 2203], "especi": [2, 10, 12, 24, 29, 51, 56, 68, 253, 894, 983, 1001, 1012, 1758, 2099, 2108, 2130, 2136, 2137, 2147, 2149, 2160, 2165, 2170, 2188, 2193, 2194, 2196, 2203], "notic": [2, 13, 29, 56, 69, 699, 833, 1165, 1179, 1223, 1410, 1420, 1494, 1635, 1705, 1865, 1866, 1872, 1873, 1879, 2032, 2096, 2097, 2125, 2130, 2170, 2185, 2191, 2194, 2197, 2199, 2201], "fit": [2, 10, 42, 63, 64, 498, 961, 1312, 1846, 1917, 2133, 2152, 2177, 2204], "evalu": [2, 5, 9, 10, 33, 34, 38, 55, 57, 62, 68, 703, 709, 811, 892, 948, 1013, 1101, 1209, 1210, 1219, 1227, 1229, 1254, 1315, 1486, 1490, 1496, 1497, 1498, 1519, 1536, 1544, 1545, 1546, 1554, 1582, 1596, 1601, 1622, 1732, 1740, 1755, 1793, 1846, 1868, 1880, 1902, 2100, 2101, 2136, 2144, 2151, 2152, 2170, 2179, 2182, 2190, 2192, 2193], "saved_tensors_hook": [2, 929, 932, 934, 2130], "pack_hook": [2, 2130], "unpack_hook": [2, 2130], "pair": [2, 29, 31, 37, 38, 50, 53, 55, 616, 745, 894, 968, 989, 1026, 1179, 1225, 1229, 1329, 1379, 1417, 1574, 1583, 1588, 1592, 1729, 1861, 1911, 2099, 2100, 2106, 2119, 2126, 2130, 2133, 2165, 2166, 2167, 2175, 2177, 2181, 2194, 2201, 2203], "retriev": [2, 24, 25, 29, 34, 35, 50, 68, 86, 536, 918, 919, 934, 935, 1056, 1227, 1246, 1524, 1528, 1634, 1679, 1772, 1817, 1837, 1866, 1971, 2121, 2130, 2143, 2155, 2158, 2165, 2166, 2167, 2184, 2192, 2206], "everytim": 2, "store": [2, 4, 6, 15, 19, 25, 31, 35, 40, 44, 51, 55, 56, 57, 59, 68, 325, 331, 397, 696, 840, 845, 919, 934, 981, 1051, 1053, 1055, 1231, 1259, 1315, 1321, 1323, 1326, 1346, 1357, 1358, 1359, 1388, 1393, 1405, 1496, 1497, 1498, 1524, 1582, 1611, 1772, 1789, 1792, 1805, 1806, 1807, 1809, 1810, 1811, 1812, 1816, 1880, 1939, 1976, 2095, 2097, 2104, 2108, 2120, 2121, 2122, 2135, 2136, 2137, 2138, 2143, 2157, 2158, 2160, 2165, 2166, 2167, 2170, 2172, 2175, 2176, 2181, 2184, 2185, 2191, 2192, 2193, 2196, 2208], "content": [2, 4, 8, 20, 31, 36, 43, 55, 56, 68, 929, 932, 934, 953, 1323, 1326, 1346, 1357, 1359, 1364, 1377, 1839, 1840, 1841, 1842, 1843, 1844, 1845, 1846, 1847, 1856, 1859, 1860, 1861, 1862, 1863, 1988, 2020, 2041, 2095, 2100, 2111, 2118, 2130, 2133, 2146, 2157, 2165, 2174, 2175, 2176, 2188, 2199, 2203, 2204], "equal": [2, 13, 20, 25, 29, 38, 50, 51, 53, 55, 68, 259, 498, 544, 616, 680, 704, 705, 706, 749, 750, 751, 752, 753, 770, 778, 779, 838, 839, 840, 845, 890, 905, 970, 977, 979, 990, 991, 1015, 1026, 1053, 1055, 1072, 1115, 1134, 1150, 1189, 1204, 1205, 1222, 1225, 1229, 1239, 1240, 1255, 1258, 1273, 1274, 1276, 1277, 1278, 1300, 1304, 1340, 1361, 1374, 1378, 1381, 1382, 1405, 1406, 1417, 1439, 1479, 1483, 1484, 1485, 1487, 1488, 1489, 1494, 1495, 1501, 1510, 1511, 1513, 1514, 1518, 1525, 1528, 1529, 1530, 1533, 1535, 1552, 1587, 1588, 1596, 1598, 1634, 1643, 1656, 1657, 1661, 1664, 1667, 1679, 1680, 1683, 1684, 1690, 1697, 1772, 1820, 1880, 1883, 1896, 1952, 1953, 1958, 1985, 1993, 2015, 2031, 2037, 2051, 2098, 2101, 2107, 2119, 2120, 2121, 2130, 2131, 2136, 2141, 2147, 2156, 2157, 2171, 2172, 2175, 2177, 2191, 2202], "term": [2, 9, 10, 38, 50, 56, 57, 68, 557, 744, 773, 804, 991, 1013, 1161, 1162, 1163, 1164, 1165, 1166, 1168, 1176, 1177, 1178, 1181, 1193, 1229, 1236, 1347, 1486, 1494, 1535, 1542, 1552, 1553, 1587, 1596, 1614, 1685, 1697, 1732, 1744, 1772, 1789, 1797, 1798, 1799, 1800, 1801, 1802, 1803, 1804, 1839, 1840, 1841, 1842, 1843, 1844, 1845, 1847, 1859, 1860, 1863, 1915, 1951, 1959, 2096, 2100, 2121, 2129, 2130, 2136, 2137, 2138, 2141, 2146, 2155, 2157, 2158, 2160, 2166, 2170, 2182, 2190, 2191, 2194, 2203], "mulbackward0": [2, 938, 941, 942, 2136], "inplac": [2, 31, 55, 62, 64, 68, 70, 78, 79, 765, 768, 786, 788, 790, 803, 810, 828, 833, 865, 867, 868, 886, 887, 892, 929, 930, 934, 955, 1001, 1203, 1315, 1490, 1500, 1519, 1520, 1521, 1522, 1523, 1526, 1538, 1539, 1540, 1568, 1581, 1582, 1601, 1602, 1603, 1610, 1612, 1625, 1654, 1662, 1673, 1674, 1675, 1676, 1677, 1681, 1692, 1693, 1694, 1703, 1719, 1734, 1735, 1738, 1741, 1743, 1753, 1839, 1840, 1841, 1842, 1843, 1844, 1845, 1846, 1847, 1851, 1852, 1859, 1860, 1861, 1862, 1863, 1880, 2036, 2098, 2107, 2130, 2136, 2155, 2181, 2198, 2202], "lead": [2, 6, 8, 23, 24, 29, 39, 56, 59, 65, 68, 894, 929, 930, 934, 937, 939, 943, 949, 1003, 1196, 1214, 1219, 1229, 1346, 1552, 1598, 1614, 1880, 1982, 1988, 2048, 2100, 2104, 2120, 2121, 2130, 2132, 2135, 2136, 2137, 2139, 2146, 2147, 2150, 2157, 2158, 2160, 2165, 2170, 2175, 2176, 2188, 2195, 2203], "undefin": [2, 29, 36, 39, 43, 318, 470, 616, 907, 929, 933, 934, 935, 948, 949, 977, 985, 1003, 1148, 1198, 1199, 1415, 1494, 1836, 1889, 2104, 2126, 2130, 2133, 2136, 2137, 2148, 2165], "recurs": [2, 33, 38, 55, 57, 59, 68, 1003, 1007, 1137, 1227, 1236, 1255, 1315, 1327, 1329, 1582, 1781, 1782, 1866, 1878, 1880, 2099, 2136, 2144, 2158, 2165, 2191, 2192, 2194, 2195, 2203], "inner": [2, 4, 59, 60, 65, 829, 831, 937, 1128, 1204, 1208, 1213, 1226, 1234, 2021, 2098, 2136, 2156, 2165, 2181, 2203], "save_on_cpu": 2, "pin_memori": [2, 24, 33, 70, 72, 75, 79, 80, 81, 444, 445, 446, 447, 448, 1146, 1148, 1197, 1904, 1908, 1910, 1979, 1980, 1981, 1982, 1983, 1984, 2014, 2097, 2098, 2104, 2121, 2133, 2136, 2156, 2172, 2198], "within": [2, 6, 10, 13, 20, 24, 25, 29, 34, 35, 36, 37, 38, 50, 51, 53, 55, 56, 59, 67, 68, 86, 87, 487, 907, 924, 948, 949, 955, 1001, 1018, 1044, 1045, 1128, 1218, 1219, 1227, 1233, 1315, 1319, 1408, 1445, 1446, 1491, 1492, 1493, 1501, 1520, 1521, 1522, 1526, 1528, 1575, 1576, 1577, 1582, 1588, 1622, 1634, 1688, 1698, 1713, 1714, 1715, 1772, 1793, 1797, 1798, 1799, 1800, 1801, 1802, 1803, 1804, 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2108, 2149, 2152, 2157, 2158, 2160, 2165, 2170, 2175, 2181, 2202, 2204], "bla": [3, 20, 2132], "choos": [3, 10, 16, 20, 68, 921, 934, 935, 961, 1013, 1066, 1361, 1374, 1379, 1494, 1998, 2128, 2129, 2132, 2152, 2158, 2161, 2175, 2184], "cubla": [3, 12, 18, 20, 21, 1057, 2036, 2148, 2188], "cublaslt": [3, 18, 21], "ck": 3, "rocm": [3, 18, 696, 699, 969, 981, 1085, 1410, 1420, 1509, 1510, 1511, 1512, 1513, 1514, 1534, 1553, 1569, 2096], "subject": [3, 4, 12, 19, 20, 24, 29, 31, 34, 36, 37, 59, 67, 68, 69, 232, 894, 1641, 1642, 1643, 1646, 1740, 1772, 2100, 2119, 2120, 2125, 2130, 2136, 2149, 2155, 2159, 2160, 2161, 2165, 2170, 2172, 2173, 2179, 2180, 2181, 2184, 2190, 2200], "hipbla": [3, 18, 20], "hipblaslt": [3, 18, 20], "offer": [3, 29, 31, 33, 36, 59, 62, 1588, 1626, 1627, 1628, 1629, 1630, 1772, 2036, 2133, 2136, 2142, 2153, 2158, 2159, 2165, 2170, 2189, 2194, 2198, 2207], "wherev": [3, 10, 2119], "prefer": [3, 6, 10, 24, 29, 31, 40, 51, 59, 891, 907, 939, 1219, 1242, 1313, 1356, 1369, 1373, 1381, 1388, 1589, 1740, 1931, 1993, 2014, 2097, 2104, 2108, 2130, 2133, 2147, 2155, 2157, 2158, 2170, 2194, 2203], "environ": [3, 4, 8, 15, 16, 19, 20, 23, 25, 31, 35, 36, 38, 40, 43, 48, 54, 55, 57, 68, 680, 1056, 1227, 1229, 1252, 1325, 1331, 1552, 1598, 2036, 2095, 2096, 2097, 2106, 2130, 2132, 2133, 2135, 2142, 2147, 2148, 2150, 2165, 2183, 2184, 2185, 2187, 2189, 2194, 2203, 2204], "variabl": [3, 4, 6, 13, 15, 19, 20, 23, 25, 33, 35, 36, 38, 40, 43, 48, 50, 54, 56, 57, 59, 64, 68, 70, 73, 78, 79, 447, 680, 722, 723, 724, 725, 726, 727, 728, 729, 730, 733, 734, 735, 736, 738, 739, 740, 741, 742, 744, 748, 749, 750, 751, 752, 753, 755, 756, 766, 770, 774, 813, 843, 844, 846, 929, 930, 934, 1003, 1018, 1022, 1026, 1114, 1146, 1214, 1229, 1329, 1331, 1388, 1499, 1509, 1510, 1511, 1512, 1513, 1514, 1518, 1524, 1525, 1533, 1534, 1552, 1553, 1554, 1567, 1569, 1582, 1585, 1590, 1598, 1600, 1772, 1816, 1817, 1818, 1819, 1820, 1821, 1822, 1834, 1838, 1840, 1864, 1865, 1866, 1867, 1869, 1870, 1871, 1872, 1873, 1874, 1875, 1876, 1878, 1879, 1881, 1904, 1906, 1908, 2036, 2048, 2093, 2095, 2096, 2106, 2107, 2120, 2121, 2130, 2132, 2133, 2135, 2138, 2141, 2142, 2147, 2148, 2150, 2152, 2155, 2157, 2160, 2165, 2172, 2175, 2183, 2184, 2185, 2190, 2191, 2192, 2194, 2196, 2203, 2204], "torch_blas_prefer_cublaslt": 3, "global": [3, 4, 6, 9, 12, 13, 21, 24, 29, 31, 34, 36, 37, 38, 40, 51, 59, 64, 67, 68, 70, 73, 78, 79, 883, 890, 894, 902, 957, 970, 979, 1018, 1037, 1063, 1077, 1095, 1103, 1146, 1148, 1158, 1163, 1181, 1197, 1200, 1203, 1273, 1274, 1296, 1303, 1315, 1318, 1331, 1335, 1386, 1402, 1582, 1740, 1764, 1765, 1766, 1767, 1768, 1769, 1770, 1772, 1802, 1806, 1834, 1880, 1904, 1906, 1908, 1911, 1935, 1949, 1950, 1951, 1952, 1953, 1954, 1955, 1956, 1957, 1958, 1959, 2078, 2093, 2097, 2100, 2101, 2104, 2112, 2122, 2129, 2136, 2143, 2144, 2146, 2148, 2160, 2165, 2166, 2167, 2170, 2173, 2175, 2191, 2192, 2193, 2194, 2195, 2202, 2204], "overridden": [3, 6, 15, 68, 918, 919, 920, 934, 935, 1086, 1582, 1971, 2100, 2130, 2136, 2147, 2204, 2205], "achiev": [3, 20, 24, 25, 29, 31, 37, 38, 51, 59, 1145, 1210, 1315, 1495, 1501, 1582, 1588, 1589, 1661, 1690, 1772, 1880, 2114, 2133, 2143, 2146, 2158, 2165, 2167, 2172, 2204], "better": [3, 4, 8, 9, 10, 15, 24, 28, 29, 40, 55, 63, 944, 1001, 1114, 1202, 1206, 1207, 1223, 1302, 1331, 1517, 1616, 1740, 1746, 1772, 1781, 1782, 1875, 1880, 1902, 2100, 2121, 2129, 2130, 2132, 2133, 2139, 2141, 2148, 2150, 2155, 2157, 2159, 2160, 2161, 2170, 2175, 2181, 2184, 2189, 2191, 2192, 2200, 2203], "select": [3, 6, 16, 18, 20, 22, 24, 29, 31, 38, 40, 48, 56, 312, 314, 316, 320, 684, 685, 686, 770, 991, 1028, 1030, 1034, 1045, 1046, 1050, 1058, 1059, 1060, 1061, 1063, 1064, 1072, 1088, 1092, 1094, 1095, 1097, 1099, 1101, 1102, 1103, 1108, 1109, 1110, 1111, 1114, 1115, 1118, 1120, 1122, 1123, 1165, 1388, 1417, 1446, 1447, 1448, 1449, 1450, 1461, 1463, 1465, 1501, 1509, 1510, 1511, 1512, 1513, 1514, 1533, 1552, 1598, 1646, 1663, 1664, 1665, 1666, 1667, 1668, 1672, 1740, 1811, 1812, 1934, 2011, 2039, 2051, 2055, 2056, 2057, 2058, 2060, 2076, 2077, 2078, 2079, 2080, 2081, 2083, 2084, 2087, 2090, 2091, 2097, 2098, 2100, 2118, 2119, 2120, 2121, 2130, 2132, 2133, 2141, 2148, 2151, 2156, 2160, 2170, 2174, 2177, 2192, 2198, 2200, 2203, 2206], "incorrect": [3, 5, 6, 59, 68, 253, 512, 555, 929, 932, 934, 948, 957, 983, 1003, 1013, 1331, 1374, 1387, 1588, 1626, 1627, 1628, 1629, 1630, 1758, 1830, 1982, 1993, 2097, 2100, 2104, 2133, 2147, 2155, 2191, 2207], "_blasbackend": 3, "preferred_rocm_fa_librari": 3, "flash": [3, 1641, 1646], "attent": [3, 8, 36, 37, 745, 1495, 1588, 1626, 1628, 1630, 1641, 1643, 1661, 1740, 2096, 2150, 2174, 2191], "aotriton": 3, "experimeent": 3, "composable_kernel": 3, "torch_rocm_fa_prefer_ck": 3, "_rocmfabackend": 3, "preferred_linalg_librari": [3, 1352], "heurist": [3, 15, 24, 33, 50, 51, 68, 1006, 1145, 2139, 2188, 2197], "cusolv": [3, 1379, 1380, 1997], "magma": [3, 1361, 1405, 1895, 1997, 2150, 2170], "algebra": [3, 10, 1145, 1352, 1373, 2105], "decid": [3, 5, 8, 29, 40, 50, 57, 63, 485, 1225, 1806, 2107, 2155, 2170, 2193, 2194, 2197], "pick": [3, 29, 49, 51, 55, 512, 1001, 2130, 2165, 2190, 2192, 2197], "torch_linalg_prefer_cusolv": 3, "linalg": [3, 12, 405, 991, 992, 993, 994, 1036, 1131, 1259, 1293, 1396, 1405, 1406, 1407, 1411, 1412, 1789, 1790, 1795, 1830, 1882, 1887, 1889, 1895, 1965, 1977, 1997, 1998, 2023, 2045, 2096], "inv": [3, 38, 1293, 1347, 1351, 1357, 1373, 1377, 1381], "inv_ex": [3, 1356], "cholesky_ex": [3, 1345], "lu_factor": [3, 1364, 1365, 1405, 1406, 1407], "lu": [3, 11, 1357, 1363, 1364, 1365, 1406, 1407, 2098], "eigh": [3, 1345, 1351, 1354, 1373, 1379, 2147], "eighval": 3, "svdval": [3, 1347, 1361, 1370, 1379, 1997, 2147], "_linalgbackend": 3, "sdpaparam": 3, "flash_sdp_en": 3, "scale": [3, 8, 24, 35, 38, 40, 49, 51, 96, 172, 472, 474, 616, 695, 699, 700, 701, 748, 749, 750, 751, 752, 753, 754, 759, 760, 761, 762, 763, 764, 765, 766, 769, 780, 782, 783, 784, 785, 787, 790, 791, 807, 813, 816, 834, 836, 837, 838, 839, 840, 845, 853, 969, 1159, 1160, 1163, 1181, 1213, 1214, 1402, 1490, 1519, 1524, 1525, 1526, 1542, 1554, 1610, 1635, 1636, 1637, 1641, 1646, 1679, 1680, 1681, 1697, 1699, 1740, 1741, 1746, 1780, 1840, 1841, 1868, 1897, 1898, 1899, 1900, 1901, 1974, 1975, 1994, 2048, 2096, 2098, 2120, 2121, 2126, 2128, 2133, 2138, 2140, 2144, 2160, 2161, 2163, 2171, 2194], "product": [3, 16, 29, 31, 36, 38, 50, 149, 696, 699, 700, 701, 770, 922, 927, 938, 940, 941, 942, 943, 969, 981, 987, 991, 1022, 1036, 1050, 1103, 1108, 1122, 1123, 1126, 1142, 1145, 1209, 1213, 1214, 1292, 1336, 1348, 1355, 1381, 1382, 1384, 1410, 1417, 1420, 1470, 1533, 1534, 1552, 1553, 1641, 1646, 1687, 1740, 1789, 1883, 1884, 1893, 1939, 2016, 2031, 2045, 2048, 2097, 2119, 2121, 2126, 2130, 2141, 2143, 2158, 2160, 2170, 2191, 2203, 2206], "enable_mem_efficient_sdp": [3, 1740], "mem_efficient_sdp_en": 3, "enable_flash_sdp": [3, 1740], "math_sdp_en": 3, "math": [3, 24, 68, 1155, 1367, 1641, 1646, 1700, 1740, 1885, 1902, 1998, 2097, 2099, 2100, 2115, 2139, 2147, 2170, 2171, 2177, 2185, 2204], "enable_math_sdp": [3, 1740], "fp16_bf16_reduction_math_sdp_allow": 3, "allow_fp16_bf16_reduction_math_sdp": [3, 2147], "cudnn_sdp_en": 3, "enable_cudnn_sdp": 3, "is_flash_attention_avail": 3, "check": [3, 4, 5, 6, 12, 15, 20, 21, 24, 29, 30, 31, 36, 38, 50, 59, 70, 71, 76, 79, 85, 87, 220, 232, 339, 498, 683, 688, 704, 890, 929, 930, 932, 933, 934, 935, 948, 949, 955, 1003, 1014, 1039, 1040, 1044, 1048, 1081, 1101, 1225, 1229, 1237, 1238, 1248, 1249, 1250, 1302, 1312, 1315, 1331, 1332, 1345, 1346, 1351, 1352, 1354, 1357, 1359, 1364, 1370, 1373, 1377, 1379, 1380, 1405, 1432, 1444, 1445, 1555, 1556, 1557, 1558, 1559, 1560, 1561, 1562, 1563, 1564, 1565, 1566, 1567, 1582, 1762, 1772, 1792, 1795, 1808, 1818, 1837, 1838, 1880, 1971, 1979, 1980, 1981, 1982, 1983, 1984, 2037, 2053, 2054, 2081, 2095, 2099, 2100, 2101, 2104, 2110, 2119, 2120, 2129, 2133, 2136, 2137, 2138, 2139, 2143, 2144, 2145, 2148, 2149, 2152, 2155, 2157, 2158, 2159, 2160, 2165, 2170, 2172, 2175, 2177, 2187, 2188, 2190, 2191, 2192, 2193, 2194, 2195, 2196, 2197, 2202, 2203, 2204, 2205, 2208], "flashattent": [3, 1630, 1740], "depend": [3, 5, 6, 13, 15, 24, 29, 31, 33, 34, 35, 36, 38, 40, 50, 51, 53, 56, 59, 68, 85, 314, 795, 907, 953, 1050, 1103, 1108, 1122, 1123, 1166, 1168, 1176, 1177, 1178, 1204, 1208, 1213, 1229, 1239, 1241, 1242, 1243, 1246, 1327, 1331, 1351, 1352, 1379, 1405, 1410, 1444, 1494, 1495, 1515, 1517, 1525, 1528, 1541, 1547, 1548, 1573, 1574, 1585, 1586, 1587, 1589, 1596, 1614, 1615, 1631, 1634, 1635, 1653, 1660, 1661, 1671, 1680, 1688, 1700, 1724, 1732, 1740, 1759, 1762, 1772, 1795, 1796, 1802, 1868, 1883, 1885, 1902, 1918, 1979, 1980, 1981, 1983, 1984, 1997, 2023, 2051, 2095, 2097, 2099, 2100, 2104, 2107, 2108, 2118, 2126, 2130, 2132, 2133, 2136, 2137, 2139, 2141, 2144, 2146, 2151, 2154, 2155, 2159, 2160, 2165, 2170, 2176, 2177, 2182, 2184, 2188, 2189, 2190, 2191, 2193, 2194, 2196, 2199, 2201, 2202, 2206], "can_use_flash_attent": 3, "debug": [3, 5, 6, 9, 20, 21, 24, 35, 41, 47, 55, 680, 812, 833, 851, 854, 858, 872, 948, 949, 1001, 1003, 1011, 1037, 1076, 1119, 1263, 1345, 1765, 1766, 1767, 1768, 1938, 1945, 2099, 2106, 2130, 2132, 2133, 2135, 2142, 2143, 2144, 2148, 2150, 2155, 2158, 2180, 2190, 2191, 2192, 2195, 2196, 2199, 2206, 2207, 2208], "util": [3, 8, 18, 31, 51, 55, 56, 57, 59, 60, 67, 68, 70, 498, 742, 745, 748, 749, 750, 755, 756, 766, 770, 774, 802, 811, 935, 1055, 1146, 1147, 1148, 1196, 1238, 1248, 1249, 1250, 1253, 1315, 1533, 1552, 1582, 1598, 1599, 1763, 1764, 1765, 1766, 1767, 1768, 1769, 1770, 1772, 1792, 1797, 1798, 1799, 1800, 1801, 1802, 1803, 1804, 1816, 1839, 1840, 1841, 1842, 1843, 1844, 1845, 1846, 1847, 1850, 1851, 1852, 1853, 1854, 1855, 1859, 1860, 1861, 1862, 1863, 1868, 1875, 1880, 2001, 2003, 2005, 2006, 2007, 2036, 2096, 2100, 2104, 2108, 2113, 2121, 2129, 2131, 2132, 2133, 2136, 2138, 2140, 2141, 2143, 2144, 2146, 2148, 2153, 2155, 2158, 2184, 2190, 2191, 2194, 2201, 2204, 2207], "_sdpaparam": 3, "kei": [3, 4, 24, 31, 35, 40, 48, 50, 51, 55, 56, 57, 59, 66, 68, 414, 602, 680, 745, 812, 883, 888, 956, 961, 968, 1111, 1202, 1212, 1230, 1315, 1331, 1332, 1387, 1582, 1583, 1588, 1592, 1626, 1627, 1628, 1629, 1630, 1643, 1740, 1809, 1825, 1839, 1840, 1841, 1842, 1843, 1844, 1845, 1846, 1847, 1849, 1859, 1860, 1861, 1862, 1863, 1880, 2037, 2039, 2083, 2084, 2098, 2099, 2100, 2104, 2112, 2126, 2133, 2136, 2143, 2144, 2149, 2155, 2156, 2157, 2159, 2160, 2163, 2165, 2166, 2172, 2175, 2177, 2180, 2182, 2195, 2200, 2201, 2202, 2207], "mask": [3, 48, 398, 399, 400, 401, 402, 543, 745, 948, 949, 1409, 1490, 1501, 1526, 1588, 1626, 1627, 1628, 1629, 1630, 1681, 1740, 1797, 1798, 1799, 1800, 1801, 1802, 1803, 1804, 1805, 1806, 1807, 1809, 1810, 1811, 1812, 1863, 2096, 2098, 2104, 2119, 2120, 2121, 2126, 2170, 2194, 2198], "dropout": [3, 6, 37, 68, 744, 745, 770, 1315, 1490, 1520, 1521, 1522, 1526, 1533, 1552, 1582, 1588, 1598, 1599, 1626, 1628, 1630, 1654, 1674, 1675, 1676, 1681, 1740, 1880, 2098, 2110, 2120, 2130, 2133, 2156, 2162], "causal": [3, 745, 1588, 1626, 1627, 1628, 1629, 1630, 1642, 1643, 1644, 1645, 1740, 2126], "warn": [3, 4, 15, 19, 20, 25, 26, 29, 34, 35, 36, 55, 680, 937, 943, 991, 1050, 1103, 1108, 1119, 1122, 1123, 1214, 1229, 1296, 1312, 1331, 1373, 1387, 1417, 1588, 1626, 1627, 1628, 1629, 1630, 1740, 1772, 1792, 1795, 1938, 1945, 1993, 2036, 2048, 2095, 2106, 2113, 2126, 2131, 2136, 2149, 2152, 2156, 2158, 2164, 2191, 2203, 2204], "why": [3, 4, 8, 24, 55, 64, 68, 1145, 1319, 1740, 1843, 1844, 1862, 2104, 2107, 2137, 2139, 2190, 2191, 2193, 2201, 2206], "could": [3, 5, 6, 8, 9, 18, 20, 24, 29, 35, 36, 37, 38, 40, 50, 57, 64, 67, 68, 582, 583, 584, 586, 587, 812, 957, 1081, 1166, 1168, 1176, 1177, 1178, 1230, 1312, 1315, 1321, 1323, 1346, 1387, 1405, 1434, 1435, 1582, 1781, 1782, 1867, 1880, 1973, 1998, 2032, 2099, 2100, 2107, 2118, 2130, 2133, 2135, 2149, 2150, 2153, 2155, 2158, 2160, 2161, 2165, 2166, 2167, 2170, 2174, 2175, 2189, 2191, 2194, 2195, 2199, 2203, 2204, 2207, 2208], "can_use_efficient_attent": 3, "efficient_attent": [3, 1641, 1646], "can_use_cudnn_attent": 3, "cudnn_attent": [3, 1641], "sdp_kernel": 3, "enable_flash": 3, "enable_math": 3, "enable_mem_effici": 3, "enable_cudnn": 3, "temporarili": [3, 40, 1935, 2104, 2130, 2155, 2159, 2197], "previou": [3, 19, 29, 31, 33, 50, 55, 57, 59, 68, 512, 555, 770, 1001, 1003, 1042, 1077, 1190, 1269, 1326, 1386, 1402, 1533, 1552, 1598, 1646, 1772, 1797, 1798, 1799, 1800, 1801, 1802, 1803, 1804, 1837, 1877, 1902, 1993, 2110, 2130, 2133, 2136, 2144, 2150, 2152, 2153, 2157, 2160, 2167, 2194, 2203], "restor": [3, 6, 31, 34, 36, 68, 86, 1012, 1190, 1386, 1402, 1646, 1827, 1922, 1943, 2144, 2149, 2192], "is_avail": [3, 18, 29, 683, 692, 2039, 2133, 2140, 2142, 2145, 2149, 2159, 2184, 2211], "determinist": [3, 4, 6, 23, 29, 33, 38, 68, 86, 485, 498, 512, 902, 1146, 1147, 1148, 1263, 1296, 1318, 1331, 1332, 1413, 1416, 1418, 1501, 1509, 1510, 1511, 1512, 1513, 1514, 1552, 1598, 1663, 1664, 1665, 1666, 1667, 1668, 1672, 1740, 1932, 1938, 2036, 2096, 2098, 2130, 2139, 2148, 2157, 2164], "algorithm": [3, 4, 8, 12, 20, 22, 25, 30, 34, 38, 40, 59, 86, 680, 770, 789, 795, 991, 1312, 1356, 1363, 1369, 1373, 1379, 1388, 1405, 1501, 1509, 1510, 1511, 1512, 1513, 1514, 1528, 1531, 1533, 1552, 1598, 1634, 1635, 1663, 1664, 1665, 1666, 1667, 1668, 1672, 1688, 1699, 1740, 1759, 1772, 1840, 1841, 1842, 1843, 1844, 1845, 1846, 1847, 1859, 1860, 1861, 1863, 1885, 1924, 1939, 1997, 1998, 2036, 2096, 2130, 2133, 2147, 2165, 2170], "are_deterministic_algorithms_en": [3, 2188], "use_deterministic_algorithm": [3, 28, 498, 902, 1146, 1147, 1148, 1296, 1938, 2148], "benchmark": [3, 20, 680, 2096, 2098, 2133, 2142, 2159, 2188, 2194, 2200, 2203], "fastest": [3, 18, 20, 906, 1772, 1829, 1843, 1844, 1862, 2141, 2148, 2157], "benchmark_limit": 3, "maximum": [3, 28, 38, 50, 51, 55, 498, 705, 707, 781, 807, 838, 839, 840, 845, 850, 903, 1052, 1092, 1094, 1101, 1109, 1110, 1124, 1125, 1146, 1147, 1148, 1191, 1208, 1276, 1277, 1278, 1388, 1413, 1439, 1540, 1679, 1680, 1779, 1841, 1866, 1875, 1902, 1916, 1949, 1950, 1951, 1952, 1953, 1954, 1955, 1956, 1957, 1958, 1959, 2038, 2076, 2077, 2081, 2098, 2106, 2115, 2121, 2128, 2129, 2133, 2146, 2156, 2163, 2177, 2178, 2190, 2198, 2206, 2208], "try": [3, 4, 5, 8, 9, 29, 31, 36, 37, 42, 43, 47, 50, 55, 63, 64, 232, 1001, 1003, 1115, 1199, 1208, 1214, 1229, 1254, 1320, 1322, 1331, 1352, 1439, 1501, 1509, 1510, 1511, 1512, 1513, 1514, 1626, 1627, 1629, 1635, 1641, 1663, 1664, 1665, 1666, 1667, 1668, 1672, 1740, 1772, 1839, 1840, 1842, 1843, 1844, 1845, 1846, 1847, 1859, 1860, 1861, 1862, 2048, 2095, 2097, 2100, 2101, 2104, 2130, 2133, 2136, 2138, 2141, 2146, 2155, 2157, 2158, 2160, 2161, 2165, 2170, 2189, 2190, 2193, 2194, 2196, 2203, 2204, 2206], "dispatch": [3, 15, 29, 55, 59, 68, 1215, 1216, 1230, 1320, 1434, 1435, 2037, 2039, 2100, 2104, 2133, 2136, 2152, 2155, 2194, 2195, 2205], "v8": [3, 21], "api": [3, 4, 6, 9, 10, 15, 21, 25, 29, 30, 31, 33, 37, 40, 42, 43, 47, 48, 49, 50, 51, 53, 59, 60, 63, 67, 69, 70, 75, 79, 80, 81, 232, 814, 842, 890, 924, 925, 926, 927, 928, 940, 945, 946, 990, 1003, 1006, 1013, 1037, 1056, 1077, 1078, 1079, 1086, 1087, 1089, 1118, 1149, 1189, 1203, 1206, 1207, 1209, 1214, 1227, 1239, 1326, 1327, 1329, 1439, 1463, 1772, 1825, 1827, 1828, 1836, 1924, 1935, 2029, 2039, 2048, 2070, 2090, 2095, 2106, 2107, 2108, 2114, 2115, 2118, 2119, 2121, 2125, 2130, 2135, 2140, 2149, 2151, 2161, 2165, 2166, 2170, 2173, 2175, 2179, 2182, 2184, 2185, 2192, 2196, 2203, 2205, 2208], "get_fastpath_en": 3, "fast": [3, 8, 9, 24, 29, 40, 68, 948, 1523, 1544, 1545, 1546, 1614, 1630, 1781, 1782, 1875, 1883, 1902, 1924, 1939, 2115, 2132, 2133, 2136, 2142, 2157, 2165, 2170, 2173, 2174, 2182, 2191, 2193, 2196, 2197, 2206], "path": [3, 4, 5, 9, 15, 21, 29, 31, 40, 48, 50, 53, 54, 55, 57, 59, 68, 744, 958, 959, 960, 1037, 1145, 1238, 1315, 1582, 1599, 1630, 1837, 1880, 2095, 2098, 2099, 2130, 2133, 2136, 2139, 2142, 2149, 2152, 2155, 2157, 2158, 2159, 2170, 2180, 2184, 2188, 2196, 2197], "transformerencod": 3, "multiheadattent": [3, 1626, 1628, 1630, 2160], "fastpath": [3, 1588, 2193], "condit": [3, 6, 13, 22, 24, 55, 56, 57, 64, 68, 70, 78, 79, 616, 619, 624, 704, 770, 938, 948, 949, 1013, 1018, 1312, 1331, 1345, 1347, 1352, 1355, 1361, 1362, 1363, 1374, 1379, 1533, 1552, 1598, 1630, 1818, 1823, 2023, 2051, 2097, 2098, 2099, 2101, 2109, 2130, 2136, 2147, 2175, 2190, 2191, 2192, 2193, 2198, 2203], "met": [3, 13, 616, 1018, 1355, 1362, 1363, 1374, 1388, 1630, 1982], "set_fastpath_en": 3, "verbos": [3, 15, 20, 29, 68, 680, 1229, 1865, 1866, 1867, 1868, 1869, 1870, 1871, 1872, 1873, 1874, 1875, 1876, 1877, 1878, 1879, 2095, 2106, 2115, 2120, 2155, 2175, 2204], "On": [3, 15, 23, 24, 29, 30, 33, 38, 51, 57, 59, 64, 68, 696, 699, 969, 981, 1042, 1096, 1363, 1410, 1420, 1509, 1510, 1511, 1512, 1513, 1514, 1534, 1549, 1550, 1551, 1552, 1553, 1569, 1592, 1598, 1611, 1772, 1843, 1844, 1859, 1862, 2097, 2107, 2130, 2132, 2133, 2136, 2147, 2158, 2165, 2166, 2167, 2170, 2188, 2190, 2191, 2193, 2194, 2203], "demand": [3, 24, 1079, 2099, 2121, 2143, 2146, 2165], "onemkl": 3, "easier": [3, 8, 24, 36, 55, 68, 2097, 2099, 2126, 2130, 2131, 2136, 2137, 2139, 2170, 2194, 2203, 2204], "dump": [3, 68, 1037, 2135, 2150, 2155, 2194, 2204, 2208], "durat": [3, 20, 29, 47, 50, 966, 1434, 1435, 2113, 2152, 2159, 2194], "kernel": [3, 4, 5, 12, 15, 19, 21, 29, 59, 67, 87, 485, 680, 692, 749, 750, 751, 752, 753, 782, 783, 784, 955, 1001, 1035, 1040, 1044, 1045, 1086, 1087, 1121, 1123, 1441, 1445, 1446, 1491, 1492, 1493, 1509, 1510, 1511, 1512, 1513, 1514, 1528, 1529, 1530, 1549, 1550, 1551, 1558, 1559, 1560, 1561, 1562, 1563, 1575, 1576, 1577, 1578, 1579, 1580, 1634, 1663, 1664, 1665, 1666, 1667, 1668, 1683, 1684, 1740, 1781, 1782, 1793, 2037, 2039, 2054, 2055, 2092, 2097, 2104, 2110, 2115, 2121, 2126, 2133, 2136, 2137, 2139, 2145, 2147, 2155, 2157, 2159, 2161, 2170, 2185, 2188, 2191, 2192, 2193, 2194, 2195, 2196, 2203, 2204, 2207], "mkl_verbos": 3, "methodologi": 3, "larg": [3, 4, 8, 9, 24, 29, 35, 37, 68, 680, 887, 1024, 1026, 1044, 1101, 1352, 1361, 1379, 1486, 1528, 1634, 1682, 1772, 1875, 1963, 1976, 1998, 2054, 2081, 2096, 2118, 2120, 2121, 2132, 2133, 2136, 2138, 2144, 2147, 2149, 2152, 2155, 2158, 2161, 2165, 2170, 2173, 2176, 2188, 2194, 2201, 2203, 2204, 2206], "moreov": [3, 31, 57, 512, 1772, 1862, 2207], "investig": [3, 8, 29, 64, 2201, 2203], "singl": [3, 4, 6, 15, 20, 25, 29, 31, 34, 35, 36, 37, 38, 40, 43, 48, 49, 50, 57, 59, 60, 62, 63, 64, 65, 67, 68, 70, 78, 79, 253, 680, 705, 706, 776, 777, 778, 779, 782, 783, 784, 828, 924, 937, 938, 939, 940, 941, 942, 943, 955, 970, 979, 983, 1001, 1003, 1022, 1026, 1086, 1089, 1202, 1204, 1205, 1208, 1210, 1214, 1273, 1274, 1278, 1300, 1314, 1315, 1331, 1332, 1335, 1393, 1417, 1434, 1435, 1484, 1485, 1488, 1489, 1492, 1493, 1495, 1509, 1510, 1511, 1513, 1514, 1517, 1518, 1529, 1530, 1536, 1549, 1550, 1551, 1554, 1576, 1577, 1582, 1590, 1594, 1595, 1597, 1611, 1622, 1647, 1648, 1649, 1650, 1651, 1652, 1655, 1656, 1657, 1663, 1664, 1665, 1666, 1667, 1668, 1683, 1684, 1685, 1713, 1714, 1715, 1758, 1764, 1766, 1769, 1770, 1771, 1772, 1778, 1779, 1780, 1783, 1785, 1787, 1788, 1825, 1826, 1839, 1840, 1841, 1842, 1843, 1844, 1845, 1846, 1847, 1857, 1859, 1860, 1861, 1862, 1863, 1868, 1880, 1892, 1918, 1939, 1985, 1991, 1992, 2032, 2033, 2036, 2043, 2044, 2048, 2097, 2099, 2100, 2107, 2110, 2118, 2120, 2121, 2122, 2126, 2130, 2132, 2133, 2136, 2137, 2139, 2141, 2144, 2146, 2147, 2148, 2149, 2155, 2157, 2158, 2159, 2160, 2166, 2167, 2170, 2172, 2173, 2176, 2177, 2181, 2187, 2188, 2190, 2192, 2193, 2194, 2196, 2202, 2203, 2204], "enough": [3, 9, 25, 36, 57, 68, 1229, 1236, 1327, 1632, 1979, 1980, 1981, 1982, 1983, 1984, 2036, 2099, 2118, 2121, 2133, 2136, 2141, 2157, 2158, 2173, 2191, 2194, 2203, 2205], "scope": 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[4, 24, 29, 31, 35, 50, 55, 1225, 2097, 2130, 2138, 2143, 2144, 2146, 2158, 2160, 2165, 2166, 2170, 2189, 2192, 2194, 2196, 2203], "differenti": [4, 36, 38, 60, 65, 149, 351, 588, 816, 918, 920, 922, 927, 928, 929, 931, 934, 935, 937, 938, 939, 940, 941, 942, 943, 948, 949, 998, 1013, 1089, 1207, 1208, 1209, 1213, 1330, 1362, 1363, 1374, 1405, 1500, 1501, 1524, 1690, 1725, 1772, 1795, 1839, 1840, 1842, 1843, 1844, 1845, 1847, 1859, 1860, 1861, 1862, 2036, 2107, 2136, 2138, 2141, 2155, 2157, 2165, 2176, 2194, 2203], "distinguish": [4, 1228, 2121, 2160, 2170], "princip": [4, 1362, 1885], "signal": [4, 12, 31, 40, 50, 53, 748, 749, 750, 776, 777, 792, 793, 1161, 1162, 1163, 1164, 1166, 1167, 1168, 1169, 1170, 1171, 1173, 1174, 1175, 1176, 1177, 1178, 1179, 1180, 1181, 1182, 1312, 1378, 1483, 1484, 1485, 1487, 1488, 1489, 1491, 1492, 1493, 1509, 1510, 1511, 1529, 1530, 1549, 1550, 1551, 1570, 1575, 1576, 1577, 1636, 1637, 1647, 1648, 1649, 1650, 1651, 1652, 1655, 1663, 1666, 1683, 1684, 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1396, 1495, 2069, 2100, 2133, 2136, 2149, 2157, 2165, 2166, 2190, 2203], "workload": [4, 9, 20, 24, 29, 59, 1089, 2133, 2139, 2143, 2147, 2159, 2165, 2183, 2187, 2201], "good": [4, 8, 9, 15, 68, 1001, 1206, 1236, 1590, 1877, 1959, 2095, 2104, 2118, 2133, 2136, 2139, 2143, 2144, 2146, 2155, 2158, 2160, 2190, 2191, 2192, 2193, 2200, 2203], "intrins": [4, 710, 711, 712, 713, 714, 715, 716, 717, 718, 719, 720, 721, 722, 723, 724, 725, 726, 727, 728, 729, 730, 731, 732, 733, 734, 735, 736, 737, 738, 739, 804, 805, 2161, 2162], "contrast": [4, 38, 40, 55, 57, 770, 1225, 1533, 1841, 1862, 2128, 2130, 2137, 2139, 2170, 2198], "adaptive_autorang": 4, "threshold": [4, 25, 68, 1370, 1373, 1542, 1614, 1619, 1697, 1748, 1754, 1841, 1877, 1943, 2098, 2129, 2133, 2156, 2175], "min_run_tim": 4, "01": [4, 30, 34, 53, 487, 765, 790, 839, 840, 1146, 1188, 1568, 1703, 1704, 1762, 1837, 1839, 1840, 1841, 1842, 1843, 1844, 1845, 1846, 1847, 1856, 1859, 1860, 1861, 1862, 1863, 1868, 1875, 1897, 1950, 1952, 1953, 1958, 1959, 2098, 2128, 2143, 2155, 2157, 2198, 2203], "max_run_tim": 4, "callback": [4, 25, 29, 40, 53, 67, 68, 1772, 2113, 2136, 2143, 2159, 2165], "similar": [4, 8, 10, 12, 21, 24, 29, 33, 35, 36, 38, 47, 56, 59, 64, 67, 68, 492, 514, 722, 723, 724, 725, 726, 727, 730, 740, 741, 742, 743, 755, 756, 766, 774, 816, 906, 921, 934, 935, 982, 987, 1000, 1001, 1191, 1192, 1315, 1515, 1516, 1528, 1541, 1544, 1545, 1546, 1582, 1631, 1670, 1816, 1830, 1831, 1841, 1863, 1880, 1889, 1917, 1924, 1973, 1977, 2011, 2017, 2033, 2099, 2100, 2104, 2118, 2119, 2121, 2130, 2132, 2133, 2136, 2137, 2141, 2147, 2158, 2160, 2165, 2166, 2170, 2171, 2172, 2176, 2190, 2193, 2194, 2196, 2199, 2203, 2204, 2209], "blocked_autorang": 4, "variablil": 4, "until": [4, 6, 8, 24, 25, 29, 31, 34, 40, 50, 55, 59, 67, 68, 85, 87, 485, 1039, 1040, 1044, 1079, 1199, 1336, 1388, 1428, 1434, 1435, 1444, 1445, 1688, 1762, 1772, 1865, 1867, 1872, 2017, 2053, 2054, 2070, 2104, 2112, 2118, 2128, 2133, 2138, 2139, 2149, 2155, 2159, 2165, 2167, 2172, 2188, 2191, 2194], "iqr": 4, "smaller": [4, 20, 24, 33, 68, 498, 544, 1026, 1552, 1772, 1877, 1894, 1939, 1985, 2133, 2149, 2157, 2158, 2189, 2206, 2209], "reach": [4, 8, 9, 10, 24, 25, 29, 30, 40, 50, 55, 60, 1388, 1772, 1865, 1868, 1872, 1873, 2130, 2136, 2146, 2161, 2165, 2192], "At": [4, 6, 7, 8, 16, 18, 20, 24, 33, 35, 1179, 1496, 1497, 1498, 1509, 1510, 1511, 1512, 1513, 1514, 1549, 1550, 1551, 2037, 2041, 2110, 2132, 2141, 2160, 2165, 2171, 2191, 2192, 2194, 2204], "high": [4, 5, 8, 9, 10, 11, 16, 25, 29, 33, 38, 40, 47, 49, 51, 68, 118, 1501, 1629, 1906, 1907, 1939, 2041, 2098, 2108, 2113, 2115, 2118, 2136, 2141, 2142, 2144, 2145, 2146, 2157, 2160, 2161, 2165, 2170, 2175, 2176, 2177, 2181, 2185, 2187, 2188, 2191, 2198, 2203, 2204, 2207, 2208], "pseudo": [4, 86], "block_siz": [4, 834, 844, 847, 2104, 2126], "enough_data": 4, "len": [4, 24, 31, 68, 70, 75, 217, 544, 703, 705, 706, 709, 1164, 1168, 1171, 1175, 1178, 1182, 1213, 1319, 1382, 1403, 1415, 1473, 1476, 1518, 1727, 1801, 1803, 1819, 1867, 1875, 1982, 1985, 1991, 1992, 1996, 2042, 2043, 2044, 2097, 2098, 2100, 2136, 2140, 2156, 2157, 2169, 2170, 2175, 2191, 2202, 2203], "small_iqr": 4, "break": [4, 8, 29, 38, 62, 68, 680, 922, 1001, 1003, 1229, 1315, 1361, 1582, 1880, 1924, 1943, 2096, 2101, 2106, 2108, 2121, 2135, 2157, 2170, 2179, 2184, 2188, 2190, 2192, 2195, 2197, 2199], "stop": [4, 6, 29, 38, 40, 50, 51, 53, 56, 894, 923, 966, 1081, 1229, 1313, 1388, 1501, 1816, 1877, 2097, 2100, 2159, 2165, 2169, 2191, 2195, 2203], "repetit": [4, 1917, 2017], "statist": [4, 25, 29, 38, 813, 814, 838, 839, 840, 842, 845, 1050, 1063, 1092, 1094, 1095, 1097, 1099, 1101, 1102, 1103, 1108, 1109, 1110, 1111, 1122, 1123, 1393, 1453, 1458, 1459, 1460, 1496, 1497, 1498, 1536, 1544, 1545, 1546, 1554, 1555, 1556, 1557, 1564, 1565, 1566, 1622, 1880, 2076, 2077, 2078, 2079, 2080, 2081, 2083, 2084, 2113, 2130, 2138, 2157, 2160, 2163, 2194], "minimum": [4, 15, 20, 25, 50, 55, 706, 707, 781, 807, 838, 839, 840, 845, 850, 904, 972, 1125, 1192, 1276, 1277, 1278, 1418, 1501, 1540, 1632, 1866, 1867, 1875, 1959, 1979, 1980, 1981, 1982, 1983, 1984, 2098, 2121, 2128, 2130, 2141, 2156, 2163, 2173, 2198], "total_tim": 4, "choic": [4, 9, 10, 15, 29, 33, 1331, 1379, 1583, 1592, 1902, 2132, 2155, 2163, 2170, 2190, 2191, 2204], "block": [4, 8, 9, 21, 24, 29, 31, 33, 34, 37, 50, 53, 59, 67, 68, 87, 485, 582, 583, 584, 844, 862, 964, 980, 1039, 1044, 1101, 1336, 1388, 1445, 1509, 1510, 1511, 1512, 1513, 1514, 1528, 1558, 1559, 1560, 1561, 1562, 1563, 1585, 1588, 1626, 1627, 1628, 1629, 1630, 1634, 1682, 1758, 1772, 1979, 1980, 1981, 2054, 2097, 2099, 2100, 2113, 2118, 2121, 2122, 2126, 2130, 2133, 2135, 2139, 2141, 2155, 2160, 2165, 2170, 2182, 2201, 2203, 2206, 2208], "qualiti": [4, 8, 25], "balanc": [4, 1001], "compet": [4, 2146], "amort": 4, "invoc": [4, 6, 68, 870, 1001, 1006, 1227, 1318, 1331, 2018, 2097, 2100, 2133, 2136, 2143, 2165, 2167, 2183, 2188, 2201, 2203], "less": [4, 7, 8, 15, 20, 24, 25, 29, 36, 38, 51, 53, 55, 59, 363, 680, 837, 937, 939, 948, 949, 1026, 1097, 1115, 1145, 1189, 1193, 1202, 1340, 1352, 1388, 1404, 1405, 1486, 1542, 1596, 1605, 1614, 1620, 1819, 1915, 2025, 2027, 2079, 2095, 2098, 2100, 2121, 2133, 2136, 2139, 2146, 2147, 2156, 2160, 2191, 2194, 2201], "bias": [4, 25, 744, 770, 806, 1496, 1497, 1498, 1533, 1534, 1536, 1544, 1545, 1546, 1552, 1553, 1554, 1598, 1600, 1622, 1642, 1643, 2157], "trivial": [4, 33, 36, 40, 43, 57, 745, 991, 1222, 1229, 1789, 1982, 2155, 2167, 2191, 2193], "low": [4, 8, 19, 25, 33, 38, 59, 498, 940, 1239, 1259, 1501, 1885, 1902, 1906, 1907, 1924, 1998, 2098, 2113, 2115, 2118, 2133, 2146, 2147, 2159, 2172, 2177, 2191, 2197, 2198, 2205], "digit": [4, 985, 1943, 2095, 2111, 2143, 2147], "microsecond": [4, 2133], "bia": [4, 10, 29, 35, 55, 70, 722, 723, 724, 725, 726, 727, 728, 729, 730, 735, 736, 737, 738, 739, 740, 741, 742, 743, 744, 745, 748, 749, 750, 751, 752, 753, 759, 761, 762, 763, 764, 766, 770, 771, 774, 775, 782, 783, 784, 791, 804, 1212, 1315, 1325, 1486, 1499, 1509, 1510, 1511, 1512, 1513, 1514, 1533, 1534, 1552, 1553, 1554, 1555, 1556, 1557, 1558, 1559, 1560, 1561, 1562, 1563, 1564, 1565, 1566, 1567, 1569, 1582, 1588, 1598, 1599, 1600, 1626, 1628, 1630, 1643, 1658, 1659, 1663, 1664, 1665, 1666, 1667, 1668, 1689, 1698, 1702, 1705, 1740, 1762, 1772, 1784, 1786, 1789, 1790, 1791, 1805, 1807, 1809, 1824, 1827, 1839, 1840, 1841, 1842, 1843, 1844, 1845, 1846, 1847, 1856, 1859, 1860, 1861, 1862, 1863, 1880, 1899, 2098, 2104, 2108, 2110, 2112, 2121, 2127, 2136, 2144, 2149, 2152, 2157, 2160, 2162, 2170, 2175, 2198], "period": [4, 10, 34, 50, 970, 979, 1050, 1088, 1102, 1103, 1108, 1122, 1123, 1165, 1273, 1274, 1335, 1879, 1949, 1950, 1951, 1952, 1953, 1954, 1955, 1956, 1957, 1958, 1959, 2098, 2113, 2118, 2146, 2208], "overal": [4, 10, 24, 35, 40, 50, 948, 1161, 1162, 1164, 1166, 1167, 1168, 1169, 1170, 1171, 1173, 1174, 1175, 1176, 1177, 1178, 1179, 1180, 1182, 1629, 2130, 2132, 2133, 2146, 2160, 2161, 2197], "main": [4, 8, 24, 25, 29, 30, 31, 36, 38, 40, 41, 42, 43, 48, 50, 51, 53, 54, 55, 68, 259, 495, 834, 1001, 1132, 1133, 1134, 1135, 1136, 1378, 1690, 1976, 2024, 2025, 2026, 2027, 2095, 2097, 2113, 2118, 2120, 2121, 2129, 2130, 2133, 2135, 2136, 2137, 2139, 2144, 2146, 2150, 2151, 2152, 2160, 2165, 2166, 2175, 2176, 2179, 2182, 2184, 2189, 2191, 2194, 2202, 2203], "collect_callgrind": 4, "collect_baselin": 4, "retain_out_fil": 4, "callgrindstat": 4, "tupl": [4, 6, 13, 15, 20, 24, 29, 31, 35, 36, 37, 40, 47, 48, 50, 55, 56, 57, 59, 68, 232, 318, 444, 446, 448, 492, 496, 519, 536, 559, 582, 583, 584, 703, 705, 706, 707, 709, 745, 776, 777, 778, 779, 782, 783, 784, 789, 795, 796, 797, 805, 809, 812, 831, 832, 834, 844, 862, 883, 890, 891, 907, 908, 915, 916, 917, 918, 919, 921, 934, 935, 937, 938, 939, 940, 941, 942, 943, 948, 949, 952, 953, 954, 968, 984, 995, 1003, 1011, 1018, 1025, 1041, 1051, 1052, 1055, 1068, 1089, 1095, 1124, 1125, 1143, 1146, 1148, 1162, 1164, 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1825, 1835, 1875, 1905, 1907, 1909, 1936, 1979, 1980, 1981, 1982, 1983, 1984, 1993, 2074, 2094, 2100, 2101, 2120, 2121, 2133, 2135, 2136, 2139, 2144, 2152, 2155, 2158, 2160, 2165, 2167, 2170, 2172, 2173, 2175, 2177, 2190, 2193, 2194, 2202, 2203, 2204, 2211], "jitter": 4, "interpret": [4, 24, 29, 34, 38, 40, 50, 53, 55, 56, 57, 232, 789, 795, 909, 943, 1041, 1100, 1166, 1167, 1168, 1173, 1175, 1176, 1177, 1178, 1199, 1227, 1266, 1278, 1315, 1320, 1327, 1331, 1368, 1388, 1521, 1646, 1688, 1699, 1700, 1759, 1936, 1941, 2095, 2099, 2100, 2104, 2118, 2132, 2133, 2170, 2172, 2174, 2191, 2192, 2203], "ideal": [4, 49, 51, 1331, 1863, 2120, 2121, 2194, 2203], "analysi": [4, 25, 38, 55, 57, 68, 1335, 1885, 1949, 1950, 1951, 1952, 1953, 1954, 1955, 1956, 1957, 1958, 1959, 2101, 2108, 2144, 2151, 2152, 2181, 2189, 2191, 2192, 2193, 2197], "valgrind": 4, "degrad": [4, 15, 59, 2132, 2135, 2136, 2170], "due": [4, 5, 6, 8, 25, 29, 31, 33, 38, 49, 59, 64, 68, 70, 72, 485, 909, 937, 938, 943, 1022, 1219, 1328, 1351, 1352, 1379, 1405, 1521, 1535, 1626, 1635, 1699, 1740, 1823, 1839, 1840, 1841, 1842, 1843, 1844, 1845, 1847, 1859, 1860, 1861, 1862, 1863, 1998, 2097, 2133, 2141, 2142, 2147, 2148, 2149, 2155, 2160, 2161, 2165, 2167, 2188, 2194, 2199, 2203, 2204, 2206, 2208], "amelior": 4, "suffici": [4, 15, 25, 29, 38, 42, 50, 51, 56, 59, 485, 1013, 1843, 1844, 1862, 1998, 2095, 2104, 2140, 2170, 2173, 2194], "callgrind_control": 4, "callgrind_annot": 4, "boundari": [4, 33, 35, 68, 789, 795, 985, 1269, 1503, 1504, 1505, 1506, 1507, 1508, 1604, 1605, 1606, 1607, 1608, 1609, 1635, 1638, 1639, 1640, 1699, 1759, 1868, 1875, 2098, 2121, 2135, 2158, 2165, 2194], "caller": [4, 40, 50, 68, 883, 1315, 1582, 1630, 1880, 1982, 2104, 2130, 2133, 2165, 2167], "structur": [4, 6, 10, 13, 24, 25, 29, 33, 35, 39, 40, 43, 51, 55, 56, 57, 59, 68, 70, 71, 75, 79, 921, 934, 935, 967, 1001, 1018, 1196, 1209, 1214, 1227, 1234, 1235, 1331, 1582, 1642, 1802, 1806, 1838, 1885, 1998, 2048, 2099, 2101, 2104, 2107, 2113, 2126, 2133, 2135, 2136, 2137, 2139, 2146, 2149, 2150, 2155, 2157, 2158, 2165, 2172, 2175, 2176, 2177, 2179, 2181, 2184, 2190, 2191, 2192, 2194, 2195, 2199, 2206], "restrict": [4, 9, 13, 24, 38, 56, 59, 64, 65, 1003, 1018, 1387, 1410, 1517, 2099, 2100, 2101, 2120, 2121, 2130, 2133, 2141, 2149, 2160, 2190], "builtin": [4, 29, 68, 70, 79, 1328, 1387, 1911, 2005, 2097, 2099, 2101, 2165, 2167, 2191, 2194, 2203, 2204], "surpris": [4, 9, 59, 2095, 2141, 2147, 2193], "serial": [4, 16, 24, 29, 31, 34, 50, 1315, 1325, 1326, 1387, 1582, 1762, 1880, 1927, 2095, 2096, 2098, 2111, 2120, 2130, 2133, 2139, 2143, 2144, 2146, 2152, 2155, 2158, 2160, 2165, 2166, 2172], "subsequ": [4, 8, 15, 16, 18, 29, 55, 57, 59, 68, 1001, 1040, 1075, 1315, 1327, 1331, 1509, 1510, 1511, 1512, 1513, 1514, 1582, 1611, 1880, 2121, 2133, 2148, 2155, 2159, 2165, 2170, 2183, 2184, 2188, 2194], "deseri": [4, 31, 1229, 1387, 1762, 2095, 2111, 2149, 2165, 2172], "globalsbridg": 4, "care": [4, 8, 15, 29, 38, 59, 67, 68, 1166, 1168, 1228, 1229, 1582, 2118, 2132, 2133, 2135, 2136, 2138, 2144, 2146, 2149, 2155, 2165, 2170, 2188, 2193, 2194, 2195], "reli": [4, 10, 15, 24, 25, 35, 40, 43, 59, 68, 149, 922, 948, 1145, 1518, 1863, 2109, 2130, 2132, 2135, 2136, 2139, 2148, 2149, 2170, 2174, 2193, 2194, 2195, 2197], "pickl": [4, 24, 25, 29, 1315, 1323, 1387, 1582, 1880, 1927, 2095, 2111, 2118, 2149, 2153, 2158, 2165, 2206], "transfer": [4, 24, 29, 59, 2110, 2118, 2133, 2142, 2144, 2157, 2158, 2165, 2173], "profil": [4, 5, 18, 20, 47, 68, 485, 956, 957, 964, 1001, 1056, 1329, 1460, 1765, 1766, 1767, 1768, 1943, 2096, 2115, 2139, 2155, 2156, 2165, 2182, 2183, 2194, 2203], "empti": [4, 13, 28, 29, 31, 36, 40, 48, 55, 56, 57, 59, 64, 68, 232, 320, 512, 514, 522, 536, 544, 831, 953, 968, 971, 972, 982, 988, 1018, 1145, 1147, 1313, 1315, 1316, 1341, 1361, 1362, 1367, 1374, 1397, 1398, 1399, 1400, 1407, 1415, 1452, 1495, 1517, 1525, 1582, 1589, 1661, 1680, 1727, 1823, 1835, 1836, 1880, 1982, 1997, 2014, 2094, 2097, 2098, 2099, 2100, 2102, 2110, 2112, 2119, 2120, 2128, 2131, 2133, 2136, 2148, 2149, 2156, 2158, 2170, 2171, 2176, 2179, 2190, 2191, 2193, 2198, 2201], "drive": [4, 10, 29, 2130], "facil": [4, 26, 1387, 2118], "analyz": [4, 5, 19, 55, 57, 68, 2135, 2136, 2139, 2147, 2149, 2204], "manipul": [4, 20, 59, 67, 1319, 2129, 2138, 2144, 2163, 2172, 2202], "1000000": [4, 1839], "mirror": [4, 138, 1772], "semant": [4, 10, 12, 18, 29, 36, 37, 50, 51, 55, 56, 59, 66, 68, 87, 149, 806, 903, 904, 905, 922, 943, 983, 1044, 1101, 1203, 1214, 1227, 1242, 1243, 1319, 1326, 1385, 1445, 1495, 1661, 1733, 1781, 1782, 1863, 2033, 2048, 2096, 2099, 2100, 2104, 2107, 2121, 2137, 2144, 2155, 2165, 2170, 2183, 2194], "number_per_run": 4, "raw_tim": 4, "task_spec": 4, "serializ": [4, 25, 2097], "consum": [4, 24, 31, 35, 39, 53, 56, 67, 434, 1145, 2118, 2133, 2139, 2146, 2151, 2155, 2175], "extrapol": 4, "sinc": [4, 8, 15, 24, 25, 29, 31, 33, 37, 38, 43, 51, 53, 55, 57, 59, 68, 314, 487, 533, 757, 795, 805, 822, 823, 833, 991, 1092, 1094, 1097, 1145, 1185, 1186, 1187, 1202, 1312, 1315, 1320, 1326, 1329, 1405, 1494, 1524, 1578, 1579, 1580, 1582, 1635, 1643, 1653, 1688, 1690, 1759, 1762, 1806, 1825, 1830, 1838, 1839, 1840, 1842, 1843, 1844, 1845, 1847, 1859, 1860, 1861, 1862, 1863, 1864, 1865, 1866, 1867, 1868, 1869, 1870, 1871, 1872, 1873, 1874, 1875, 1876, 1877, 1878, 1879, 1880, 1881, 1993, 2076, 2077, 2079, 2097, 2098, 2099, 2100, 2104, 2106, 2110, 2113, 2121, 2129, 2130, 2133, 2136, 2137, 2138, 2139, 2141, 2143, 2144, 2149, 2150, 2151, 2155, 2156, 2158, 2159, 2160, 2164, 2165, 2166, 2167, 2170, 2171, 2173, 2174, 2176, 2177, 2185, 2188, 2191, 2194, 2199, 2202, 2203, 2204], "properti": [4, 24, 29, 30, 31, 34, 35, 36, 38, 42, 47, 50, 55, 56, 59, 68, 804, 909, 935, 952, 962, 1041, 1070, 1161, 1162, 1164, 1166, 1167, 1168, 1176, 1177, 1178, 1315, 1490, 1582, 1616, 1641, 1746, 1762, 1773, 1774, 1775, 1776, 1816, 1880, 2065, 2100, 2104, 2113, 2126, 2130, 2133, 2136, 2141, 2152, 2157, 2159, 2165, 2170, 2172, 2173, 2176, 2179, 2184, 2190, 2191, 2192, 2193, 2205, 2209], "significant_figur": 4, "figur": [4, 8, 9, 31, 36, 68, 957, 2132, 2135, 2136, 2153, 2155, 2167, 2175, 2181, 2191, 2194, 2208], "intend": [4, 31, 50, 55, 57, 68, 919, 929, 932, 934, 1046, 1229, 1335, 1653, 1765, 1766, 1767, 1768, 1839, 1840, 1842, 1843, 1844, 1845, 1847, 1859, 1860, 1861, 1862, 1863, 2100, 2128, 2130, 2133, 2144, 2155, 2158, 2203], "interquartil": 4, "mitig": [4, 70, 2139, 2147, 2172], "tail": [4, 24, 48], "645": 4, "conjunct": [4, 24, 29, 34, 59, 834, 842, 1653, 1688, 1772, 2163, 2165], "trim_sigfig": 4, "human": [4, 14, 35, 48, 55, 1088, 1102, 2128, 2155, 2192, 2205], "raw": [4, 68, 1199, 1374, 2104, 2133, 2158, 2159], "built_with_debug_symbol": 4, "baseline_inclusive_stat": 4, "baseline_exclusive_stat": 4, "stmt_inclusive_stat": 4, "stmt_exclusive_stat": 4, "stmt_callgrind_out": 4, "done": [4, 16, 24, 29, 31, 33, 34, 35, 36, 38, 48, 56, 59, 67, 68, 485, 495, 680, 770, 948, 955, 1162, 1164, 1167, 1168, 1170, 1171, 1174, 1175, 1177, 1178, 1180, 1182, 1331, 1405, 1496, 1497, 1498, 1518, 1521, 1533, 1544, 1545, 1546, 1556, 1557, 1564, 1565, 1566, 1567, 1592, 1622, 1628, 1630, 1680, 1772, 1865, 1872, 1880, 1988, 2100, 2110, 2112, 2118, 2130, 2132, 2133, 2135, 2136, 2137, 2138, 2144, 2147, 2152, 2153, 2155, 2157, 2160, 2161, 2165, 2167, 2173, 2189, 2190, 2191, 2193, 2200, 2202], "functioncount": 4, "stat": [4, 62, 812, 834, 850, 863, 864, 1101, 1109, 1110, 1111, 1622, 1772, 2083, 2084, 2113, 2133, 2180, 2194, 2204], "as_standard": 4, "strip": [4, 1690, 1772, 2097, 2156, 2170], "prefix": [4, 29, 31, 40, 50, 59, 68, 758, 767, 1315, 1582, 1772, 1880, 2095, 2106, 2130, 2155, 2158, 2160, 2180, 2201], "stumbl": 4, "filepath": 4, "dif": 4, "compon": [4, 8, 10, 16, 19, 20, 29, 36, 38, 68, 680, 923, 948, 1003, 1165, 1166, 1168, 1176, 1177, 1178, 1355, 1626, 1627, 1628, 1629, 1630, 1762, 1885, 1993, 2046, 2047, 2101, 2106, 2121, 2126, 2130, 2135, 2136, 2143, 2144, 2170, 2172, 2183, 2189, 2195, 2203, 2204], "locat": [4, 10, 15, 29, 31, 35, 38, 47, 55, 57, 153, 253, 485, 512, 680, 908, 983, 985, 1053, 1124, 1125, 1337, 1387, 1413, 1418, 1421, 1468, 1518, 1528, 1617, 1634, 1653, 1688, 1755, 1758, 1772, 1795, 1896, 1931, 1952, 1974, 1978, 2036, 2095, 2097, 2111, 2126, 2133, 2149, 2158, 2159, 2165, 2167, 2170, 2175, 2180, 2184, 2188, 2193, 2203], "someth": [4, 8, 55, 56, 61, 68, 937, 939, 1145, 1229, 1239, 1315, 1331, 1582, 1839, 1840, 1841, 1842, 1843, 1844, 1845, 1846, 1847, 1856, 1859, 1860, 1861, 1862, 1863, 1880, 2008, 2102, 2113, 2118, 2119, 2121, 2130, 2142, 2150, 2155, 2165, 2189, 2190, 2191, 2194, 2196, 2202, 2203, 2204, 2205], "resembl": [4, 16, 2184], "23234231": 4, "first_build_dir": 4, "foo": [4, 15, 29, 47, 48, 55, 67, 68, 70, 78, 79, 834, 842, 883, 888, 1001, 1013, 1202, 1203, 1212, 1313, 1318, 1323, 1326, 1327, 1330, 1331, 1825, 2037, 2039, 2095, 2097, 2099, 2100, 2104, 2108, 2136, 2144, 2155, 2158, 2177, 2188, 2191, 2194, 2203, 2204], "9823794": 4, "bar": [4, 8, 47, 55, 68, 70, 78, 79, 883, 888, 1013, 1026, 1202, 1318, 1326, 1991, 1992, 2043, 2044, 2095, 2097, 2099, 2104, 2111, 2144, 2155, 2158, 2177], "53453": 4, "src": [4, 29, 68, 195, 227, 312, 320, 470, 511, 512, 513, 514, 515, 516, 518, 537, 1136, 1203, 1626, 1629, 1630, 1836, 1928, 1929, 1930, 1934, 1964, 2010, 2036, 2098, 2115, 2121, 2158, 2198], "function_that_actually_chang": 4, "second_build_dir": 4, "cancel": [4, 1312], "site": [4, 8, 2184], "denois": 4, "explan": [4, 10, 21, 26, 43, 891, 1315, 1582, 1880, 2135, 2136, 2144, 2194, 2200, 2204], "delta": [4, 38, 770, 1026, 1533, 1542, 1552, 1614, 1697, 1840, 1991, 1992, 2021, 2043, 2044, 2098, 2128], "inclus": [4, 38, 55, 86, 512, 1233, 1239, 1276, 1278, 1386, 1402, 1408, 1644, 1645, 1906, 1907, 1990, 2164, 2177], "diff": [4, 8, 2097, 2098, 2121, 2156], "One": [4, 9, 15, 29, 31, 55, 56, 62, 64, 68, 582, 980, 1003, 1203, 1214, 1306, 1635, 1726, 1773, 1781, 1782, 1789, 1793, 1863, 1868, 1877, 1906, 1907, 2031, 2048, 2097, 2099, 2100, 2120, 2131, 2132, 2133, 2136, 2143, 2149, 2159, 2167, 2175, 2185, 2189, 2190, 2192, 2193, 2201, 2208], "reason": [4, 9, 10, 24, 29, 31, 33, 40, 55, 59, 64, 680, 812, 870, 934, 935, 1166, 1168, 1315, 1331, 1332, 1351, 1352, 1379, 1416, 1494, 1521, 1582, 1690, 1740, 1795, 1880, 1927, 2097, 2099, 2100, 2104, 2106, 2121, 2130, 2135, 2137, 2139, 2147, 2149, 2155, 2165, 2173, 2189, 2190, 2191, 2193, 2194, 2197, 2203, 2204, 2205], "unit": [4, 12, 15, 38, 40, 51, 59, 68, 770, 771, 1163, 1181, 1490, 1500, 1523, 1526, 1531, 1532, 1533, 1534, 1601, 1602, 1612, 1653, 1677, 1681, 1686, 1687, 1734, 1743, 1799, 1800, 1804, 1807, 1809, 1812, 1908, 2023, 2078, 2114, 2130, 2132, 2139, 2141, 2158, 2187, 2188], "next": [4, 24, 29, 33, 35, 38, 50, 55, 56, 57, 59, 68, 559, 770, 1227, 1319, 1482, 1534, 1553, 1600, 1772, 1818, 2118, 2129, 2130, 2132, 2133, 2136, 2137, 2139, 2144, 2146, 2151, 2159, 2165, 2166, 2170, 2172, 2173, 2175, 2184, 2188, 2191, 2192, 2194], "logic": [4, 6, 13, 15, 24, 37, 41, 54, 57, 59, 68, 973, 975, 976, 978, 1008, 1009, 1010, 1162, 1164, 1167, 1168, 1170, 1171, 1174, 1175, 1177, 1178, 1180, 1182, 1203, 1229, 1319, 1397, 1398, 1399, 1400, 1410, 1588, 1772, 1894, 1921, 2007, 2100, 2101, 2133, 2135, 2136, 2137, 2141, 2160, 2191], "question": [4, 11, 24, 68, 1237, 1827, 2096, 2130, 2139, 2182, 2190, 2193], "involv": [4, 6, 8, 10, 12, 24, 29, 57, 59, 62, 64, 68, 1225, 1772, 1939, 2100, 2119, 2121, 2130, 2133, 2135, 2138, 2144, 2147, 2155, 2160, 2165, 2166, 2167, 2170, 2190, 2193, 2201, 2203], "look": [4, 5, 8, 9, 10, 13, 16, 29, 36, 38, 49, 50, 55, 56, 57, 61, 64, 68, 70, 80, 81, 485, 891, 937, 939, 1018, 1227, 1238, 1315, 1410, 1486, 1582, 1679, 1808, 1839, 1840, 1841, 1842, 1843, 1844, 1845, 1846, 1847, 1856, 1859, 1860, 1861, 1862, 1863, 1880, 1979, 1980, 1981, 1983, 1984, 2097, 2099, 2104, 2119, 2126, 2129, 2130, 2133, 2137, 2143, 2144, 2146, 2150, 2155, 2158, 2160, 2165, 2166, 2181, 2185, 2188, 2189, 2190, 2191, 2194, 2195, 2196, 2197, 2200, 2201, 2202, 2203, 2204, 2206], "autom": [4, 9, 68, 2097, 2160, 2184, 2194], "easili": [4, 8, 9, 12, 25, 29, 31, 35, 55, 1203, 1589, 1688, 1727, 1863, 1924, 2037, 2112, 2121, 2136, 2137, 2141, 2144, 2149, 2152, 2157, 2164, 2165, 2167, 2175, 2194, 2202, 2203, 2204], "exclus": [4, 24, 29, 38, 40, 50, 59, 68, 745, 1278, 1772, 1906, 1907, 1910, 2130, 2177], "basi": [4, 10, 11, 38, 680, 1388, 1868, 2133, 2143, 2160, 2165, 2183, 2188, 2203], "thought": [4, 47, 68, 1163, 1165, 1181, 1233, 2191], "path_and_function_nam": 4, "children": [4, 35, 43, 59, 68, 803, 1315, 1582, 1880, 2118, 2139, 2144, 2158, 2167], "identifi": [4, 8, 10, 29, 31, 40, 44, 47, 50, 51, 53, 68, 85, 485, 831, 1278, 1387, 1444, 1622, 2101, 2104, 2118, 2131, 2143, 2144, 2149, 2158, 2165, 2166, 2167, 2172, 2175, 2183, 2190, 2201, 2203], "hot": [4, 38, 1690, 1726, 2141, 2188], "spot": [4, 1781, 1782], "_data": 4, "truncate_row": 4, "_linewidth": 4, "subtract": [4, 312, 563, 894, 1199, 1690, 1979, 1980, 1981, 1983, 1984, 1994, 2098, 2156, 2170], "index": [4, 16, 20, 24, 25, 29, 31, 35, 38, 48, 55, 57, 68, 190, 207, 280, 311, 312, 313, 314, 315, 316, 318, 320, 321, 470, 471, 511, 512, 513, 514, 515, 516, 517, 518, 683, 684, 685, 689, 690, 691, 705, 706, 831, 883, 906, 921, 923, 934, 935, 966, 985, 1014, 1058, 1061, 1095, 1124, 1125, 1165, 1206, 1207, 1208, 1212, 1214, 1256, 1287, 1288, 1289, 1290, 1313, 1332, 1337, 1345, 1363, 1381, 1395, 1403, 1405, 1409, 1413, 1416, 1417, 1418, 1421, 1447, 1450, 1468, 1474, 1477, 1478, 1486, 1501, 1517, 1524, 1525, 1583, 1584, 1589, 1592, 1593, 1632, 1634, 1679, 1680, 1726, 1801, 1803, 1810, 1811, 1816, 1829, 1865, 1866, 1867, 1868, 1869, 1871, 1872, 1873, 1874, 1875, 1878, 1879, 1881, 1896, 1928, 1929, 1930, 1931, 1933, 1934, 1935, 1964, 1975, 1976, 1979, 1980, 1981, 1983, 1984, 1993, 2010, 2031, 2032, 2034, 2035, 2036, 2039, 2048, 2056, 2058, 2096, 2097, 2098, 2100, 2104, 2107, 2118, 2119, 2120, 2126, 2130, 2133, 2135, 2137, 2140, 2142, 2148, 2156, 2157, 2167, 2169, 2170, 2172, 2173, 2174, 2175, 2176, 2177, 2193, 2194, 2198, 2203, 2204], "cpython": [4, 15, 55, 68, 2182, 2192], "known": [4, 8, 10, 23, 28, 29, 34, 44, 50, 56, 60, 63, 65, 70, 80, 81, 1209, 1210, 1229, 1331, 1332, 1387, 1509, 1510, 1511, 1512, 1513, 1514, 1528, 1552, 1598, 1612, 1614, 1634, 1699, 1743, 1762, 1951, 1952, 1998, 2014, 2098, 2102, 2104, 2113, 2118, 2121, 2128, 2130, 2132, 2148, 2155, 2157, 2165, 2167, 2171, 2182, 2190, 2195, 2204], "quit": [4, 8, 68, 1781, 1782, 2100, 2126, 2136, 2138, 2158, 2165, 2201], "noisi": 4, "higher": [4, 8, 9, 25, 29, 33, 59, 60, 65, 70, 75, 79, 87, 149, 922, 924, 943, 1044, 1089, 1101, 1137, 1204, 1210, 1214, 1223, 1239, 1242, 1276, 1292, 1445, 1475, 1517, 1574, 1589, 1740, 1840, 1896, 1998, 2048, 2054, 2104, 2133, 2136, 2137, 2141, 2143, 2149, 2160, 2161, 2165, 2173, 2190, 2203], "filter": [4, 21, 543, 782, 783, 784, 1312, 1335, 1509, 1510, 1511, 1512, 1513, 1514, 1663, 1664, 1665, 1666, 1667, 1668, 1949, 1950, 1951, 1952, 1953, 1954, 1955, 1956, 1957, 1958, 1959, 1993, 2100, 2158, 2204], "transform": [4, 13, 24, 31, 35, 36, 37, 55, 59, 64, 66, 414, 791, 810, 865, 867, 886, 887, 992, 1161, 1162, 1163, 1164, 1165, 1166, 1167, 1168, 1169, 1170, 1171, 1173, 1174, 1175, 1176, 1177, 1178, 1179, 1180, 1182, 1202, 1203, 1204, 1208, 1213, 1236, 1312, 1325, 1499, 1536, 1544, 1545, 1546, 1554, 1569, 1588, 1611, 1627, 1628, 1629, 1630, 1653, 1659, 1688, 1705, 1725, 1762, 1839, 1840, 1841, 1842, 1843, 1844, 1845, 1846, 1847, 1855, 1859, 1860, 1861, 1862, 1863, 1959, 1993, 2108, 2121, 2137, 2139, 2140, 2160, 2170, 2175, 2181, 2182, 2184, 2192, 2196, 2203], "rather": [4, 9, 10, 15, 29, 40, 43, 53, 55, 57, 59, 68, 70, 79, 789, 795, 961, 991, 1014, 1313, 1316, 1387, 1478, 1653, 1688, 1699, 1759, 1790, 1939, 1943, 2097, 2100, 2120, 2121, 2131, 2133, 2136, 2137, 2155, 2158, 2160, 2165, 2170, 2175, 2188, 2189, 2191, 2193, 2194, 2195, 2196, 2203], "unicod": [4, 2101], "dictionari": [4, 15, 24, 25, 31, 35, 36, 38, 55, 63, 68, 414, 680, 804, 805, 806, 810, 829, 830, 831, 865, 867, 868, 883, 887, 893, 968, 1001, 1101, 1202, 1212, 1222, 1229, 1315, 1316, 1323, 1327, 1331, 1332, 1387, 1388, 1458, 1459, 1464, 1524, 1525, 1582, 1583, 1592, 1679, 1806, 1839, 1840, 1841, 1842, 1843, 1844, 1845, 1846, 1847, 1856, 1859, 1860, 1861, 1862, 1863, 1880, 2063, 2081, 2082, 2099, 2101, 2136, 2144, 2155, 2157, 2165, 2175, 2180, 2199, 2205, 2206], "lookup": [4, 31, 38, 50, 1524, 1679, 2097, 2101, 2132, 2166, 2197], "map": [4, 15, 29, 31, 36, 38, 40, 47, 48, 50, 51, 55, 56, 57, 59, 64, 65, 68, 70, 79, 232, 694, 745, 804, 805, 809, 810, 813, 828, 829, 831, 837, 867, 868, 883, 884, 885, 886, 887, 893, 914, 982, 1044, 1197, 1203, 1214, 1225, 1227, 1229, 1234, 1269, 1278, 1323, 1326, 1387, 1388, 1512, 1513, 1514, 1520, 1521, 1522, 1526, 1528, 1578, 1579, 1580, 1583, 1592, 1674, 1675, 1676, 1681, 1789, 1806, 1839, 1840, 1841, 1842, 1843, 1844, 1845, 1846, 1847, 1856, 1859, 1860, 1861, 1862, 1863, 1896, 1898, 2032, 2033, 2048, 2054, 2101, 2104, 2112, 2120, 2122, 2130, 2133, 2135, 2136, 2137, 2145, 2150, 2152, 2155, 2157, 2158, 2160, 2163, 2165, 2166, 2167, 2170, 2172, 2177, 2192, 2193, 2202, 2205], "agnost": [4, 17, 50, 1027, 1028, 1029, 1030, 1031, 1032, 1033, 1034, 1035, 1688, 1781, 1782, 2104, 2120], "reliabl": 4, "warrant": 4, "except": [4, 6, 8, 10, 15, 29, 30, 31, 33, 36, 38, 40, 42, 43, 47, 51, 55, 56, 59, 67, 68, 232, 582, 586, 587, 616, 683, 694, 703, 705, 706, 709, 770, 833, 914, 947, 948, 949, 988, 995, 999, 1043, 1053, 1055, 1088, 1102, 1137, 1143, 1145, 1191, 1192, 1212, 1214, 1231, 1279, 1315, 1323, 1331, 1333, 1337, 1371, 1387, 1403, 1413, 1415, 1416, 1418, 1421, 1443, 1473, 1476, 1478, 1533, 1552, 1582, 1598, 1635, 1726, 1765, 1772, 1790, 1816, 1823, 1824, 1828, 1830, 1880, 1893, 1917, 1933, 1936, 1969, 1977, 1982, 1989, 1991, 1992, 1996, 2021, 2043, 2044, 2048, 2049, 2096, 2097, 2099, 2100, 2101, 2102, 2104, 2107, 2118, 2133, 2136, 2139, 2144, 2147, 2150, 2158, 2165, 2167, 2170, 2172, 2173, 2177, 2189, 2191, 2193, 2201, 2202, 2206, 2208], "filter_fn": 4, "map_fn": 4, "coalesc": [4, 322, 327, 543, 613, 1052, 1978, 1982, 2031, 2098, 2135, 2156, 2170, 2194], "entri": [4, 20, 29, 30, 34, 38, 40, 41, 48, 50, 51, 314, 745, 887, 888, 961, 1086, 1087, 1135, 1186, 1187, 1290, 1315, 1336, 1524, 1525, 1582, 1588, 1679, 1680, 1801, 1802, 1810, 1839, 1840, 1841, 1842, 1843, 1844, 1845, 1846, 1847, 1856, 1859, 1860, 1861, 1862, 1863, 1864, 1865, 1866, 1867, 1869, 1870, 1871, 1872, 1873, 1874, 1875, 1876, 1878, 1879, 1880, 1881, 1975, 2097, 2100, 2101, 2107, 2121, 2130, 2135, 2136, 2141, 2144, 2149, 2163, 2170, 2175, 2180, 2189, 2190, 2191, 2192], "color": [4, 68, 1546, 2099, 2100, 2175, 2195, 2203], "rowwis": [4, 37], "columnwis": 4, "extend_result": 4, "highlight_warn": 4, "highlight": [4, 56, 740, 741, 2100], "trim_significant_figur": 4, "trim": [4, 970, 979, 1161, 1162, 1164, 1166, 1167, 1168, 1169, 1170, 1171, 1173, 1174, 1175, 1176, 1177, 1178, 1179, 1180, 1182, 1273, 1274, 1312], "h": [5, 11, 15, 35, 38, 486, 487, 751, 752, 753, 770, 993, 994, 1345, 1352, 1355, 1365, 1379, 1484, 1485, 1492, 1493, 1495, 1497, 1498, 1510, 1511, 1513, 1521, 1522, 1524, 1526, 1533, 1534, 1545, 1546, 1552, 1553, 1554, 1565, 1566, 1576, 1577, 1594, 1595, 1598, 1600, 1617, 1633, 1636, 1637, 1653, 1661, 1688, 1724, 1730, 1731, 1740, 1789, 1790, 1824, 1836, 1885, 1997, 1998, 2098, 2115, 2119, 2120, 2126, 2130, 2133, 2135, 2136, 2138, 2141, 2155, 2174, 2175, 2176, 2184, 2190, 2200], "finit": [5, 38, 948, 949, 1304, 1305, 1351, 1352, 1362, 1363, 1379, 1405, 1468, 1472, 1494, 1997, 2136, 2141, 2177], "natur": [5, 8, 9, 12, 31, 38, 59, 70, 72, 948, 949, 1344, 1349, 1375, 1389, 1391, 1396, 1486, 1630, 1740, 2139, 2141, 2151, 2152, 2170, 2171], "against": [5, 6, 15, 29, 40, 50, 57, 806, 894, 948, 949, 1173, 1174, 1175, 1179, 1180, 1182, 1256, 1306, 1315, 1331, 1332, 1582, 1588, 1837, 1880, 2095, 2100, 2158, 2181, 2191, 2200], "cprofil": 5, "mode": [5, 9, 24, 25, 29, 31, 33, 38, 50, 53, 55, 57, 64, 65, 68, 220, 221, 487, 756, 757, 782, 783, 784, 789, 795, 796, 797, 829, 831, 841, 850, 888, 889, 918, 920, 924, 927, 928, 929, 934, 935, 937, 938, 939, 940, 944, 945, 946, 948, 1001, 1006, 1014, 1037, 1076, 1119, 1120, 1149, 1161, 1162, 1164, 1166, 1167, 1168, 1169, 1170, 1171, 1173, 1174, 1175, 1176, 1177, 1178, 1179, 1180, 1182, 1203, 1206, 1207, 1208, 1209, 1212, 1213, 1215, 1216, 1223, 1228, 1263, 1298, 1299, 1313, 1315, 1316, 1319, 1331, 1333, 1374, 1387, 1417, 1434, 1435, 1465, 1496, 1497, 1498, 1509, 1510, 1511, 1525, 1536, 1544, 1545, 1546, 1554, 1555, 1556, 1557, 1564, 1565, 1566, 1582, 1599, 1622, 1635, 1636, 1663, 1664, 1665, 1680, 1688, 1699, 1727, 1740, 1759, 1760, 1761, 1774, 1783, 1785, 1790, 1795, 1828, 1868, 1877, 1880, 1895, 1938, 1940, 2036, 2098, 2104, 2110, 2119, 2128, 2133, 2135, 2137, 2138, 2139, 2140, 2144, 2151, 2152, 2156, 2159, 2161, 2162, 2163, 2165, 2176, 2179, 2184, 2188, 2192, 2193, 2194, 2195, 2196, 2198, 2203, 2204, 2205], "correct": [5, 6, 7, 8, 25, 29, 30, 31, 35, 36, 38, 50, 55, 57, 87, 204, 208, 220, 554, 579, 602, 603, 614, 622, 929, 930, 934, 949, 1026, 1166, 1168, 1176, 1177, 1178, 1234, 1290, 1313, 1316, 1331, 1332, 1351, 1445, 1518, 1535, 1547, 1762, 1772, 1816, 1942, 1991, 1992, 2043, 2044, 2097, 2098, 2099, 2104, 2108, 2119, 2120, 2128, 2133, 2136, 2141, 2157, 2172, 2191, 2198, 2202, 2203], "launch": [5, 15, 24, 32, 35, 36, 40, 42, 43, 48, 49, 50, 54, 67, 1040, 1075, 1772, 2130, 2132, 2133, 2135, 2136, 2165, 2188, 2196], "spent": [5, 29, 962, 1875, 2132, 2144, 2194, 2201, 2204], "appear": [5, 25, 29, 38, 55, 56, 68, 883, 964, 1089, 1145, 1186, 1187, 1214, 1387, 1421, 1423, 1773, 1774, 1917, 1945, 1949, 1950, 1951, 1952, 1953, 1954, 1955, 1956, 1957, 1958, 1959, 2048, 2097, 2100, 2119, 2120, 2136, 2137, 2144, 2155, 2158, 2191, 2194, 2201], "extrem": [5, 29, 1772, 2130, 2155, 2193], "expens": [5, 24, 38, 59, 1789, 2126, 2133, 2141, 2143, 2159, 2165, 2176, 2190, 2194, 2197, 2200, 2204], "bound": [5, 16, 25, 29, 56, 59, 480, 807, 985, 996, 1159, 1160, 1229, 1233, 1238, 1315, 1491, 1492, 1493, 1575, 1576, 1577, 1582, 1601, 1688, 1877, 1880, 1910, 1931, 2100, 2101, 2128, 2130, 2139, 2158, 2160, 2171, 2190, 2191, 2193, 2203, 2204], "greater": [5, 20, 29, 50, 70, 71, 291, 616, 680, 977, 990, 991, 996, 1161, 1162, 1164, 1166, 1167, 1168, 1169, 1170, 1171, 1173, 1174, 1175, 1176, 1177, 1178, 1179, 1180, 1182, 1258, 1272, 1370, 1439, 1494, 1631, 1688, 1699, 1726, 1740, 1756, 1759, 1790, 1824, 1877, 2036, 2098, 2121, 2130, 2133, 2148, 2156, 2170, 2171], "spend": [5, 8, 20, 35, 1388, 2108, 2197], "sens": [5, 38, 50, 68, 1806, 2032, 2033, 2100, 2130, 2139], "respons": [5, 8, 10, 15, 29, 31, 34, 35, 36, 38, 40, 47, 53, 59, 67, 1003, 1040, 1075, 1231, 1570, 1706, 1772, 1982, 2069, 2104, 2130, 2133, 2136, 2137, 2144, 2165, 2193, 2203], "Of": [5, 1829, 2095, 2135, 2136, 2182, 2191, 2194], "cours": [5, 20, 68, 1217, 2095, 2135, 2136, 2165, 2191, 2194], "realiti": [5, 2139, 2203], "complic": [5, 25, 37, 68, 805, 1939, 2104, 2120, 2131, 2158, 2165, 2167, 2191, 2193, 2203], "account": [5, 35, 48, 68, 1495, 1772, 2126, 2128, 2132, 2139, 2170, 2188], "heavili": [5, 1846, 2132, 2136, 2158], "similarli": [5, 8, 29, 31, 35, 55, 67, 68, 770, 802, 806, 887, 992, 1165, 1315, 1371, 1582, 1630, 1797, 1798, 1799, 1800, 1801, 1802, 1803, 1804, 1813, 1880, 2024, 2025, 2026, 2027, 2099, 2104, 2130, 2136, 2138, 2141, 2147, 2170, 2193, 2196, 2201], "platform": [5, 9, 10, 15, 29, 42, 43, 47, 1362, 1363, 1374, 1895, 1997, 2110, 2142, 2147, 2148, 2160, 2184], "startup": [5, 20], "slower": [5, 15, 29, 35, 905, 938, 1185, 1186, 1187, 1345, 1352, 1707, 1789, 1840, 1841, 2136, 2148, 2157, 2171, 2193, 2203], "rerun": [6, 29, 2133], "segment": [6, 1101, 1199, 1218, 1614, 2133, 2158, 2194, 2199, 2203, 2206], "persist": [6, 22, 31, 55, 59, 70, 71, 72, 73, 74, 75, 76, 78, 79, 80, 81, 82, 83, 770, 829, 830, 831, 1315, 1533, 1552, 1582, 1598, 1773, 1775, 1880, 2144, 2149, 2150, 2172, 2183, 2188, 2193, 2197], "rng": [6, 24, 1073, 1116, 1429, 1440, 1454, 1462, 1932, 2067, 2088, 2133, 2148, 2164], "advanc": [6, 16, 24, 25, 40, 1829, 1998, 2039, 2104, 2107, 2120, 2133, 2136, 2142, 2146, 2174, 2175, 2194, 2203], "juggl": 6, "moder": 6, "hit": [6, 9, 15, 57, 1231, 1772, 2095, 2121, 2133, 2188, 2191, 2194, 2197, 2203, 2204], "preserve_rng_st": 6, "checkpoint_sequenti": 6, "omit": [6, 15, 29, 149, 1179, 1180, 1182, 1229, 1535, 1596, 2150, 2155, 2165, 2177, 2191, 2195], "exclud": [6, 10, 25, 33, 50, 68, 1011, 1278, 1525, 1680, 1729, 1837, 1939, 2024, 2025, 2026, 2027, 2130, 2142, 2149, 2158, 2166, 2192, 2194], "_infer_device_typ": 6, "remain": [6, 9, 20, 36, 38, 50, 68, 1423, 1524, 1525, 1679, 1680, 1762, 1772, 1781, 1782, 1797, 1798, 1799, 1800, 1801, 1802, 1803, 1804, 1813, 1839, 1840, 1841, 1842, 1843, 1844, 1845, 1846, 1847, 1849, 1859, 1860, 1861, 1862, 1863, 1877, 1896, 1918, 2112, 2113, 2118, 2129, 2138, 2144, 2157, 2172, 2184, 2192], "consequ": [6, 64, 1379, 1552, 1997, 2100, 2130, 2133, 2146, 2148, 2151], "random": [6, 36, 38, 40, 50, 51, 60, 68, 86, 153, 770, 921, 934, 935, 971, 1073, 1074, 1080, 1090, 1091, 1112, 1113, 1116, 1117, 1207, 1214, 1268, 1291, 1388, 1408, 1429, 1430, 1438, 1440, 1454, 1462, 1490, 1501, 1526, 1533, 1552, 1601, 1681, 1732, 1738, 1762, 1790, 1797, 1803, 1804, 1811, 1812, 1831, 1885, 1902, 1904, 1905, 1906, 1907, 1908, 1909, 1910, 1932, 1944, 1998, 2039, 2048, 2067, 2068, 2071, 2074, 2075, 2085, 2086, 2088, 2089, 2095, 2096, 2098, 2104, 2106, 2128, 2137, 2141, 2147, 2155, 2156, 2175, 2194, 2204], "gradient": [6, 12, 24, 25, 29, 33, 34, 35, 36, 38, 59, 60, 63, 149, 220, 221, 289, 334, 486, 487, 494, 512, 705, 706, 909, 918, 920, 922, 925, 926, 927, 928, 929, 930, 931, 932, 934, 935, 937, 938, 939, 940, 941, 942, 943, 944, 945, 948, 949, 953, 954, 972, 998, 1149, 1202, 1203, 1204, 1205, 1209, 1214, 1256, 1315, 1351, 1352, 1355, 1362, 1363, 1379, 1388, 1405, 1407, 1413, 1416, 1418, 1494, 1501, 1512, 1513, 1514, 1517, 1518, 1524, 1525, 1535, 1549, 1550, 1551, 1582, 1589, 1614, 1671, 1672, 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43, 2097, 2130, 2137, 2141, 2191, 2195], "maker": 10, "strong": 10, "toward": [10, 70, 1140, 1277, 1278, 1388, 1482, 1772, 1872, 1915, 1923, 1924, 2139], "philosophi": [10, 63, 2096], "beyond": [10, 25, 57, 1439, 1517, 1843, 1844, 1859, 1922, 2115, 2138, 2144, 2190], "encourag": [10, 15, 40, 2160, 2170, 2177], "propos": [10, 1839, 1866, 1867, 1880, 2107, 2141, 2157, 2166], "review": [10, 11, 25, 2110, 2158, 2196], "willing": [10, 2183], "invest": 10, "anyon": 10, "ownership": [10, 68], "codebas": [10, 2136, 2203], "strictli": [10, 24, 149, 190, 207, 485, 922, 985, 1278, 1315, 1319, 1582, 1880, 2130, 2171], "compani": 10, "bui": 10, "addition": [10, 15, 24, 25, 29, 31, 36, 38, 40, 55, 57, 59, 149, 512, 616, 922, 957, 1203, 1207, 1208, 1319, 1379, 1486, 1544, 1545, 1546, 1973, 2138, 2179, 2203, 2206], "membership": [10, 40, 49, 50, 2101], "That": [10, 13, 18, 20, 40, 48, 51, 61, 68, 1014, 1018, 1312, 1386, 1402, 2036, 2104, 2110, 2136, 2137, 2138, 2139, 2149, 2158, 2165, 2191, 2194], "seat": 10, "reserv": [10, 33, 47, 1101, 2081, 2101, 2133, 2144, 2206], "emploi": [10, 31, 1862, 2144, 2158, 2184], "directori": [10, 15, 29, 31, 35, 40, 48, 1837, 2095, 2111, 2143, 2155, 2158, 2159, 2175, 2184, 2185, 2194, 2204], "procedur": [10, 38, 888, 1331, 1332, 1388, 1971, 2165, 2204], "disput": 10, "made": [10, 18, 21, 29, 31, 34, 51, 55, 68, 929, 932, 934, 949, 1042, 1315, 1582, 1628, 1630, 1839, 1840, 1841, 1842, 1843, 1844, 1845, 1846, 1847, 1848, 1851, 1853, 1859, 1860, 1861, 1862, 1863, 1880, 1912, 2099, 2107, 2130, 2150, 2158, 2175, 2177, 2188, 2190, 2191, 2203, 2204, 2205], "public": [10, 11, 36, 1203, 2121, 2136, 2203, 2205], "resolut": [10, 1594, 1595, 1653, 1688, 1730, 1731, 2021, 2101, 2158, 2209], "conclus": 10, "publicli": [10, 2205], "vision": [10, 1325, 1517, 1671, 2095, 2196], "roadmap": [10, 11], "parti": [10, 50, 2095, 2097, 2104, 2133, 2137, 2144, 2158, 2191], "triag": [10, 11], "meet": [10, 11, 13, 31, 49, 1013, 1018, 1345, 2133, 2194], "Their": [10, 1137, 2136, 2193], "articul": 10, "cohes": 10, "negoti": [10, 2165], "contenti": 10, "broad": [10, 2144, 2207], "stakehold": 10, "power": [10, 13, 33, 49, 1108, 1161, 1162, 1164, 1166, 1167, 1168, 1169, 1170, 1171, 1173, 1174, 1175, 1176, 1177, 1178, 1179, 1180, 1182, 1188, 1339, 1369, 1383, 1549, 1550, 1551, 1709, 1710, 1711, 1790, 1824, 1839, 1876, 1892, 1916, 2042, 2101, 2133, 2170], "veto": 10, "admin": 10, "amongst": 10, "commonli": [10, 38, 59, 1417, 1841, 2100, 2102, 2121, 2129, 2130, 2139, 2157, 2160, 2173, 2182, 2187, 2190], "merit": 10, "demonstr": [10, 35, 55, 68, 70, 78, 79, 1501, 2097, 2144, 2149, 2152, 2153, 2157, 2165, 2172, 2184, 2192, 2194, 2196, 2201], "expertis": 10, "align": [10, 25, 55, 770, 789, 795, 838, 850, 1001, 1145, 1269, 1371, 1493, 1501, 1517, 1533, 1547, 1552, 1576, 1577, 1598, 1635, 1643, 1644, 1645, 1671, 1672, 1699, 1700, 1740, 1759, 1789, 1830, 1840, 1841, 1842, 1843, 1844, 1845, 1847, 1859, 1860, 1861, 1862, 1866, 2021, 2119, 2126, 2130, 2141, 2149, 2155, 2163, 2171], "continu": [10, 24, 29, 31, 38, 54, 57, 59, 68, 401, 607, 836, 938, 1269, 1351, 1352, 1379, 1500, 1501, 1772, 1877, 2101, 2107, 2130, 2139, 2149, 2159, 2172, 2179, 2189, 2191, 2192, 2194, 2203], "light": [10, 2175, 2203], "mainten": [10, 50, 51], "emeritu": [10, 11], "inact": [10, 1101, 2133, 2206], "contact": [10, 29], "item": [10, 24, 31, 55, 70, 80, 81, 582, 583, 584, 588, 804, 805, 806, 828, 829, 830, 831, 968, 1014, 1517, 1583, 1592, 1795, 1943, 2095, 2097, 2098, 2099, 2100, 2108, 2119, 2121, 2133, 2140, 2149, 2155, 2156, 2157, 2158, 2165, 2175, 2176, 2188, 2190, 2191, 2193, 2198, 2203], "nomine": 10, "breadth": [10, 35], "testimoni": 10, "posit": [10, 24, 31, 35, 36, 38, 55, 68, 86, 401, 470, 544, 745, 886, 890, 892, 970, 972, 979, 992, 993, 994, 1044, 1161, 1162, 1163, 1164, 1165, 1173, 1175, 1179, 1181, 1199, 1209, 1210, 1213, 1227, 1229, 1273, 1274, 1307, 1310, 1315, 1318, 1345, 1346, 1357, 1359, 1370, 1373, 1374, 1388, 1408, 1423, 1472, 1477, 1478, 1495, 1509, 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2098, 2119, 2121, 2128, 2130, 2136, 2137, 2155, 2156, 2164, 2170, 2171, 2190, 2194, 2198], "interact": [10, 16, 24, 29, 36, 68, 891, 944, 1040, 1075, 1079, 2069, 2101, 2104, 2130, 2136, 2158, 2160, 2175, 2188, 2191, 2194, 2197, 2206], "final": [10, 20, 29, 31, 34, 38, 40, 50, 55, 56, 57, 59, 63, 64, 696, 699, 700, 770, 969, 987, 1000, 1145, 1184, 1203, 1242, 1269, 1387, 1405, 1410, 1502, 1533, 1535, 1552, 1598, 1611, 1762, 1841, 1974, 2021, 2097, 2099, 2100, 2101, 2119, 2136, 2139, 2141, 2144, 2147, 2149, 2151, 2152, 2155, 2157, 2158, 2166, 2167, 2188, 2190, 2192, 2203, 2204], "declin": 10, "conflict": [10, 25, 31, 51, 2158], "lack": [10, 12, 35, 968, 1351, 1352, 1379], "unfit": 10, "conduct": [10, 1772, 1885, 1998, 2165, 2184, 2188], "filial": 10, "romant": 10, "strength": 10, "candid": [10, 961, 2158], "letter": [10, 1145], "befit": 10, "candidaci": 10, "behind": [10, 29, 55, 2096, 2149, 2166, 2193], "75": [10, 1277, 1570, 1688, 1706, 1839, 1896, 2098, 2171], "unforeseen": 10, "circumst": [10, 50, 1001, 1239, 1501, 1509, 1510, 1511, 1512, 1513, 1514, 1663, 1664, 1665, 1666, 1667, 1668, 1672, 1740, 2133], "perman": [10, 68, 1797, 1798, 1799, 1800, 1801, 1802, 1803, 1804, 1813, 2130, 2167], "unavail": [10, 1056, 2099], "rank": [10, 24, 25, 29, 30, 31, 34, 35, 36, 37, 38, 40, 44, 48, 50, 51, 54, 56, 59, 70, 79, 1361, 1362, 1363, 1370, 1405, 1574, 1622, 1772, 1795, 1885, 1998, 2098, 2135, 2139, 2146, 2155, 2165, 2166, 2183, 2190, 2203, 2204, 2208], "elect": 10, "invit": [10, 2095], "convinc": 10, "approach": [10, 25, 29, 33, 68, 989, 1214, 1495, 1880, 1939, 1971, 2048, 2097, 2118, 2133, 2136, 2141, 2151, 2152, 2153, 2157, 2160, 2165, 2194, 2203], "interview": 10, "talk": [10, 53, 2104, 2143], "gather": [10, 29, 31, 33, 35, 36, 50, 59, 512, 2011, 2036, 2098, 2136, 2138, 2139, 2143, 2156, 2158, 2198], "read": [10, 13, 19, 20, 24, 29, 31, 36, 39, 40, 43, 50, 56, 59, 63, 68, 447, 955, 1018, 1103, 1128, 1196, 1198, 1199, 1231, 1323, 1387, 1877, 2104, 2119, 2120, 2126, 2130, 2131, 2133, 2135, 2137, 2143, 2147, 2151, 2158, 2160, 2165, 2188, 2191, 2192, 2194, 2196, 2203], "attend": [10, 745, 1588, 1626], "confer": [10, 1535], "pipelin": [10, 31, 68, 2096, 2165, 2194], "world": [10, 29, 33, 34, 40, 48, 50, 51, 55, 59, 1622, 1772, 2130, 2158, 2160, 2190, 2191, 2192, 2203], "cover": [10, 56, 57, 60, 68, 70, 1119, 1145, 1575, 1713, 1714, 1715, 2100, 2102, 2120, 2130, 2136, 2139, 2141, 2143, 2144, 2165, 2192, 2197, 2201, 2207], "push": [10, 42, 1105, 1107, 1214, 1381, 2048, 2149, 2159, 2191, 2203], "codeown": 10, "notifi": [10, 30, 51, 2167], "expert": [10, 33, 2157], "strongli": [10, 29, 40, 51, 57, 1520, 1521, 1522, 1526, 1993, 2095], "failur": [10, 29, 38, 40, 42, 43, 47, 48, 49, 50, 54, 55, 680, 948, 949, 1001, 1003, 1203, 1229, 1331, 1332, 1439, 2100, 2106, 2115, 2118, 2139, 2165, 2167, 2177, 2191, 2194, 2203], "revert": [10, 38, 59, 1619, 1748, 2167], "substanti": [10, 25, 2133, 2183, 2188, 2203], "syntact": [10, 43, 68], "incompat": [10, 15, 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2160, 2190, 2191, 2198, 2203], "quantiz": [12, 28, 55, 68, 218, 325, 337, 471, 472, 473, 474, 475, 477, 710, 711, 712, 713, 714, 715, 716, 717, 718, 719, 720, 721, 722, 723, 724, 725, 726, 727, 728, 729, 730, 733, 734, 735, 736, 737, 738, 739, 740, 741, 742, 743, 744, 745, 746, 747, 748, 749, 750, 751, 752, 753, 754, 755, 756, 757, 758, 759, 760, 761, 762, 763, 764, 765, 766, 767, 768, 769, 770, 771, 772, 773, 774, 775, 776, 777, 778, 779, 780, 781, 782, 783, 784, 785, 786, 787, 788, 789, 790, 791, 792, 793, 794, 795, 796, 797, 798, 799, 800, 801, 802, 803, 804, 805, 806, 807, 808, 809, 810, 811, 812, 813, 814, 815, 816, 817, 818, 819, 820, 821, 822, 823, 824, 825, 826, 827, 828, 829, 830, 831, 832, 833, 834, 835, 836, 837, 838, 839, 840, 841, 842, 843, 844, 845, 846, 847, 848, 849, 850, 851, 852, 853, 854, 855, 856, 857, 858, 859, 860, 861, 862, 863, 864, 865, 866, 867, 868, 869, 870, 871, 872, 873, 874, 875, 876, 877, 878, 879, 880, 881, 882, 883, 884, 885, 887, 888, 889, 890, 891, 892, 893, 961, 1130, 1159, 1160, 1897, 1898, 1899, 1900, 1901, 2036, 2096, 2156, 2173, 2176, 2177, 2180, 2181], "pred": [13, 57, 70, 73, 78, 79, 1018, 1547, 1772, 2145, 2157, 2165, 2198, 2203], "union": [13, 15, 24, 31, 33, 35, 36, 37, 40, 44, 48, 51, 55, 56, 59, 68, 1001, 1018, 1221, 1224, 1225, 1227, 1229, 1255, 1287, 1301, 1327, 1387, 1483, 1484, 1485, 1487, 1488, 1489, 1491, 1492, 1493, 1529, 1530, 1549, 1550, 1551, 1575, 1576, 1577, 1582, 1626, 1628, 1630, 1633, 1646, 1685, 1795, 1816, 1895, 1927, 1988, 2039, 2098, 2099, 2100, 2104, 2121, 2126, 2149, 2155, 2158, 2172, 2175, 2177, 2181], "true_fn": [13, 56, 57, 70, 73, 78, 79, 1018], "false_fn": [13, 56, 57, 70, 73, 78, 79, 1018], "operand": [13, 57, 70, 78, 79, 977, 1018, 1145, 2100, 2101, 2136, 2170, 2173], "Its": [13, 29, 56, 68, 1026, 1053, 1315, 1363, 1381, 1382, 1407, 1582, 1830, 1839, 1840, 1841, 1842, 1843, 1844, 1845, 1846, 1847, 1856, 1859, 1860, 1861, 1862, 1863, 1880, 1977, 2045, 2100, 2135, 2172], "uniqu": [13, 29, 40, 47, 50, 51, 53, 232, 512, 516, 847, 1306, 1351, 1352, 1356, 1362, 1363, 1365, 1374, 1376, 1378, 1379, 1416, 1423, 1973, 1977, 1997, 2033, 2095, 2097, 2106, 2111, 2136, 2158, 2159, 2165, 2166, 2167, 2170, 2172, 2175, 2183, 2188, 2206], "predic": [13, 70, 75, 78, 79, 1229], "unlock": [13, 2107], "architectur": [13, 37, 68, 1067, 1161, 1162, 1164, 1166, 1167, 1168, 1169, 1170, 1171, 1173, 1174, 1175, 1176, 1177, 1178, 1179, 1180, 1182, 1517, 1626, 1671, 1940, 2062, 2133, 2147, 2152, 2155, 2157, 2160, 2170, 2187, 2203], "prototyp": [13, 19, 20, 29, 33, 36, 55, 680, 937, 939, 943, 948, 949, 1018, 1325, 1630, 1642, 1643, 1843, 1844, 2096, 2107, 2113, 2120, 2121, 2126, 2140, 2144, 2149, 2163, 2165, 2170, 2173, 2179, 2180, 2181, 2184], "classif": [13, 1018, 1495, 1501, 1517, 1585, 1587, 1589, 1615, 1672, 2096, 2126, 2128, 2161], "co": [13, 55, 56, 57, 65, 70, 74, 75, 78, 79, 80, 81, 83, 200, 635, 636, 693, 979, 1001, 1018, 1086, 1204, 1207, 1208, 1213, 1273, 1274, 1367, 1515, 1516, 1519, 1866, 1867, 1875, 1881, 1889, 1950, 1954, 1955, 1956, 1957, 1959, 1979, 1980, 1981, 1983, 1984, 2098, 2104, 2119, 2156, 2157, 2170, 2175, 2177, 2189, 2196, 2198], "sin": [13, 15, 55, 56, 57, 64, 65, 70, 74, 75, 78, 79, 80, 81, 531, 669, 670, 910, 1001, 1018, 1086, 1204, 1206, 1207, 1208, 1210, 1213, 1274, 1367, 1417, 1889, 1951, 1957, 2098, 2104, 2119, 2121, 2130, 2156, 2170, 2171, 2175, 2189, 2194, 2196, 2198, 2201, 2203], "dynamicshapecondpred": 13, "dyn_shape_mod": 13, "eagerli": [13, 35, 1013, 1073, 1080, 2067, 2071, 2133, 2190, 2194, 2203], "vari": [13, 55, 1321, 1495, 1614, 1816, 1952, 2018, 2113, 2121, 2126, 2133, 2146, 2147, 2157, 2160, 2161, 2170, 2184, 2188, 2190, 2191, 2193, 2194, 2203, 2204, 2206], "inp2": 13, "assert": [13, 24, 25, 31, 35, 38, 61, 63, 64, 65, 68, 70, 76, 79, 80, 81, 624, 688, 692, 951, 953, 954, 1204, 1206, 1207, 1208, 1209, 1212, 1213, 1214, 1228, 1229, 1313, 1318, 1319, 1325, 1740, 1836, 2048, 2099, 2101, 2104, 2136, 2137, 2144, 2149, 2157, 2158, 2160, 2172, 2177, 2190, 2194, 2202, 2204], "export": [13, 16, 19, 29, 35, 39, 54, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 680, 833, 869, 960, 1003, 1008, 1010, 1018, 1039, 1223, 1233, 1318, 1320, 1321, 1327, 1333, 1817, 1818, 1837, 1838, 2096, 2097, 2099, 2100, 2104, 2136, 2156, 2159, 2165, 2182, 2185, 2190, 2195, 2201, 2202], "deploy": [13, 40, 50, 55, 2096, 2110, 2184, 2195], "dim_batch": 13, "dim": [13, 29, 33, 36, 37, 38, 55, 57, 64, 65, 68, 70, 72, 78, 79, 111, 113, 114, 115, 117, 133, 134, 135, 183, 203, 206, 209, 210, 211, 212, 213, 214, 228, 232, 259, 263, 280, 311, 312, 313, 314, 315, 316, 320, 321, 353, 379, 391, 406, 408, 409, 410, 413, 428, 429, 430, 431, 435, 436, 452, 461, 469, 478, 490, 491, 493, 504, 505, 511, 512, 513, 514, 515, 516, 517, 518, 536, 537, 540, 541, 543, 546, 551, 552, 554, 559, 564, 572, 577, 578, 582, 583, 584, 586, 587, 589, 604, 605, 608, 609, 610, 611, 614, 616, 703, 705, 706, 707, 709, 903, 904, 905, 939, 940, 942, 988, 995, 1016, 1018, 1025, 1036, 1053, 1055, 1124, 1125, 1126, 1127, 1128, 1137, 1143, 1161, 1162, 1164, 1165, 1166, 1167, 1168, 1169, 1170, 1171, 1172, 1173, 1174, 1175, 1176, 1177, 1178, 1179, 1180, 1182, 1184, 1185, 1204, 1214, 1223, 1256, 1269, 1279, 1287, 1288, 1289, 1290, 1292, 1337, 1347, 1348, 1368, 1372, 1382, 1384, 1385, 1395, 1403, 1413, 1415, 1416, 1418, 1421, 1423, 1442, 1473, 1474, 1475, 1476, 1477, 1478, 1486, 1516, 1517, 1518, 1521, 1525, 1527, 1532, 1547, 1553, 1572, 1588, 1589, 1590, 1616, 1618, 1630, 1632, 1633, 1670, 1671, 1687, 1690, 1699, 1707, 1713, 1714, 1715, 1724, 1725, 1727, 1729, 1733, 1740, 1746, 1747, 1755, 1772, 1790, 1791, 1797, 1801, 1803, 1810, 1811, 1824, 1827, 1830, 1841, 1886, 1893, 1896, 1916, 1917, 1922, 1923, 1928, 1929, 1930, 1933, 1934, 1964, 1967, 1968, 1972, 1975, 1978, 1985, 1988, 1990, 1991, 1992, 1993, 1996, 2011, 2015, 2016, 2017, 2018, 2021, 2022, 2030, 2031, 2032, 2033, 2035, 2043, 2044, 2048, 2049, 2097, 2098, 2104, 2120, 2121, 2137, 2138, 2149, 2155, 2156, 2170, 2171, 2173, 2180, 2184, 2190, 2198], "batch": [13, 25, 29, 35, 36, 38, 51, 54, 55, 59, 60, 61, 64, 65, 68, 70, 78, 79, 582, 583, 584, 586, 587, 696, 712, 713, 714, 715, 716, 717, 745, 770, 789, 795, 943, 948, 949, 969, 981, 982, 989, 992, 993, 994, 1001, 1036, 1133, 1135, 1145, 1207, 1208, 1214, 1312, 1345, 1346, 1347, 1348, 1349, 1351, 1352, 1353, 1354, 1355, 1356, 1357, 1358, 1359, 1360, 1361, 1362, 1363, 1364, 1365, 1367, 1368, 1369, 1370, 1371, 1372, 1373, 1374, 1375, 1376, 1377, 1378, 1379, 1380, 1383, 1384, 1385, 1388, 1396, 1405, 1406, 1410, 1494, 1495, 1496, 1497, 1498, 1501, 1509, 1510, 1515, 1517, 1518, 1520, 1521, 1522, 1524, 1525, 1526, 1528, 1533, 1534, 1535, 1536, 1541, 1542, 1544, 1545, 1546, 1547, 1548, 1552, 1553, 1554, 1555, 1556, 1557, 1564, 1565, 1566, 1573, 1574, 1585, 1586, 1587, 1588, 1589, 1591, 1594, 1595, 1596, 1597, 1598, 1600, 1614, 1615, 1622, 1626, 1627, 1628, 1629, 1630, 1631, 1632, 1634, 1653, 1658, 1660, 1661, 1671, 1672, 1674, 1675, 1676, 1679, 1680, 1681, 1682, 1685, 1698, 1699, 1700, 1724, 1732, 1740, 1758, 1759, 1762, 1771, 1772, 1789, 1816, 1817, 1819, 1820, 1822, 1839, 1840, 1842, 1843, 1844, 1845, 1847, 1859, 1860, 1861, 1862, 1867, 1868, 1875, 1880, 1883, 1885, 1895, 1899, 1979, 1980, 1981, 1983, 1984, 1988, 1993, 1997, 1998, 2020, 2023, 2024, 2026, 2045, 2048, 2104, 2119, 2120, 2121, 2126, 2129, 2133, 2137, 2138, 2144, 2146, 2153, 2160, 2165, 2170, 2175, 2176, 2184, 2188, 2190, 2191, 2193, 2194, 2197, 2201, 2204], "min": [13, 24, 29, 36, 43, 50, 55, 68, 115, 184, 185, 186, 187, 299, 705, 706, 707, 766, 768, 780, 781, 790, 816, 836, 838, 839, 840, 845, 904, 996, 997, 1086, 1125, 1159, 1160, 1239, 1276, 1347, 1361, 1362, 1368, 1372, 1374, 1379, 1385, 1405, 1468, 1500, 1540, 1568, 1570, 1590, 1603, 1610, 1662, 1699, 1703, 1733, 1735, 1741, 1759, 1841, 1861, 1866, 1867, 1877, 1883, 1885, 1895, 1997, 1998, 2006, 2024, 2025, 2026, 2027, 2098, 2113, 2119, 2155, 2156, 2160, 2163, 2165, 2184, 2190, 2194, 2198, 2209], "ep": [13, 55, 57, 722, 723, 724, 725, 726, 727, 733, 734, 746, 747, 759, 761, 762, 763, 764, 807, 833, 834, 836, 838, 839, 840, 845, 850, 948, 949, 1325, 1401, 1482, 1496, 1497, 1498, 1516, 1535, 1536, 1544, 1545, 1546, 1554, 1555, 1556, 1557, 1564, 1565, 1566, 1591, 1596, 1597, 1622, 1626, 1628, 1630, 1631, 1658, 1670, 1685, 1689, 1690, 1698, 1702, 1725, 1728, 1732, 1737, 1756, 1771, 1790, 1824, 1840, 1841, 1842, 1843, 1844, 1845, 1847, 1859, 1860, 1863, 1877, 1899, 2098, 2136, 2141, 2171, 2185, 2198, 2209], "dynamic_shap": [13, 55, 70, 72, 78, 79, 1222, 1223, 2152, 2155, 2184, 2200], "graphmodul": [13, 35, 55, 56, 57, 70, 71, 72, 73, 74, 75, 76, 78, 79, 80, 81, 82, 83, 866, 888, 889, 890, 891, 1217, 2160, 2181, 2189, 2192, 2193, 2202, 2203, 2204], "arg0_1": [13, 55, 57], "f32": [13, 55, 57, 70, 71, 72, 73, 74, 75, 76, 78, 79, 80, 81, 82, 83, 2203], "s0": [13, 55, 56, 70, 78, 79, 485, 1219, 1229, 2172, 2190, 2191, 2203, 2204], "sym_siz": [13, 56, 70, 78, 79, 2156, 2198], "sym": [13, 70, 78, 79, 80, 81, 1229, 1949, 1950, 1951, 1952, 1953, 1954, 1955, 1956, 1957, 1958, 1959], "gt": [13, 70, 78, 79, 295, 1270, 2098, 2119, 2141, 2156, 2198], "true_graph_0": [13, 56, 70, 73, 78, 79], "false_graph_0": [13, 56, 70, 73, 78, 79], "symbol": [13, 15, 23, 55, 56, 70, 79, 624, 831, 869, 1003, 1222, 1223, 1225, 1227, 1229, 1231, 1232, 1234, 1235, 1238, 1239, 1243, 1253, 1330, 1384, 1552, 1598, 2023, 2045, 2100, 2104, 2121, 2152, 2188, 2190, 2193, 2194, 2203, 2204], "sub": [13, 29, 37, 43, 59, 67, 70, 73, 78, 79, 561, 828, 890, 891, 1315, 1327, 1331, 1332, 1413, 1416, 1418, 1582, 1594, 1595, 1626, 1627, 1629, 1880, 1916, 1995, 2015, 2032, 2095, 2097, 2098, 2119, 2121, 2130, 2146, 2155, 2156, 2170, 2173, 2191, 2198, 2202, 2203], "datadependentcondpred": 13, "sum_1": [13, 68, 2189, 2191, 2192], "b8": [13, 70, 73, 78], "flatten": [13, 25, 33, 36, 55, 56, 59, 68, 70, 76, 903, 904, 1134, 1278, 1372, 1385, 1634, 1788, 1830, 1838, 1896, 1912, 1917, 1922, 2011, 2031, 2032, 2033, 2034, 2098, 2119, 2120, 2122, 2126, 2128, 2155, 2156, 2162, 2174, 2190], "closur": [13, 34, 70, 78, 79, 1003, 1839, 1840, 1841, 1842, 1843, 1844, 1845, 1846, 1847, 1857, 1859, 1860, 1861, 1862, 1863, 2192, 2195], "flat": [13, 33, 55, 1917, 1959, 2034, 2098, 2139, 2155, 2170], "_higher_order_op": 13, "condition": [13, 50, 945, 1018, 2203], "constraint": [13, 29, 33, 55, 59, 70, 71, 72, 73, 74, 75, 76, 78, 79, 80, 81, 82, 83, 806, 807, 938, 1018, 1077, 1089, 1208, 1222, 1223, 1225, 1228, 1229, 1239, 1740, 1772, 1790, 1816, 1863, 2100, 2137, 2139, 2141, 2144, 2170, 2188, 2190, 2191], "true_branch": [13, 1018], "false_branch": [13, 1018], "consist": [13, 15, 20, 24, 29, 31, 35, 38, 50, 51, 56, 68, 948, 992, 993, 994, 1018, 1237, 1315, 1345, 1351, 1352, 1354, 1356, 1357, 1358, 1359, 1383, 1385, 1415, 1582, 1630, 1792, 1795, 1838, 1880, 1895, 1997, 2097, 2100, 2104, 2107, 2120, 2121, 2136, 2137, 2139, 2140, 2144, 2146, 2148, 2155, 2157, 2159, 2170, 2183, 2189, 2192, 2197, 2204], "possibli": [13, 24, 50, 55, 57, 59, 862, 1018, 1241, 1242, 1315, 1331, 1358, 1363, 1582, 1772, 1880, 2097, 2104, 2121, 2148, 2158, 2193, 2206], "aka": [13, 29, 55, 56, 70, 78, 79, 961, 1018, 1542, 1697, 2121, 2130, 2173, 2190], "add_": [13, 29, 55, 62, 64, 70, 73, 487, 1018, 1203, 2098, 2119, 2131, 2149, 2170, 2188], "parallel_info": [14, 2132], "cppextens": 15, "setuptool": 15, "bare": 15, "pypa": 15, "userguid": 15, "ext_modul": 15, "libtorch_python": 15, "py_limited_api": 15, "libtorch": [15, 16, 2184], "dpy_limited_api": 15, "bdist_wheel": 15, "min_supported_cpython": 15, "enforc": [15, 23, 24, 25, 67, 807, 919, 934, 1315, 1552, 1582, 1598, 1841, 1880, 2100, 2144, 2174], "safeti": [15, 31, 40, 55, 57, 68, 485, 1003, 1004, 1239, 2097, 2100, 2120, 2148, 2195], "saniti": [15, 21, 31, 36, 2137], "practic": [15, 24, 29, 35, 37, 38, 56, 59, 485, 2096, 2097, 2107, 2118, 2120, 2130, 2136, 2139, 2141, 2144, 2149, 2158, 2165, 2172, 2182, 2191, 2193, 2194, 2203], "hexcod": 15, "buildextens": 15, 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2146, 2149, 2152, 2157, 2160, 2180], "comm_hook": 25, "comm_hook_st": 25, "barrier": [25, 40, 50, 2156], "map_loc": [25, 1319, 1323, 1387, 1772, 2095, 2108, 2111, 2158], "new_ddp_model": 25, "load_state_dict": [25, 31, 34, 59, 66, 414, 1315, 1387, 1582, 1762, 1839, 1840, 1841, 1842, 1843, 1844, 1845, 1846, 1847, 1850, 1851, 1859, 1860, 1861, 1862, 1863, 1864, 1865, 1866, 1867, 1868, 1869, 1870, 1871, 1872, 1873, 1874, 1875, 1876, 1877, 1878, 1879, 1880, 1881, 2095, 2144, 2149, 2157, 2160], "n_gpu": 25, "device_count": [25, 33, 2039], "got": [25, 29, 920, 934, 935, 957, 2136, 2149, 2177], "thank": [25, 38, 2136], "author": [25, 50, 59, 1214, 1859, 1902, 2048, 2129, 2184, 2192, 2194, 2196, 2199, 2200, 2201, 2204], "thij": 25, "par": 25, "torch_show_cpp_stacktrac": [26, 29], "torch_cpp_log_level": [26, 29], "c10": [26, 50, 2143, 2184, 2190], "glog": 26, "logger": [26, 29, 40, 812, 866, 2180, 2181], "info": [26, 29, 35, 36, 44, 48, 50, 59, 680, 921, 934, 935, 1039, 1040, 1044, 1229, 1346, 1357, 1359, 1360, 1364, 1377, 1405, 1434, 1435, 1836, 2096, 2098, 2104, 2106, 2135, 2136, 2137, 2155, 2158, 2185, 2190, 2192, 2193, 2203, 2204, 2208], "fatal": [26, 55, 2118, 2146], "torch_log": [26, 35, 36, 70, 72, 79, 680, 1001, 2106, 2135, 2190, 2191, 2192, 2194, 2204], "_log": [26, 35, 36, 2096, 2100, 2184, 2203, 2204, 2207], "home": [27, 2133], "fill_uninitialized_memori": [28, 498, 1146, 1147, 1148, 2036, 2148], "fill": [28, 29, 35, 36, 153, 172, 257, 258, 259, 285, 316, 320, 376, 399, 444, 445, 446, 448, 453, 480, 512, 607, 623, 972, 1133, 1146, 1147, 1148, 1200, 1201, 1346, 1357, 1359, 1525, 1626, 1680, 1727, 1834, 1835, 1904, 1905, 1906, 1907, 1908, 1909, 1997, 2036, 2093, 2094, 2098, 2108, 2120, 2121, 2128, 2133, 2136, 2149, 2156, 2170, 2172, 2177, 2193, 2198], "detriment": [28, 2148], "resize_": [28, 499, 1203, 1337, 2036, 2097, 2098, 2099, 2119, 2148, 2162, 2172, 2198], "empty_strid": [28, 2098, 2102, 2156, 2190, 2198], "empty_permut": [28, 2098, 2156, 2198], "empty_lik": [28, 31, 57, 2098, 2102, 2104, 2108, 2119, 2156, 2170], "brief": [29, 35, 1772, 2118, 2165], "introduct": [29, 38, 1077, 1772, 2097, 2101, 2119, 2131, 2144, 2155, 2165, 2170, 2175], "mpi": [29, 1772], "gloo": [29, 51, 1772, 2135, 2142, 2165], "recv": [29, 35, 1772, 2156, 2166], "broadcast": [29, 31, 34, 36, 37, 38, 59, 64, 96, 195, 397, 399, 400, 401, 512, 514, 516, 565, 695, 696, 697, 698, 699, 700, 701, 707, 745, 913, 969, 974, 977, 981, 982, 983, 984, 1021, 1052, 1086, 1139, 1140, 1145, 1150, 1190, 1191, 1192, 1193, 1256, 1258, 1272, 1282, 1340, 1341, 1348, 1361, 1365, 1370, 1371, 1373, 1376, 1384, 1404, 1409, 1410, 1420, 1467, 1470, 1479, 1482, 1495, 1516, 1535, 1588, 1661, 1670, 1733, 1740, 1772, 1838, 1884, 1892, 1915, 1917, 1994, 1998, 2016, 2021, 2051, 2096, 2100, 2119, 2120, 2126, 2135, 2155, 2156, 2171, 2194, 2198], "all_reduc": [29, 36, 53, 1772, 2156, 2188], "all_gath": [29, 36], "scatter": [29, 33, 34, 36, 37, 59, 512, 514, 516, 1518, 2036, 2098, 2138, 2139, 2156, 2165, 2198], "reduce_scatt": [29, 36, 2156], "all_to_al": [29, 36], "v1": [29, 59, 1384, 1884, 2095, 2135, 2165], "init_method": [29, 1772, 2165], "adher": [29, 1003, 2100, 2121, 2170], "some_fil": 29, "machine_nam": 29, "share_folder_nam": 29, "tcpstore": [29, 50], "past": [29, 55, 68, 85, 1050, 1103, 1108, 1122, 1123, 1444, 1772, 2138, 2196, 2197, 2200, 2203], "ask": [29, 63, 64, 2095, 2096, 2137, 2141, 2182, 2191, 2206], "infiniband": [29, 51, 1772, 2165], "interconnect": [29, 35], "gpudirect": 29, "ethernet": 29, "ip": [29, 50], "ib": 29, "upcom": [29, 2129], "nccl_socket_ifnam": 29, "eth0": 29, "gloo_socket_ifnam": 29, "eth1": 29, "eth2": 29, "eth3": 29, "imper": 29, "nccl_debug": 29, "nccl_debug_subsi": 29, "coll": 29, "hang": [29, 30, 34, 38, 1772, 2109, 2135, 2208], "topologi": [29, 31, 36, 37, 40, 2183], "effort": [29, 55, 2121, 2165, 2191, 2197], "socket": [29, 41, 2118, 2165], "nccl_socket_nthread": 29, "nccl_nsocks_perthread": 29, "cloud": [29, 2170, 2175], "aw": [29, 42, 1026], "gcp": [29, 2200], "primit": [29, 34, 35, 36, 50, 57, 1387, 2097, 2099, 2101, 2132, 2145, 2149, 2155, 2165, 2198], "kind": [29, 44, 55, 68, 70, 71, 72, 73, 74, 75, 76, 78, 79, 80, 81, 82, 83, 680, 1225, 1335, 1773, 1774, 1837, 1894, 1958, 2095, 2104, 2136, 2146, 2148, 2155, 2158, 2160, 2171, 2172, 2173], "connect": [29, 35, 40, 50, 1509, 1510, 1511, 1512, 1513, 1514, 1558, 1559, 1560, 1561, 1562, 1563, 1611, 2118, 2126, 2165, 2201], "advantag": [29, 50, 1495, 1542, 2135, 2138, 2151, 2152, 2165, 2170, 2188, 2203], "redund": [29, 59, 1161, 1162, 1164, 1166, 1180, 1182, 1993, 2152], "elimin": [29, 55, 68, 609, 1222, 2032, 2033, 2148, 2152, 2196, 2202], "thrash": 29, "recurr": [29, 770, 771, 1331, 1501, 1518, 1533, 1534, 1552, 1598, 1793, 1819, 1860, 2133], "device_mesh": [29, 36, 37, 59, 1772], "init_device_mesh": [29, 37], "inconsist": [29, 59, 894, 1911, 2136], "uuid": [29, 2130], "use_distribut": 29, "group_nam": [29, 47], "pg_option": 29, "url": [29, 50, 960, 2095, 2111, 2140, 2165], "encod": [29, 40, 47, 50, 57, 68, 1363, 1387, 1434, 1435, 1494, 1495, 1626, 1627, 1628, 1629, 1630, 1772, 1939, 1979, 1980, 1981, 1983, 1984, 2097, 2100, 2101, 2136, 2149, 2158, 2166, 2170], "ucc": 29, "plugin": [29, 2159, 2175, 2189], "c10d": [29, 40, 49, 51, 54, 680, 1772, 2135, 2156], "registr": [29, 34, 67, 1086, 1764, 1769, 1770, 1772, 1792, 1795, 2104, 2152, 2158, 2189], "lowercas": 29, "deadlock": [29, 1772], "invalid": [29, 43, 48, 1004, 1315, 1582, 1880, 2129, 2130, 2148, 2158, 2185, 2193], "job": [29, 40, 43, 44, 46, 47, 49, 50, 51, 53, 1466, 1772, 1868, 1875, 2133, 2143, 2159, 2175, 2183, 2191, 2203, 2206, 2208], "exchang": [29, 50, 1040, 1075, 1133, 2069, 2133, 2151], "timedelta": [29, 50, 2113], "abort": [29, 2133, 2208], "crash": [29, 43, 50, 1352, 2118, 2130, 2165, 2167, 2175, 2190, 2203], "corrupt": [29, 50, 1518, 2133, 2146, 2149], "torch_nccl_blocking_wait": [29, 2208], "processgroupopt": 29, "processgroupnccl": [29, 2135, 2207], "is_high_priority_stream": 29, "availbl": 29, "deeplearn": 29, "ncclconfig": 29, "ncclcomminit": 29, "lazi": [29, 1019, 1219, 1328, 1555, 1556, 1557, 1558, 1559, 1560, 1561, 1562, 1563, 1564, 1565, 1566, 1567, 1762, 2070, 2190], "ncclcommsplit": 29, "unnecessari": [29, 34, 57, 1781, 1782, 1823, 2100, 2121, 2130, 2133, 2136, 2149, 2158, 2174], "backend_nam": [29, 2039], "custom_backend": 29, "mesh_shap": 29, "mesh_dim_nam": 29, "dimension": [29, 36, 38, 56, 512, 514, 909, 915, 916, 917, 980, 987, 999, 1133, 1135, 1136, 1145, 1161, 1162, 1164, 1165, 1166, 1167, 1168, 1169, 1170, 1171, 1173, 1174, 1175, 1176, 1179, 1180, 1182, 1184, 1186, 1187, 1199, 1269, 1278, 1279, 1280, 1348, 1353, 1354, 1355, 1356, 1381, 1386, 1402, 1410, 1417, 1486, 1503, 1504, 1505, 1506, 1507, 1508, 1517, 1518, 1524, 1525, 1554, 1572, 1589, 1597, 1604, 1605, 1606, 1607, 1608, 1609, 1616, 1618, 1622, 1638, 1639, 1640, 1671, 1724, 1727, 1760, 1761, 1771, 1789, 1829, 1902, 1979, 1980, 1981, 1982, 1983, 1984, 1993, 2011, 2014, 2015, 2021, 2100, 2121, 2128, 2131, 2141, 2147, 2170, 2172, 2173, 2176, 2179, 2190], "layout": [29, 36, 37, 56, 68, 149, 190, 207, 232, 341, 342, 434, 444, 445, 446, 447, 448, 457, 543, 580, 581, 582, 583, 584, 586, 587, 616, 699, 894, 922, 927, 970, 979, 1054, 1146, 1147, 1148, 1158, 1163, 1181, 1197, 1200, 1201, 1227, 1273, 1274, 1335, 1386, 1402, 1410, 1420, 1478, 1705, 1834, 1835, 1863, 1904, 1905, 1906, 1907, 1908, 1909, 1910, 1911, 1949, 1950, 1951, 1952, 1953, 1954, 1955, 1956, 1957, 1958, 1959, 1973, 1974, 1976, 1977, 1978, 1979, 1980, 1981, 1982, 1983, 1984, 2020, 2025, 2027, 2093, 2094, 2095, 2097, 2098, 2099, 2102, 2126, 2133, 2156, 2158, 2170, 2175, 2176, 2177, 2198], "spmd": [29, 31, 36, 2183], "nd": [29, 1371, 2139, 2147], "scene": [29, 2149, 2175], "mesh": [29, 33, 36, 37, 2175], "mesh_1d": 29, "mesh_2d": 29, "dp": 29, "is_initi": 29, "is_mpi_avail": 29, "is_nccl_avail": 29, "is_gloo_avail": 29, "distributed_c10d": [29, 50], "is_xccl_avail": 29, "xccl": 29, "is_torchelastic_launch": 29, "elast": [29, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 52, 53, 54, 2096], "torchelast": [29, 32, 40, 42, 43, 44, 47, 49, 50, 51, 53, 54], "torchelastic_run_id": [29, 51], "proxi": [29, 31, 57, 1003, 1215, 1216, 1219, 2160, 2165, 2191], "rendezv": [29, 32, 40, 44, 47, 49, 54, 2135, 2165], "null": [29, 42, 44, 47, 70, 74], "discoveri": [29, 50, 2133, 2158], "reachabl": 29, "multicast": 29, "20": [29, 34, 38, 320, 695, 730, 738, 739, 744, 748, 749, 750, 751, 752, 753, 756, 766, 770, 771, 772, 773, 774, 775, 782, 1127, 1269, 1313, 1316, 1327, 1338, 1372, 1467, 1490, 1492, 1493, 1495, 1496, 1497, 1498, 1499, 1501, 1508, 1509, 1510, 1511, 1513, 1514, 1519, 1520, 1521, 1522, 1526, 1529, 1530, 1533, 1534, 1536, 1543, 1544, 1545, 1546, 1549, 1550, 1551, 1552, 1553, 1554, 1569, 1575, 1576, 1577, 1579, 1580, 1582, 1598, 1600, 1611, 1619, 1622, 1625, 1626, 1627, 1628, 1640, 1663, 1665, 1666, 1668, 1672, 1683, 1684, 1690, 1748, 1789, 1790, 1791, 1815, 1824, 1827, 1846, 1867, 1881, 1898, 2011, 2097, 2098, 2108, 2121, 2126, 2135, 2136, 2147, 2152, 2156, 2157, 2159, 2165, 2169, 2170, 2191, 2192, 2193], "23456": 29, "clean": [29, 35, 40, 55, 68, 1012, 1081, 2095, 2118, 2158], "fcntl": 29, "nf": 29, "init": [29, 37, 40, 43, 44, 55, 59, 1315, 1582, 1590, 1610, 1823, 1880, 2096, 2102, 2130, 2136, 2143, 2144, 2157, 2190], "brand": [29, 31, 2136], "succe": [29, 50, 55, 64, 2133, 2136, 2149, 2150, 2203, 2204, 2206], "unexpect": [29, 31, 39, 68, 894, 1036, 1196, 1199, 1202, 1315, 1582, 1825, 1838, 1880, 1988, 2097, 2130, 2136, 2147, 2191, 2193, 2194], "unsuccess": 29, "mnt": 29, "sharedfil": 29, "port": [29, 40, 49, 50, 51, 2108, 2142], "backend_str": 29, "uppercas": 29, "classmethod": [29, 31, 50, 70, 72, 742, 748, 749, 750, 755, 756, 766, 774, 804, 805, 806, 829, 830, 831, 834, 842, 883, 957, 1039, 1524, 1525, 1622, 1797, 1798, 1799, 1800, 1801, 1802, 1803, 1804, 1836, 2100, 2101, 2126, 2136, 2160, 2165, 2172, 2205], "register_backend": [29, 2165, 2189], "extended_api": 29, "instanti": [29, 34, 49, 50, 51, 59, 68, 870, 1525, 1680, 1792, 1816, 1823, 2095, 2097, 2099, 2100, 2104, 2121, 2133, 2136, 2144, 2149, 2152, 2172], "3rd": [29, 38, 50, 616, 2131], "processgroup": [29, 31, 34, 35, 36, 59, 1772], "four": [29, 1361, 1510, 2126, 2136, 2141, 2165, 2167], "distributedbackendopt": 29, "get_backend": [29, 50], "get_rank": [29, 59, 1622], "consecut": [29, 59, 609, 1818, 1864, 2021, 2032, 2033, 2120, 2155], "get_world_s": 29, "pattern": [29, 31, 35, 804, 805, 806, 808, 883, 890, 1086, 1518, 1642, 1819, 1974, 2097, 2100, 2121, 2126, 2130, 2133, 2136, 2138, 2142, 2149, 2161, 2162, 2170, 2188, 2190, 2191, 2193, 2202, 2206], "launcher": [29, 35], "pg": [29, 1772], "destructor": [29, 2118, 2167], "ncclcommabort": 29, "gc": [29, 485, 2167], "fault": [29, 32, 40, 49, 50, 1199, 2203], "toler": [29, 32, 40, 49, 50, 68, 704, 948, 949, 1304, 1331, 1332, 1370, 1373, 1388, 1838, 1846, 2097, 2177], "_after_": [29, 2135], "unsupport": [29, 40, 55, 57, 64, 70, 72, 77, 79, 84, 1014, 2097, 2101, 2120, 2121, 2149, 2157, 2165, 2170, 2194, 2199, 2203], "untest": [29, 2120], "grain": [29, 55, 887, 890, 1740, 2130, 2155, 2170, 2182, 2196, 2203, 2204], "plai": [29, 2192, 2194], "new_group": [29, 59, 1622], "opaqu": [29, 39, 1003, 1037, 1077, 1078, 1196, 2104, 2199], "use_local_synchron": 29, "group_desc": 29, "enqueu": [29, 67, 1040, 1044, 1045, 1446, 2055, 2133, 2166], "groupmemb": 29, "non_group_memb": 29, "significantli": [29, 55, 59, 938, 1772, 1839, 1840, 1842, 1843, 1844, 1845, 1847, 1859, 1860, 1861, 1862, 1939, 2152, 2153, 2170, 2189, 2203], "taken": [29, 35, 38, 55, 57, 67, 68, 70, 75, 894, 1166, 1168, 1191, 1192, 1501, 1517, 1525, 1589, 1597, 1672, 1680, 1697, 1720, 1771, 1943, 1976, 2121, 2128, 2130, 2132, 2133, 2136, 2138, 2139, 2143, 2149, 2155, 2158, 2159, 2204, 2206], "get_group_rank": 29, "global_rank": [29, 40], "translat": [29, 1269, 2130, 2151, 2152, 2167], "get_global_rank": 29, "group_rank": [29, 36, 37, 40, 51], "get_process_group_rank": 29, "inter": [29, 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2073, 2074, 2075, 2076, 2077, 2078, 2079, 2080, 2081, 2082, 2083, 2084, 2085, 2086, 2087, 2088, 2089, 2090, 2091, 2092, 2211], "experiment": [1, 36, 69, 1215, 1216, 1217, 1218, 1219, 1236, 1237, 1238, 1239, 1240, 1241, 1242, 1243, 1244, 1245, 1246, 1247, 1248, 1249, 1250, 1251, 1252, 1253, 1254, 1255, 2105, 2125], "cpu": [1, 3, 11, 17, 205, 1029, 1030, 1031, 1032, 1033, 1034, 1035, 2130, 2132, 2146, 2160, 2187], "bfloat16": [1, 154], "differenti": [2, 2130], "autograd": [2, 11, 12, 16, 64, 918, 919, 920, 921, 922, 925, 926, 927, 928, 930, 931, 932, 933, 936, 937, 938, 939, 940, 941, 942, 943, 947, 948, 949, 950, 951, 952, 953, 954, 955, 958, 959, 960, 961, 962, 963, 2120, 2129, 2130, 2136, 2137, 2155, 2165, 2166], "forward": [2, 919, 2136, 2166], "mode": [2, 51, 62, 413, 1421, 2130, 2136, 2141, 2155, 2160, 2166], "function": [2, 29, 36, 38, 57, 58, 60, 61, 63, 65, 68, 918, 919, 920, 921, 930, 931, 932, 933, 936, 937, 938, 939, 940, 941, 942, 1203, 1647, 1648, 1649, 1650, 1651, 1652, 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2186, 2190, 2191, 2193, 2194, 2195, 2206], "local": [2, 2100, 2130, 2179, 2200, 2202], "disabl": [2, 24, 1007, 2097, 2130, 2179, 2194, 2195, 2203], "comput": [2, 65, 2130, 2147, 2166, 2179, 2194], "default": [2, 24, 25, 2099, 2130, 2133, 2141, 2162, 2191], "layout": [2, 2121, 2173], "manual": [2, 35], "In": [2, 9, 2130, 2131, 2179], "place": [2, 64, 2119, 2130, 2131, 2155, 2179], "oper": [2, 11, 20, 25, 29, 55, 57, 64, 84, 2099, 2100, 2105, 2107, 2119, 2120, 2121, 2130, 2134, 2136, 2143, 2150, 2155, 2156, 2160, 2170, 2176, 2179, 2193, 2201, 2203], "tensor": [2, 12, 16, 36, 37, 55, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 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1809, 1810, 1811, 1812, 1813, 1814, 1815, 1817, 1818, 1819, 1820, 1821, 1822, 1823, 1824, 1825, 1826, 1827, 2037, 2038, 2039, 2040, 2041, 2103, 2110, 2111, 2112, 2122, 2123, 2126, 2149, 2157, 2163, 2175, 2179, 2181, 2202, 2210], "numer": [2, 2141, 2147, 2161, 2163], "profil": [2, 29, 958, 959, 960, 961, 962, 963, 1431, 1432, 1433, 1434, 1435, 1436, 2114, 2143, 2144, 2159, 2197, 2201, 2204], "debug": [2, 25, 26, 29, 36, 53, 68, 2097, 2160, 2161, 2184, 2189, 2194, 2203, 2204], "anomali": 2, "detect": 2, "graph": [2, 18, 55, 56, 68, 950, 951, 952, 953, 954, 955, 1077, 2097, 2130, 2133, 2160, 2188, 2191, 2194, 2201, 2202, 2203, 2204], "backend": [3, 29, 50, 51, 2097, 2142, 2145, 2154, 2160, 2162, 2165, 2182, 2187, 2189, 2204], "cudnn": 3, "cusparselt": 3, "mha": 3, "mp": [3, 11, 1424, 1425, 1426, 1427, 1429, 1430, 1431, 1432, 1433, 1434, 1435, 1436, 1437, 1438, 1439, 1440, 1441, 2114, 2115, 2145], "mkl": 3, "mkldnn": [3, 11], "nnpack": 3, "openmp": 3, "opt_einsum": 3, "xeon": 3, "benchmark": [4, 2148, 2197], "bottleneck": 5, "checkpoint": [6, 25, 31], "pytorch": [7, 8, 9, 10, 11, 16, 27, 29, 33, 36, 55, 57, 64, 2096, 2097, 2098, 2102, 2130, 2133, 2134, 2136, 2148, 2149, 2155, 2160, 2163, 2182, 2188, 2199, 2200, 2204, 2208], "govern": [7, 10, 11], "build": [7, 8, 11, 35, 2132, 2143, 2144, 2150], "ci": [7, 11], "how": [7, 25, 35, 62, 2095, 2130, 2136, 2157, 2158, 2192, 2193, 2194, 2200], "add": [7, 10, 96, 695], "new": [7, 8, 10, 2104, 2136], "maintain": [7, 10, 11], "contribut": [8, 2121, 2151], "guid": 8, "process": [8, 10, 24, 48, 2129], "get": [8, 32, 2140, 2149, 2155, 2182, 2194, 2196], "start": [8, 32, 48, 1435, 2140, 2182, 2196, 2204], "propos": 8, "featur": [8, 36, 2143, 2144, 2194], "report": [8, 2138, 2203], "issu": [8, 2097, 2201, 2203], "implement": [8, 35, 40, 50, 53, 57, 2095, 2135, 2141, 2155, 2161, 2163, 2167, 2191, 2193], "fix": [8, 51, 62, 260, 1183], "bug": 8, "ad": [8, 2136, 2155, 2191], "tutori": [8, 11, 2158, 2165], "improv": [8, 2144], "document": [8, 32, 2096], "particip": 8, "onlin": 8, "discuss": 8, "submit": 8, "pull": 8, "request": 8, "open": 8, "review": 8, "code": [8, 68, 2097, 2133, 2140, 2158, 2185, 2194, 2203], "readabl": 8, "test": [8, 2100, 2104, 2136, 2158, 2177, 2200], "case": [8, 2097, 2172], "make": [8, 10, 2188, 2191], "codebas": 8, "more": [8, 55, 60, 2165, 2182], "robust": 8, "triag": 8, "about": [8, 2130, 2165, 2193], "sourc": [8, 2140, 2148, 2150, 2158], "develop": [8, 55, 2096, 2151, 2182], "common": [8, 29, 68, 2143, 2160], "mistak": 8, "To": 8, "avoid": [8, 2146, 2148, 2155, 2158], "frequent": [8, 2097, 2138, 2155, 2160, 2194], "ask": [8, 2097, 2138, 2155, 2160, 2194], "question": [8, 2097, 2138, 2155, 2160, 2194], "On": [8, 25, 2099], "python": [8, 9, 29, 55, 64, 71, 72, 73, 74, 75, 76, 77, 2096, 2097, 2098, 2099, 2100, 2101, 2102, 2104, 2136, 2148, 2155, 2184, 2191], "doc": [8, 11, 2121, 2142], "c": [8, 11, 16, 2104, 2130, 2142, 2149, 2155, 2184], "overview": [8, 19, 20, 55, 68, 2151, 2152, 2158, 2159, 2170, 2189, 2192], "design": [9, 2100, 2135, 2165, 2166, 2167], "philosophi": 9, "principl": [9, 10], "1": [9, 35, 51, 62, 2097, 2137, 2191], "usabl": 9, "perform": [9, 11, 2144, 2149, 2193, 2200, 2201, 2204], "2": [9, 35, 62, 2097, 2133, 2137, 2138, 2160, 2163, 2199, 2200, 2204], "simpl": [9, 2099, 2100, 2144, 2152, 2166, 2203], "easi": 9, "3": [9, 51, 62], "first": [9, 2158], "best": [9, 2133, 2146, 2160, 2187], "class": [9, 36, 43, 68, 2097, 2099, 2100, 2102, 2155, 2157, 2158, 2176], "languag": [9, 2096, 2097, 2099, 2100, 2101], "interoper": 9, "mechan": [10, 2121, 2127, 2130, 2141], "summari": [10, 2160, 2203], "modul": [10, 11, 57, 61, 63, 68, 1582, 1763, 1764, 1765, 1766, 1767, 1768, 1769, 1770, 2097, 2098, 2099, 2100, 2102, 2122, 2130, 2136, 2144, 2149, 2158, 2160, 2199], "core": [10, 11, 2198], "lead": [10, 11], "bdfl": [10, 11], "nomin": [10, 2100], "confirm": 10, "remov": [10, 1813, 2119], "The": [10, 68, 2100, 2130, 2190], "re": [10, 2158], "scope": 10, "project": 10, "decis": 10, "uncontroversi": 10, "chang": [10, 51, 62, 2140, 2194, 2203], "controversi": 10, "gener": [10, 18, 30, 64, 68, 86, 2131, 2137, 2148, 2160, 2161, 2179, 2191, 2192, 2203, 2206, 2211], "polici": [10, 2190], "faq": [10, 2150], "respons": 11, "nn": [11, 61, 63, 1642, 1644, 1645, 1646, 1647, 1648, 1649, 1650, 1651, 1652, 1653, 1654, 1655, 1656, 1657, 1658, 1659, 1660, 1661, 1662, 1663, 1664, 1665, 1666, 1667, 1668, 1669, 1670, 1671, 1672, 1673, 1674, 1675, 1676, 1677, 1678, 1679, 1680, 1681, 1682, 1683, 1684, 1685, 1686, 1687, 1688, 1689, 1690, 1691, 1692, 1693, 1694, 1695, 1696, 1697, 1698, 1699, 1700, 1701, 1702, 1703, 1704, 1705, 1706, 1707, 1708, 1709, 1710, 1711, 1712, 1713, 1714, 1715, 1716, 1717, 1718, 1719, 1720, 1721, 1722, 1723, 1724, 1725, 1726, 1727, 1728, 1729, 1730, 1731, 1732, 1733, 1734, 1735, 1736, 1737, 1738, 1739, 1740, 1741, 1742, 1743, 1744, 1745, 1746, 1747, 1748, 1749, 1750, 1751, 1752, 1753, 1754, 1755, 1756, 1757, 1758, 1759, 1760, 1761, 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"pytorch-cudagraph-integration"]], "Make Graphed Callables": [[2188, "make-graphed-callables"]], "TorchDynamo Previous CUDA Graphs Integration": [[2188, "torchdynamo-previous-cuda-graphs-integration"]], "CUDAGraph Trees Integration": [[2188, "cudagraph-trees-integration"]], "Input Mutation Support": [[2188, "input-mutation-support"]], "Dynamic Shape Support": [[2188, "dynamic-shape-support"]], "NCCL Support": [[2188, "nccl-support"]], "Reasons for Skipping CUDAGraph": [[2188, "reasons-for-skipping-cudagraph"]], "Limitations": [[2188, "limitations"], [2195, "limitations"], [2155, "limitations"]], "Comparisons": [[2188, "comparisons"], [2100, "comparisons"]], "Type Info": [[2209, "type-info"]], "torch.finfo": [[2209, "torch-finfo"]], "torch.iinfo": [[2209, "torch-iinfo"]], "AOTInductor: Ahead-Of-Time Compilation for Torch.Export-ed Models": [[2184, "aotinductor-ahead-of-time-compilation-for-torch-export-ed-models"]], "Model Compilation": [[2184, "model-compilation"]], "Inference in Python": [[2184, "inference-in-python"]], "Inference in C++": [[2184, "inference-in-c"]], "Troubleshooting": [[2184, "troubleshooting"], [2121, "troubleshooting"]], "Debugging Tools": [[2184, null], [36, "debugging-tools"]], "AOTInductor Minifier": [[2185, "aotinductor-minifier"]], "Example Code": [[2185, "example-code"]], "Minifier Launcher": [[2185, "minifier-launcher"]], "Minified Result": [[2185, "minified-result"]], "Profiling to understand torch.compile performance": [[2201, "profiling-to-understand-torch-compile-performance"]], "What to use torch.profiler for:": [[2201, "what-to-use-torch-profiler-for"]], "Basics of using torch.profiler and viewing traces": [[2201, "basics-of-using-torch-profiler-and-viewing-traces"]], "Working around CUDA Graph profiling issues": [[2201, "working-around-cuda-graph-profiling-issues"]], "Understanding compilation time": [[2201, "understanding-compilation-time"]], "Finding graph breaks: \u201cTorch-Compiled Region\u201d and \u201cCompiledFunction\u201d": [[2201, "finding-graph-breaks-torch-compiled-region-and-compiledfunction"]], "Operator Kernels": [[2201, "operator-kernels"]], "Launch overhead": [[2201, "launch-overhead"]], "Custom Backends": [[2189, "custom-backends"]], "Overview": [[2189, "overview"], [68, "module-torch.fx"], [55, "overview"], [2151, "overview"], [2159, "module-torch.profiler"], [2152, "overview"], [20, "module-torch.cuda.tunable"], [19, "module-torch.cuda._sanitizer"]], "Registering Custom Backends": [[2189, "registering-custom-backends"]], "Custom Backends after AOTAutograd": [[2189, "custom-backends-after-aotautograd"]], "Examples": [[2189, "examples"], [2140, "examples"], [13, "examples"], [45, "examples"]], "Debugging Backend": [[2189, "debugging-backend"]], "Speedy Backend": [[2189, "speedy-backend"]], "Composable Backends": [[2189, "composable-backends"]], "PyTorch 2.0 Troubleshooting (old)": [[2204, "pytorch-2-0-troubleshooting-old"]], "Title": [[2204, "id1"]], "Diagnosing Runtime Errors": [[2204, "diagnosing-runtime-errors"]], "Torchdynamo Errors": [[2204, "torchdynamo-errors"]], "Diagnosing TorchInductor Errors": [[2204, "diagnosing-torchinductor-errors"]], "Minifying TorchInductor Errors": [[2204, "minifying-torchinductor-errors"]], "Minifying Backend Compiler Errors": [[2204, "minifying-backend-compiler-errors"]], "Performance Profiling": [[2204, "performance-profiling"]], "Accessing TorchDynamo Profiler": [[2204, "accessing-torchdynamo-profiler"]], "TorchInductor Debugging using TORCH_COMPILE_DEBUG": [[2204, "torchinductor-debugging-using-torch-compile-debug"]], "Graph Breaks": [[2204, "graph-breaks"], [2194, "graph-breaks"], [55, "graph-breaks"]], "Identifying the Cause of a Graph Break": [[2204, "identifying-the-cause-of-a-graph-break"]], "Excessive Recompilation": [[2204, "excessive-recompilation"]], "Accuracy Debugging": [[2204, "accuracy-debugging"]], "Extended Debugging": [[2204, "extended-debugging"]], "Cold Start Timing and Cache Corruption Debugging": [[2204, "cold-start-timing-and-cache-corruption-debugging"]], "Frequently Asked Questions": [[2194, "frequently-asked-questions"], [2097, "frequently-asked-questions"], [2155, "frequently-asked-questions"], [2160, "frequently-asked-questions"], [2138, "frequently-asked-questions"], [8, "frequently-asked-questions"]], "Does torch.compile support training?": [[2194, "does-torch-compile-support-training"]], "Do you support Distributed code?": [[2194, "do-you-support-distributed-code"]], "Do I still need to export whole graphs?": [[2194, "do-i-still-need-to-export-whole-graphs"]], "Why is my code crashing?": [[2194, "why-is-my-code-crashing"]], "Why is compilation slow?": [[2194, "why-is-compilation-slow"]], "Why are you recompiling in production?": [[2194, "why-are-you-recompiling-in-production"]], "How are you speeding up my code?": [[2194, "how-are-you-speeding-up-my-code"]], "Why am I not seeing speedups?": [[2194, "why-am-i-not-seeing-speedups"]], "Identifying the cause of a graph break": [[2194, "identifying-the-cause-of-a-graph-break"]], "Why didn\u2019t my code recompile when I changed it?": [[2194, "why-didnt-my-code-recompile-when-i-changed-it"]], "Why am I getting incorrect results?": [[2194, "why-am-i-getting-incorrect-results"]], "Why am I getting OOMs?": [[2194, "why-am-i-getting-ooms"]], "Does torch.func work with torch.compile (for grad and vmap transforms)?": [[2194, "does-torch-func-work-with-torch-compile-for-grad-and-vmap-transforms"]], "Calling torch.func transform inside of a function handled with torch.compile": [[2194, "calling-torch-func-transform-inside-of-a-function-handled-with-torch-compile"]], "Compiling torch.func.grad with torch.compile": [[2194, "compiling-torch-func-grad-with-torch-compile"]], "Compiling torch.vmap with torch.compile": [[2194, "compiling-torch-vmap-with-torch-compile"]], "Compiling functions besides the ones which are supported (escape hatch)": [[2194, "compiling-functions-besides-the-ones-which-are-supported-escape-hatch"]], "Does NumPy work with torch.compile?": [[2194, "does-numpy-work-with-torch-compile"]], "Which NumPy features does torch.compile support?": [[2194, "which-numpy-features-does-torch-compile-support"]], "Can I compile NumPy code using torch.compile?": [[2194, "can-i-compile-numpy-code-using-torch-compile"]], "Can I execute NumPy code on CUDA and compute gradients via torch.compile?": [[2194, "can-i-execute-numpy-code-on-cuda-and-compute-gradients-via-torch-compile"]], "How do I debug NumPy code under torch.compile?": [[2194, "how-do-i-debug-numpy-code-under-torch-compile"]], "I torch.compile some NumPy code and I did not see any speed-up.": [[2194, "i-torch-compile-some-numpy-code-and-i-did-not-see-any-speed-up"]], "Which API to use for fine grain tracing?": [[2194, "which-api-to-use-for-fine-grain-tracing"]], "How do I graph break on a function?": [[2194, "how-do-i-graph-break-on-a-function"]], "What\u2019s the difference between torch._dynamo.disable and torch._dynamo.disallow_in_graph": [[2194, "what-s-the-difference-between-torch-dynamo-disable-and-torch-dynamo-disallow-in-graph"]], "What\u2019s the difference between torch._dynamo.disable and torch._dynamo_skip": [[2194, "what-s-the-difference-between-torch-dynamo-disable-and-torch-dynamo-skip"]], "torch.compiler API reference": [[2186, "torch-compiler-api-reference"]], "Fake tensor": [[2193, "fake-tensor"]], "Motivation": [[2193, "motivation"], [2190, "motivation"], [2107, "motivation"]], "Related work": [[2193, "related-work"]], "Overall architecture": [[2193, "overall-architecture"], [2190, "overall-architecture"]], "API: the important bits": [[2193, "api-the-important-bits"]], "Details": [[2193, "details"], [2126, "details"]], "About the tensor subclass": [[2193, "about-the-tensor-subclass"]], "How is each individual operator implemented?": [[2193, "how-is-each-individual-operator-implemented"]], "How does the converter work?": [[2193, "how-does-the-converter-work"]], "Performance characteristics": [[2193, "performance-characteristics"]], "Fake tensor of fake tensor?": [[2193, "fake-tensor-of-fake-tensor"]], "Interaction with dynamic shapes": [[2193, "interaction-with-dynamic-shapes"]], "Other resources": [[2193, "other-resources"]], "Torch Environment Variables": [[2207, "torch-environment-variables"]], "torch.compile Troubleshooting": [[2203, "torch-compile-troubleshooting"]], "Setting Expectations": [[2203, "setting-expectations"]], "Compile times": [[2203, "compile-times"]], "Terminology": [[2203, "terminology"], [2100, "terminology"]], "Graph break": [[2203, "graph-break"]], "Guards": [[2203, "guards"]], "Recompilation": [[2203, "recompilation"]], "Dynamic Shapes": [[2203, "dynamic-shapes"]], "Logging Tools": [[2203, "logging-tools"]], "tlparse / TORCH_TRACE": [[2203, "tlparse-torch-trace"]], "TORCH_LOGS": [[2203, "torch-logs"]], "tlparse vs. TORCH_LOGS": [[2203, "tlparse-vs-torch-logs"]], "Simple Workarounds": [[2203, "simple-workarounds"]], "Where to apply torch.compile?": [[2203, "where-to-apply-torch-compile"]], "Disabling and Suppressing Errors": [[2203, "disabling-and-suppressing-errors"]], "Resolving graph breaks": [[2203, "resolving-graph-breaks"]], "Data-dependent operations": [[2203, "data-dependent-operations"]], "Custom ops": [[2203, "custom-ops"]], "Printing": [[2203, "printing"]], "Incorrect code": [[2203, "incorrect-code"]], "Dealing with recompilations": [[2203, "dealing-with-recompilations"]], "Is dynamic shapes enabled?": [[2203, "is-dynamic-shapes-enabled"]], "Changing the cache size limit": [[2203, "changing-the-cache-size-limit"]], "Wrapping constants with tensors": [[2203, "wrapping-constants-with-tensors"]], "Reporting Issues": [[2203, "reporting-issues"], [8, "reporting-issues"]], "Ablation": [[2203, "ablation"]], "Bisecting": [[2203, "bisecting"]], "Creating a reproducer": [[2203, "creating-a-reproducer"]], "Minifier": [[2203, "minifier"]], "Debugging Deeper": [[2203, "debugging-deeper"]], "TorchDynamo": [[2203, "torchdynamo"], [11, "torchdynamo"]], "Logging what Dynamo is tracing": [[2203, "logging-what-dynamo-is-tracing"]], "Breakpointing Dynamo tracing": [[2203, "breakpointing-dynamo-tracing"]], "Bytecode generation errors": [[2203, "bytecode-generation-errors"]], "AOTAutograd": [[2203, "aotautograd"]], "Summary of TORCH_LOGS options": [[2203, "summary-of-torch-logs-options"]], "Related Articles": [[2203, "related-articles"]], "Dynamo Overview": [[2192, "dynamo-overview"]], "Dynamo Internals": [[2192, "dynamo-internals"]], "What is a guard?": [[2192, "what-is-a-guard"]], "What is Dynamo doing?": [[2192, "what-is-dynamo-doing"]], "How to inspect artifacts generated by Dynamo?": [[2192, "how-to-inspect-artifacts-generated-by-dynamo"]], "Best Practices for Backends": [[2187, "best-practices-for-backends"]], "x86 CPU": [[2187, "x86-cpu"], [11, "x86-cpu"]], "torch.overrides": [[2205, "module-torch.overrides"]], "Functions": [[2205, "functions"], [2099, "functions"], [2097, "functions"], [2155, "functions"], [2171, "functions"]], "PyTorch 2.0 NNModule Support": [[2199, "pytorch-2-0-nnmodule-support"]], "NNModule Hooks Support": [[2199, "nnmodule-hooks-support"]], "nn.Module.__call__ Hooks Usage and limitations": [[2199, "nn-module-call-hooks-usage-and-limitations"]], "state_dict Hooks": [[2199, "state-dict-hooks"]], "PyTorch 2.0 Performance Dashboard": [[2200, "pytorch-2-0-performance-dashboard"]], "How to read the dashboard?": [[2200, "how-to-read-the-dashboard"]], "What is measured on the dashboard?": [[2200, "what-is-measured-on-the-dashboard"]], "Can I check if my PR affects TorchInductor\u2019s performance on the dashboard before merging?": [[2200, "can-i-check-if-my-pr-affects-torchinductor-s-performance-on-the-dashboard-before-merging"]], "How can I run any performance test locally?": [[2200, "how-can-i-run-any-performance-test-locally"]], "TorchDynamo APIs for fine-grained tracing": [[2195, "torchdynamo-apis-for-fine-grained-tracing"]], "TorchDynamo APIs to control fine-grained tracing": [[2195, "id1"]], "torch.compiler.disable": [[2195, "torch-compiler-disable"], [1007, "torch-compiler-disable"]], "torch._dynamo.disallow_in_graph": [[2195, "torch-dynamo-disallow-in-graph"]], "torch.compiler.allow_in_graph": [[2195, "torch-compiler-allow-in-graph"], [1003, "torch-compiler-allow-in-graph"]], "TorchInductor GPU Profiling": [[2197, "torchinductor-gpu-profiling"]], "Relevant Environment Variables": [[2197, "relevant-environment-variables"]], "Breakdown Model GPU Time": [[2197, "breakdown-model-gpu-time"]], "Benchmark Individual Triton Kernel": [[2197, "benchmark-individual-triton-kernel"]], "Dynamic shapes": [[2190, "dynamic-shapes"]], "Abridged public API": [[2190, "abridged-public-api"]], "The Guard Model": [[2190, "the-guard-model"]], "Abridged internal API": [[2190, "abridged-internal-api"]], "DimDynamic policy": [[2190, "dimdynamic-policy"]], "Unbacked SymInts": [[2190, "unbacked-symints"]], "Understanding CUDA Memory Usage": [[2206, "understanding-cuda-memory-usage"]], "Generating a Snapshot": [[2206, "generating-a-snapshot"]], "Using the visualizer": [[2206, "using-the-visualizer"]], "Active Memory Timeline": [[2206, "active-memory-timeline"]], "Allocator State History": [[2206, "allocator-state-history"]], "Snapshot API Reference": [[2206, "snapshot-api-reference"]], "torch.xpu": [[2211, "module-torch.xpu"]], "Random Number Generator": [[2211, "random-number-generator"], [18, "random-number-generator"]], "Streams and events": [[2211, "streams-and-events"], [2116, "streams-and-events"], [17, "streams-and-events"], [18, "streams-and-events"]], "Memory management": [[2211, "memory-management"], [2142, "memory-management"], [2133, "memory-management"], [18, "memory-management"]], "Dynamo Deep-Dive": [[2191, "dynamo-deep-dive"]], "A Gentle Introduction to Dynamo": [[2191, "a-gentle-introduction-to-dynamo"]], "PEP 523: Adding a frame evaluation API to CPython": [[2191, "pep-523-adding-a-frame-evaluation-api-to-cpython"]], "Implementing CPython in Python": [[2191, "implementing-cpython-in-python"]], "Generating the Output Graph": [[2191, "generating-the-output-graph"]], "Making Dynamo Sound: Guards": [[2191, "making-dynamo-sound-guards"]], "Symbolic Shapes": [[2191, "symbolic-shapes"]], "Static by default": [[2191, "static-by-default"]], "0, 1 are always specialized": [[2191, "are-always-specialized"]], "Duck shaping": [[2191, "duck-shaping"]], "Guards on symbolic ints": [[2191, "guards-on-symbolic-ints"]], "Making Dynamo Complete: Graph Breaks": [[2191, "making-dynamo-complete-graph-breaks"]], "Conclusion": [[2191, "conclusion"]], "Footnotes": [[2191, "footnotes"]], "IRs": [[2198, "irs"]], "Core Aten IR": [[2198, "core-aten-ir"]], "Prims IR": [[2198, "prims-ir"]], "torch.utils": [[2210, "module-torch.utils"]], "PYTORCH ProcessGroupNCCL Environment Variables": [[2208, "pytorch-processgroupnccl-environment-variables"]], "torch.nn.functional.upsample": [[1759, "torch-nn-functional-upsample"]], "torch.nn.modules.module.register_module_backward_hook": [[1763, "torch-nn-modules-module-register-module-backward-hook"]], "torch.nn.functional.pixel_shuffle": [[1730, "torch-nn-functional-pixel-shuffle"]], "torch.nn.functional.tanh": [[1751, "torch-nn-functional-tanh"]], "torch.nn.functional.max_unpool1d": [[1716, "torch-nn-functional-max-unpool1d"]], "torch.nn.functional.softshrink": [[1749, "torch-nn-functional-softshrink"]], "torch.nn.functional.multilabel_soft_margin_loss": [[1723, "torch-nn-functional-multilabel-soft-margin-loss"]], "torch.nn.functional.upsample_nearest": [[1761, "torch-nn-functional-upsample-nearest"]], "torch.nn.functional.one_hot": [[1726, "torch-nn-functional-one-hot"]], "torch.nn.modules.module.register_module_forward_pre_hook": [[1766, "torch-nn-modules-module-register-module-forward-pre-hook"]], "torch.nn.modules.module.register_module_full_backward_hook": [[1767, "torch-nn-modules-module-register-module-full-backward-hook"]], "torch.nn.functional.pixel_unshuffle": [[1731, "torch-nn-functional-pixel-unshuffle"]], "torch.nn.functional.normalize": [[1725, "torch-nn-functional-normalize"]], "torch.nn.functional.prelu": [[1733, "torch-nn-functional-prelu"]], "torch.nn.functional.max_unpool3d": [[1718, "torch-nn-functional-max-unpool3d"]], "torch.nn.functional.multilabel_margin_loss": [[1722, "torch-nn-functional-multilabel-margin-loss"]], "torch.nn.functional.rrelu": [[1738, "torch-nn-functional-rrelu"]], "torch.nn.functional.sigmoid": [[1742, "torch-nn-functional-sigmoid"]], "torch.nn.functional.nll_loss": [[1724, "torch-nn-functional-nll-loss"]], "torch.nn.functional.selu": [[1741, "torch-nn-functional-selu"]], "LazyModuleMixin": [[1762, "lazymodulemixin"]], "torch.nn.functional.softsign": [[1750, "torch-nn-functional-softsign"]], "torch.nn.functional.tanhshrink": [[1752, "torch-nn-functional-tanhshrink"]], "torch.nn.functional.softplus": [[1748, "torch-nn-functional-softplus"]], "torch.nn.modules.module.register_module_buffer_registration_hook": [[1764, "torch-nn-modules-module-register-module-buffer-registration-hook"]], "torch.nn.functional.rms_norm": [[1737, "torch-nn-functional-rms-norm"]], "torch.nn.functional.threshold_": [[1754, "torch-nn-functional-threshold"]], "torch.nn.functional.pdist": [[1729, "torch-nn-functional-pdist"]], "torch.nn.functional.relu_": [[1736, "torch-nn-functional-relu"]], "torch.nn.functional.softmin": [[1747, "torch-nn-functional-softmin"]], "torch.nn.functional.pad": [[1727, "torch-nn-functional-pad"]], "torch.nn.functional.poisson_nll_loss": [[1732, "torch-nn-functional-poisson-nll-loss"]], "torch.nn.functional.silu": [[1743, "torch-nn-functional-silu"]], "torch.nn.functional.torch.nn.parallel.data_parallel": [[1755, "torch-nn-functional-torch-nn-parallel-data-parallel"]], "torch.nn.functional.relu6": [[1735, "torch-nn-functional-relu6"]], "torch.nn.functional.smooth_l1_loss": [[1744, 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"PyTorch documentation": [[2096, "pytorch-documentation"]], "Community": [[2096, null]], "Developer Notes": [[2096, null]], "Language Bindings": [[2096, null]], "Python API": [[2096, null], [2155, "module-torch.onnx"]], "Libraries": [[2096, null]], "Indices and tables": [[2096, "indices-and-tables"]], "torch.mps": [[2114, "module-torch.mps"]], "MPS Profiler": [[2114, "mps-profiler"]], "MPS Event": [[2114, "mps-event"]], "torch.nested": [[2121, "module-torch.nested"]], "Construction": [[2121, "construction"], [2170, "construction"]], "Data Layout and Shape": [[2121, "data-layout-and-shape"]], "Viewing nested tensor constituents": [[2121, "viewing-nested-tensor-constituents"]], "Conversions to / from padded": [[2121, "conversions-to-from-padded"]], "Shape manipulations": [[2121, "shape-manipulations"]], "Attention mechanisms": [[2121, "attention-mechanisms"]], "Usage with torch.compile": [[2121, "usage-with-torch-compile"]], "Unimplemented ops": [[2121, "unimplemented-ops"]], "Ragged 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"python-language-reference-comparison"]], "Disable JIT for Debugging": [[2097, "disable-jit-for-debugging"]], "Inspecting Code": [[2097, "inspecting-code"]], "Interpreting Graphs": [[2097, "interpreting-graphs"]], "Tracer": [[2097, "tracer"]], "Tracing Edge Cases": [[2097, "tracing-edge-cases"]], "Automatic Trace Checking": [[2097, "automatic-trace-checking"]], "Tracer Warnings": [[2097, "tracer-warnings"]], "Known Issues": [[2097, "known-issues"]], "Appendix": [[2097, "appendix"]], "Migrating to PyTorch 1.2 Recursive Scripting API": [[2097, "migrating-to-pytorch-1-2-recursive-scripting-api"]], "Modules": [[2097, "modules"], [2144, "modules"]], "Attributes": [[2097, "attributes"]], "Constants": [[2097, "constants"], [2179, "constants"]], "Fusion Backends": [[2097, "fusion-backends"]], "torch.hub": [[2095, "torch-hub"]], "Publishing models": [[2095, "publishing-models"]], "How to implement an entrypoint?": [[2095, "how-to-implement-an-entrypoint"]], "Important Notice": [[2095, "important-notice"]], "Loading models from Hub": [[2095, "loading-models-from-hub"]], "Running a loaded model:": [[2095, "running-a-loaded-model"]], "Where are my downloaded models saved?": [[2095, "where-are-my-downloaded-models-saved"]], "Caching logic": [[2095, "caching-logic"]], "Known limitations:": [[2095, "known-limitations"]], "Python Language Reference Coverage": [[2101, "python-language-reference-coverage"]], "torch.xpu.set_rng_state_all": [[2089, "torch-xpu-set-rng-state-all"]], "torch.masked": [[2107, "torch-masked"]], "What is a MaskedTensor?": [[2107, "what-is-a-maskedtensor"]], "Supported Operators": [[2107, "supported-operators"]], "Unary Operators": [[2107, "unary-operators"]], "Binary Operators": [[2107, "binary-operators"]], "Reductions": [[2107, "reductions"]], "View and select functions": [[2107, "view-and-select-functions"]], "torch.xpu.set_device": [[2087, "torch-xpu-set-device"]], "torch.mtia": [[2116, "torch-mtia"]], "torch.utils.mobile_optimizer": [[2110, 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"concurrency-on-cpu"]], "Non-determinism": [[2130, "non-determinism"]], "Graph retaining": [[2130, "graph-retaining"]], "Thread Safety on Autograd Node": [[2130, "thread-safety-on-autograd-node"]], "No thread safety on C++ hooks": [[2130, "no-thread-safety-on-c-hooks"]], "Autograd for Complex Numbers": [[2130, "autograd-for-complex-numbers"]], "What are complex derivatives?": [[2130, "what-are-complex-derivatives"]], "Wirtinger Calculus comes into the picture \u2026": [[2130, "wirtinger-calculus-comes-into-the-picture"]], "How is Wirtinger Calculus useful in optimization?": [[2130, "how-is-wirtinger-calculus-useful-in-optimization"]], "How does PyTorch compute the conjugate Wirtinger derivative?": [[2130, "how-does-pytorch-compute-the-conjugate-wirtinger-derivative"]], "How can I write my own derivative formula for a complex function?": [[2130, "how-can-i-write-my-own-derivative-formula-for-a-complex-function"]], "What about cross-domain functions?": [[2130, "what-about-cross-domain-functions"]], "Hooks for saved tensors": [[2130, "hooks-for-saved-tensors"]], "Registering hooks for a saved tensor": [[2130, "registering-hooks-for-a-saved-tensor"]], "Registering default hooks for saved tensors": [[2130, "registering-default-hooks-for-saved-tensors"]], "Backward Hooks execution": [[2130, "backward-hooks-execution"]], "Whether a particular hook will be fired": [[2130, "whether-a-particular-hook-will-be-fired"]], "The order in which the different hooks are fired": [[2130, "the-order-in-which-the-different-hooks-are-fired"]], "Special hooks": [[2130, "special-hooks"]], "Behavior of Tensor hooks when Tensor is modified in-place": [[2130, "behavior-of-tensor-hooks-when-tensor-is-modified-in-place"]], "torch.xpu.memory_reserved": [[2080, "torch-xpu-memory-reserved"]], "Broadcasting semantics": [[2131, "broadcasting-semantics"]], "General semantics": [[2131, "general-semantics"]], "In-place semantics": [[2131, "in-place-semantics"]], "Backwards 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[[935, "nestediofunction"]], "torch.autograd.function.FunctionCtx.save_for_backward": [[932, "torch-autograd-function-functionctx-save-for-backward"]], "torch.arcsinh": [[898, "torch-arcsinh"]], "torch.atleast_1d": [[915, "torch-atleast-1d"]], "quantize_qat": [[892, "quantize-qat"]], "torch.asarray": [[909, "torch-asarray"]], "InplaceFunction": [[934, "inplacefunction"]], "torch.arctanh": [[901, "torch-arctanh"]], "torch.asin": [[910, "torch-asin"]], "torch.atan2": [[913, "torch-atan2"]], "torch.atleast_3d": [[917, "torch-atleast-3d"]], "fuse_fx": [[889, "fuse-fx"]], "swap_module": [[893, "swap-module"]], "torch.autograd.Function.vmap": [[921, "torch-autograd-function-vmap"]], "torch.arccosh": [[896, "torch-arccosh"]], "UnpackedDualTensor": [[923, "unpackeddualtensor"]], "prepare_fx": [[890, "prepare-fx"]], "torch.autograd.forward_ad.enter_dual_level": [[925, "torch-autograd-forward-ad-enter-dual-level"]], "default_placeholder_observer": [[860, "default-placeholder-observer"]], "default_debug_observer": [[854, "default-debug-observer"]], "MinMaxObserver": [[838, "minmaxobserver"]], "generate_numeric_debug_handle": [[833, "generate-numeric-debug-handle"]], "default_float_qparams_observer": [[856, "default-float-qparams-observer"]], "default_qconfig": [[877, "default-qconfig"]], "default_histogram_observer": [[857, "default-histogram-observer"]], "NoopObserver": [[841, "noopobserver"]], "PlaceholderObserver": [[850, "placeholderobserver"]], "QConfig": [[870, "qconfig"], [2160, "qconfig"]], "default_observer": [[858, "default-observer"]], "default_debug_qconfig": [[872, "default-debug-qconfig"]], "MovingAverageMinMaxObserver": [[839, "movingaverageminmaxobserver"]], "default_qat_qconfig": [[875, "default-qat-qconfig"]], "PerBlock": [[844, "perblock"]], "load_observer_state_dict": [[864, "load-observer-state-dict"]], "get_block_size": [[862, "get-block-size"]], "prepare_for_propagation_comparison": [[866, "prepare-for-propagation-comparison"]], "default_weight_observer": [[861, "default-weight-observer"]], "MappingType": [[837, "mappingtype"]], "PerChannelMinMaxObserver": [[845, "perchannelminmaxobserver"]], "HistogramObserver": [[836, "histogramobserver"]], "prepare": [[865, "prepare"]], "RecordingObserver": [[851, "recordingobserver"]], "default_dynamic_qconfig": [[873, "default-dynamic-qconfig"]], "StandaloneModuleConfigEntry": [[832, "standalonemoduleconfigentry"]], "PerToken": [[849, "pertoken"]], "per_channel_dynamic_qconfig": [[882, "per-channel-dynamic-qconfig"]], "default_qat_qconfig_v2": [[876, "default-qat-qconfig-v2"]], "PerGroup": [[846, "pergroup"]], "ZeroPointDomain": [[853, "zeropointdomain"]], "MovingAveragePerChannelMinMaxObserver": [[840, "movingaverageperchannelminmaxobserver"]], "TorchAODType": [[852, "torchaodtype"]], "AffineQuantizedObserverBase": [[834, "affinequantizedobserverbase"]], "prepare_qat": [[867, "prepare-qat"]], "model_is_exported": [[869, "model-is-exported"]], "default_weight_only_qconfig": [[878, "default-weight-only-qconfig"]], "PerAxis": [[843, "peraxis"]], "float16_dynamic_qconfig": [[879, "float16-dynamic-qconfig"]], "PerRow": [[847, "perrow"]], "default_activation_only_qconfig": [[871, "default-activation-only-qconfig"]], "float_qparams_weight_only_qconfig": [[881, "float-qparams-weight-only-qconfig"]], "default_per_channel_qconfig": [[874, "default-per-channel-qconfig"]], "default_dynamic_quant_observer": [[855, "default-dynamic-quant-observer"]], "float16_static_qconfig": [[880, "float16-static-qconfig"]], "QConfigMapping": [[883, "qconfigmapping"]], "ObserverBase": [[842, "observerbase"]], "Granularity": [[835, "granularity"]], "get_observer_state_dict": [[863, "get-observer-state-dict"]], "propagate_qconfig": [[868, "propagate-qconfig"]], "default_per_channel_weight_observer": [[859, "default-per-channel-weight-observer"]], "PerTensor": [[848, "pertensor"]], "upsample": [[795, "upsample"]], "FakeQuantizeBase": [[814, "fakequantizebase"]], "conv1d": [[782, "conv1d"]], "max_pool1d": [[792, "max-pool1d"]], "upsample_nearest": [[797, "upsample-nearest"]], "default_fake_quant": [[817, "default-fake-quant"]], "extract_results_from_loggers": [[812, "extract-results-from-loggers"]], "default_fused_wt_fake_quant": [[820, "default-fused-wt-fake-quant"]], "default_weight_fake_quant": [[823, "default-weight-fake-quant"]], "enable_observer": [[827, "enable-observer"]], "FakeQuantize": [[813, "fakequantize"]], "default_fused_per_channel_wt_fake_quant": [[819, "default-fused-per-channel-wt-fake-quant"]], "hardswish": [[787, "hardswish"]], "ConvertCustomConfig": [[829, "convertcustomconfig"]], "disable_fake_quant": [[824, "disable-fake-quant"]], "default_histogram_fake_quant": [[821, "default-histogram-fake-quant"]], "clamp": [[781, "clamp"]], "CUSTOM_KEY": [[798, "custom-key"]], "QuantWrapper": [[802, "quantwrapper"]], "fuse_modules": [[828, "fuse-modules"]], "hardsigmoid": [[786, "hardsigmoid"]], "FixedQParamsFakeQuantize": [[815, "fixedqparamsfakequantize"]], "enable_fake_quant": [[826, "enable-fake-quant"]], "FuseCustomConfig": [[830, "fusecustomconfig"]], "BackendPatternConfig": [[805, "backendpatternconfig"]], "default_fused_act_fake_quant": [[818, "default-fused-act-fake-quant"]], "upsample_bilinear": [[796, "upsample-bilinear"]], "DTypeWithConstraints": [[807, "dtypewithconstraints"]], "PrepareCustomConfig": [[831, "preparecustomconfig"]], "disable_observer": [[825, "disable-observer"]], "NUMERIC_DEBUG_HANDLE_KEY": [[800, "numeric-debug-handle-key"]], "default_per_channel_weight_fake_quant": [[822, "default-per-channel-weight-fake-quant"]], "FusedMovingAvgObsFakeQuantize": [[816, "fusedmovingavgobsfakequantize"]], "BackendConfig": [[804, "backendconfig"]], "DeQuantStub": [[799, "dequantstub"]], "conv2d": [[783, "conv2d"]], "conv3d": [[784, "conv3d"]], "linear": [[791, "linear"]], "QuantStub": [[801, "quantstub"]], "hardtanh": [[788, "hardtanh"]], "interpolate": [[789, "interpolate"]], "ObservationType": [[808, "observationtype"]], "threshold": [[794, "threshold"]], "celu": [[780, "celu"]], "convert": [[810, "convert"]], "leaky_relu": [[790, "leaky-relu"]], "default_eval_fn": [[811, "default-eval-fn"]], "compare_results": [[809, "compare-results"]], "add_quant_dequant": [[803, "add-quant-dequant"]], "elu": [[785, "elu"]], "max_pool2d": [[793, "max-pool2d"]], "DTypeConfig": [[806, "dtypeconfig"]], "Features for large-scale deployments": [[2143, "features-for-large-scale-deployments"]], "Fleet-wide operator profiling": [[2143, "fleet-wide-operator-profiling"]], "API usage logging": [[2143, "api-usage-logging"]], "Attaching metadata to saved TorchScript models": [[2143, "attaching-metadata-to-saved-torchscript-models"]], "Build environment considerations": [[2143, "build-environment-considerations"]], "Common extension points": [[2143, "common-extension-points"]], "torch.sparse": [[2170, "torch-sparse"]], "Why and when to use sparsity": [[2170, "why-and-when-to-use-sparsity"]], "Functionality overview": [[2170, "functionality-overview"]], "Operator overview": [[2170, "operator-overview"]], "Sparse Semi-Structured Tensors": [[2170, "sparse-semi-structured-tensors"]], "Constructing Sparse Semi-Structured Tensors": [[2170, "constructing-sparse-semi-structured-tensors"]], "Sparse Semi-Structured Tensor Operations": [[2170, "sparse-semi-structured-tensor-operations"]], "Accelerating nn.Linear with semi-structured sparsity": [[2170, "accelerating-nn-linear-with-semi-structured-sparsity"]], "Sparse COO tensors": [[2170, "sparse-coo-tensors"]], "Sparse hybrid COO tensors": [[2170, "sparse-hybrid-coo-tensors"]], "Uncoalesced sparse COO tensors": [[2170, "uncoalesced-sparse-coo-tensors"]], "Working with sparse COO tensors": [[2170, "working-with-sparse-coo-tensors"]], "Sparse Compressed Tensors": [[2170, "sparse-compressed-tensors"]], "Sparse CSR Tensor": [[2170, "sparse-csr-tensor"]], "Construction of CSR tensors": [[2170, "construction-of-csr-tensors"]], "CSR Tensor Operations": [[2170, "csr-tensor-operations"]], "Sparse CSC Tensor": [[2170, "sparse-csc-tensor"]], "Construction of CSC tensors": [[2170, "construction-of-csc-tensors"]], "Sparse BSR Tensor": [[2170, "sparse-bsr-tensor"]], "Construction of BSR tensors": [[2170, "construction-of-bsr-tensors"]], "Sparse BSC Tensor": [[2170, "sparse-bsc-tensor"]], "Construction of BSC tensors": [[2170, "construction-of-bsc-tensors"]], "Tools for working with sparse compressed tensors": [[2170, "tools-for-working-with-sparse-compressed-tensors"]], "Construction of sparse compressed tensors": [[2170, "construction-of-sparse-compressed-tensors"]], "Supported operations": [[2170, "supported-operations"]], "Linear Algebra operations": [[2170, "linear-algebra-operations"]], "Tensor methods and sparse": [[2170, "tensor-methods-and-sparse"]], "Torch functions specific to sparse Tensors": [[2170, "torch-functions-specific-to-sparse-tensors"]], "Other functions": [[2170, "other-functions"]], "Zero-preserving unary functions": [[2170, "zero-preserving-unary-functions"]], "torch": [[2179, "module-torch"]], "Tensors": [[2179, "tensors"]], "Creation Ops": [[2179, "creation-ops"]], "Indexing, Slicing, Joining, Mutating Ops": [[2179, "indexing-slicing-joining-mutating-ops"]], "Accelerators": [[2179, "accelerators"]], "Generators": [[2179, "generators"]], "Random sampling": [[2179, "random-sampling"]], "In-place random sampling": [[2179, "in-place-random-sampling"]], "Quasi-random sampling": [[2179, "quasi-random-sampling"]], "Parallelism": [[2179, "parallelism"]], "Math operations": [[2179, "math-operations"]], "Pointwise Ops": [[2179, "pointwise-ops"]], "Reduction Ops": [[2179, "reduction-ops"]], "Comparison Ops": [[2179, "comparison-ops"]], "Spectral Ops": [[2179, "spectral-ops"]], "Other Operations": [[2179, "other-operations"]], "BLAS and LAPACK Operations": [[2179, "blas-and-lapack-operations"]], "Foreach Operations": [[2179, "foreach-operations"]], "Symbolic Numbers": [[2179, "symbolic-numbers"]], "Export Path": [[2179, "export-path"]], "Control Flow": [[2179, "control-flow"]], "Optimizations": [[2179, "optimizations"]], "Operator Tags": [[2179, "operator-tags"]], "torch.package": [[2158, "torch-package"]], "Tutorials": [[2158, "tutorials"], [2165, "tutorials"], [8, "tutorials"]], "Packaging your first model": [[2158, "packaging-your-first-model"]], "How do I\u2026": [[2158, "how-do-i"]], "See what is inside a package?": [[2158, "see-what-is-inside-a-package"]], "Treat the package like a ZIP archive": [[2158, "treat-the-package-like-a-zip-archive"]], "Use the file_structure() API": [[2158, "use-the-file-structure-api"]], "See why a given module was included as a dependency?": [[2158, "see-why-a-given-module-was-included-as-a-dependency"]], "Include arbitrary resources with my package and access them later?": [[2158, "include-arbitrary-resources-with-my-package-and-access-them-later"]], "Customize how a class is packaged?": [[2158, "customize-how-a-class-is-packaged"]], "Test in my source code whether or not it is executing inside a package?": [[2158, "test-in-my-source-code-whether-or-not-it-is-executing-inside-a-package"]], "Patch code into a package?": [[2158, "patch-code-into-a-package"]], "Access package contents from packaged code?": [[2158, "access-package-contents-from-packaged-code"]], "Distinguish between packaged code and non-packaged code?": [[2158, "distinguish-between-packaged-code-and-non-packaged-code"]], "Re-export an imported object?": [[2158, "re-export-an-imported-object"]], "Package a TorchScript module?": [[2158, "package-a-torchscript-module"]], "Explanation": [[2158, "explanation"]], "torch.package Format Overview": [[2158, "torch-package-format-overview"]], "Framework files": [[2158, "framework-files"]], "User files": [[2158, "user-files"]], "How torch.package finds your code\u2019s dependencies": [[2158, "how-torch-package-finds-your-code-s-dependencies"]], "Analyzing an object\u2019s dependencies": [[2158, "analyzing-an-object-s-dependencies"]], "Analyzing a module\u2019s dependencies": [[2158, "analyzing-a-module-s-dependencies"]], "Dependency Management": [[2158, "dependency-management"]], "intern": [[2158, "intern"]], "extern": [[2158, "extern"]], "mock": [[2158, "mock"]], "Refactoring": [[2158, "refactoring"]], "Patterns": [[2158, "patterns"]], "torch.package sharp edges": [[2158, "torch-package-sharp-edges"]], "Avoid global state in your modules": [[2158, "avoid-global-state-in-your-modules"]], "Types are not shared between packages and the loading environment": [[2158, "types-are-not-shared-between-packages-and-the-loading-environment"]], "How torch.package keeps packages isolated from each other": [[2158, "how-torch-package-keeps-packages-isolated-from-each-other"]], "Mangling": [[2158, "mangling"]], "torch.random": [[2164, "module-torch.random"]], "torch.Size": [[2169, "torch-size"]], "torch.Storage": [[2172, "torch-storage"]], "Untyped Storage API": [[2172, "untyped-storage-api"]], "Special cases": [[2172, "special-cases"]], "Legacy Typed Storage": [[2172, "legacy-typed-storage"]], "torch.ao.ns._numeric_suite_fx": [[2181, "torch-ao-ns-numeric-suite-fx"]], "torch.ao.ns.fx.utils": [[2181, "torch-ao-ns-fx-utils"]], "Tensor Attributes": [[2173, "tensor-attributes"]], "torch.dtype": [[2173, "torch-dtype"]], "torch.device": [[2173, "torch-device"]], "torch.layout": [[2173, "torch-layout"]], "torch.memory_format": [[2173, "torch-memory-format"]], "Serialization semantics": [[2149, "serialization-semantics"]], "Table of Contents": [[2149, "table-of-contents"]], "Saving and loading tensors": [[2149, "saving-and-loading-tensors"]], "Saving and loading tensors preserves views": [[2149, "saving-and-loading-tensors-preserves-views"]], "Saving and loading torch.nn.Modules": [[2149, "saving-and-loading-torch-nn-modules"]], "Serialized file format for torch.save": [[2149, "serialized-file-format-for-torch-save"]], "torch.load with weights_only=True": [[2149, "torch-load-with-weights-only-true"]], "Troubleshooting weights_only": [[2149, "troubleshooting-weights-only"]], "Getting unsafe globals": [[2149, "getting-unsafe-globals"]], "Environment Variables": [[2149, "environment-variables"], [51, "environment-variables"]], "Serializing torch.nn.Modules and loading them in C++": [[2149, "serializing-torch-nn-modules-and-loading-them-in-c"]], "Saving and loading ScriptModules across PyTorch versions": [[2149, "saving-and-loading-scriptmodules-across-pytorch-versions"]], "torch.div performing integer division": [[2149, "torch-div-performing-integer-division"]], "torch.full always inferring a float dtype": [[2149, "torch-full-always-inferring-a-float-dtype"]], "Utility functions": [[2149, "utility-functions"], [2163, "utility-functions"]], "Config": [[2149, "module-torch.utils.serialization"]], "ONNX Backend for TorchDynamo": [[2154, "onnx-backend-for-torchdynamo"]], "torch.utils.tensorboard": [[2175, "module-torch.utils.tensorboard"]], "Extending PyTorch": [[2136, "extending-pytorch"]], "Adding new operators": [[2136, "adding-new-operators"]], "Extending torch.autograd": [[2136, "extending-torch-autograd"]], "When to use": [[2136, "when-to-use"]], "When not to use": [[2136, "when-not-to-use"]], "How to use": [[2136, "how-to-use"]], "Example": [[2136, "example"], [2135, "example"]], "Combined or separate forward() and setup_context()": [[2136, "combined-or-separate-forward-and-setup-context"]], "Forward mode AD": [[2136, "forward-mode-ad"]], "torch.func transforms and/or torch.vmap()": [[2136, "torch-func-transforms-and-or-torch-vmap"]], "Extending torch.nn": [[2136, "extending-torch-nn"]], "Adding a Module": [[2136, "adding-a-module"]], "Extending torch Python API": [[2136, "extending-torch-python-api"]], "Extending torch with a Tensor-like type": [[2136, "extending-torch-with-a-tensor-like-type"]], "Subclassing torch.Tensor": [[2136, "subclassing-torch-tensor"]], "Extending torch with a Tensor wrapper type": [[2136, "extending-torch-with-a-tensor-wrapper-type"]], "Operations on multiple types that define __torch_function__": [[2136, "operations-on-multiple-types-that-define-torch-function"]], "Testing Coverage of Overrides for the PyTorch API": [[2136, "testing-coverage-of-overrides-for-the-pytorch-api"]], "Extending torch native API": [[2136, "extending-torch-native-api"]], "__torch_dispatch__ calling convention": [[2136, "torch-dispatch-calling-convention"]], "Extending all torch API with Modes": [[2136, "extending-all-torch-api-with-modes"]], "Extending torch.func with autograd.Function": [[2137, "extending-torch-func-with-autograd-function"]], "Basic Usage": [[2137, "basic-usage"]], "Example 1: autograd.Function calls into another system": [[2137, "example-1-autograd-function-calls-into-another-system"]], "Example 2: autograd.Function specifies custom gradient rules": [[2137, "example-2-autograd-function-specifies-custom-gradient-rules"]], "Limitations and gotchas": [[2137, "limitations-and-gotchas"]], "torch.vmap() Support": [[2137, "torch-vmap-support"]], "Automatically generate a vmap rule": [[2137, "automatically-generate-a-vmap-rule"]], "Defining the vmap staticmethod": [[2137, "defining-the-vmap-staticmethod"]], "torch.func.jvp() Support": [[2137, "torch-func-jvp-support"]], "TorchScript-based ONNX Exporter": [[2155, "torchscript-based-onnx-exporter"], [2151, "torchscript-based-onnx-exporter"]], "Example: AlexNet from PyTorch to ONNX": [[2155, "example-alexnet-from-pytorch-to-onnx"]], "Tracing vs Scripting": [[2155, "tracing-vs-scripting"]], "Avoiding Pitfalls": [[2155, "avoiding-pitfalls"]], "Avoid NumPy and built-in Python types": [[2155, "avoid-numpy-and-built-in-python-types"]], "Avoid Tensor.data": [[2155, "avoid-tensor-data"]], "Avoid in-place operations when using tensor.shape in tracing mode": [[2155, "avoid-in-place-operations-when-using-tensor-shape-in-tracing-mode"]], "Differences in Operator Implementations": [[2155, "differences-in-operator-implementations"]], "Unsupported Tensor Indexing Patterns": [[2155, "unsupported-tensor-indexing-patterns"]], "Reads / Gets": [[2155, "reads-gets"]], "Writes / Sets": [[2155, "writes-sets"]], "Adding support for operators": [[2155, "adding-support-for-operators"]], "ONNX exporter internals": [[2155, "onnx-exporter-internals"]], "ATen operators": [[2155, "aten-operators"]], "List of supported operators": [[2155, "list-of-supported-operators"]], "Adding support for an aten or quantized operator": [[2155, "adding-support-for-an-aten-or-quantized-operator"]], "torch.autograd.Functions": [[2155, "torch-autograd-functions"]], "Static Symbolic Method": [[2155, "static-symbolic-method"]], "Inline Autograd Function": [[2155, "inline-autograd-function"]], "ONNX-script functions": [[2155, "onnx-script-functions"]], "C++ Operators": [[2155, "c-operators"]], "Discovering all unconvertible ATen ops at once": [[2155, "discovering-all-unconvertible-aten-ops-at-once"]], "Classes": [[2155, "classes"]], "torch.onnx": [[2151, "torch-onnx"]], "TorchDynamo-based ONNX Exporter": [[2151, "torchdynamo-based-onnx-exporter"], [2152, "torchdynamo-based-onnx-exporter"]], "Contributing / Developing": [[2151, "contributing-developing"]], "HIP (ROCm) semantics": [[2142, "hip-rocm-semantics"]], "HIP Interfaces Reuse the CUDA Interfaces": [[2142, "hip-interfaces-reuse-the-cuda-interfaces"]], "Checking for HIP": [[2142, "checking-for-hip"]], "TensorFloat-32(TF32) on ROCm": [[2142, "tensorfloat-32-tf32-on-rocm"]], "hipBLAS workspaces": [[2142, "hipblas-workspaces"]], "hipFFT/rocFFT plan cache": [[2142, "hipfft-rocfft-plan-cache"]], "torch.distributed backends": [[2142, "torch-distributed-backends"]], "CUDA API to HIP API mappings in C++": [[2142, "cuda-api-to-hip-api-mappings-in-c"]], "Refer to CUDA Semantics doc": [[2142, "refer-to-cuda-semantics-doc"]], "Enabling kernel asserts": [[2142, "enabling-kernel-asserts"]], "torch.profiler": [[2159, "torch-profiler"]], "Intel Instrumentation and Tracing Technology APIs": [[2159, "intel-instrumentation-and-tracing-technology-apis"]], "Quantization": [[2160, "module-torch.ao.quantization"]], "Introduction to Quantization": [[2160, "introduction-to-quantization"]], "Quantization API Summary": [[2160, "quantization-api-summary"]], "Eager Mode Quantization": [[2160, "eager-mode-quantization"]], "Post Training Dynamic Quantization": [[2160, "post-training-dynamic-quantization"]], "Post Training Static Quantization": [[2160, "post-training-static-quantization"]], "Quantization Aware Training for Static Quantization": [[2160, "quantization-aware-training-for-static-quantization"]], "Model Preparation for Eager Mode Static Quantization": [[2160, "model-preparation-for-eager-mode-static-quantization"]], "(Prototype - maintenance mode) FX Graph Mode Quantization": [[2160, "prototype-maintenance-mode-fx-graph-mode-quantization"]], "(Prototype) PyTorch 2 Export Quantization": [[2160, 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@@ -718,52 +718,52 @@ <h2>Special cases<a class="headerlink" href="#special-cases" title="Permalink to
 expect many use cases to require operating at the Storage level!</p>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.UntypedStorage">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">UntypedStorage</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/storage.html#UntypedStorage"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/storage.py#L458"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.UntypedStorage" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">UntypedStorage</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/storage.html#UntypedStorage"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/storage.py#L458"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.UntypedStorage" title="Permalink to this definition">¶</a></dt>
 <dd><dl class="py method">
 <dt class="sig sig-object py" id="torch.UntypedStorage.bfloat16">
-<span class="sig-name descname"><span class="pre">bfloat16</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/storage.py#L321"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.UntypedStorage.bfloat16" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">bfloat16</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/storage.py#L321"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.UntypedStorage.bfloat16" title="Permalink to this definition">¶</a></dt>
 <dd><p>Casts this storage to bfloat16 type.</p>
 </dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.UntypedStorage.bool">
-<span class="sig-name descname"><span class="pre">bool</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/storage.py#L317"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.UntypedStorage.bool" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">bool</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/storage.py#L317"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.UntypedStorage.bool" title="Permalink to this definition">¶</a></dt>
 <dd><p>Casts this storage to bool type.</p>
 </dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.UntypedStorage.byte">
-<span class="sig-name descname"><span class="pre">byte</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/storage.py#L313"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.UntypedStorage.byte" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">byte</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/storage.py#L313"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.UntypedStorage.byte" title="Permalink to this definition">¶</a></dt>
 <dd><p>Casts this storage to byte type.</p>
 </dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.UntypedStorage.byteswap">
-<span class="sig-name descname"><span class="pre">byteswap</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dtype</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/storage.py#L413"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.UntypedStorage.byteswap" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">byteswap</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dtype</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/storage.py#L413"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.UntypedStorage.byteswap" title="Permalink to this definition">¶</a></dt>
 <dd><p>Swap bytes in underlying data.</p>
 </dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.UntypedStorage.char">
-<span class="sig-name descname"><span class="pre">char</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/storage.py#L309"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.UntypedStorage.char" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">char</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/storage.py#L309"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.UntypedStorage.char" title="Permalink to this definition">¶</a></dt>
 <dd><p>Casts this storage to char type.</p>
 </dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.UntypedStorage.clone">
-<span class="sig-name descname"><span class="pre">clone</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/storage.py#L247"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.UntypedStorage.clone" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">clone</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/storage.py#L247"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.UntypedStorage.clone" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return a copy of this storage.</p>
 </dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.UntypedStorage.complex_double">
-<span class="sig-name descname"><span class="pre">complex_double</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/storage.py#L325"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.UntypedStorage.complex_double" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">complex_double</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/storage.py#L325"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.UntypedStorage.complex_double" title="Permalink to this definition">¶</a></dt>
 <dd><p>Casts this storage to complex double type.</p>
 </dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.UntypedStorage.complex_float">
-<span class="sig-name descname"><span class="pre">complex_float</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/storage.py#L329"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.UntypedStorage.complex_float" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">complex_float</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/storage.py#L329"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.UntypedStorage.complex_float" title="Permalink to this definition">¶</a></dt>
 <dd><p>Casts this storage to complex float type.</p>
 </dd></dl>
 
@@ -774,13 +774,13 @@ <h2>Special cases<a class="headerlink" href="#special-cases" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.UntypedStorage.cpu">
-<span class="sig-name descname"><span class="pre">cpu</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/storage.py#L255"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.UntypedStorage.cpu" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">cpu</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/storage.py#L255"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.UntypedStorage.cpu" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return a CPU copy of this storage if it’s not already on the CPU.</p>
 </dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.UntypedStorage.cuda">
-<span class="sig-name descname"><span class="pre">cuda</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">non_blocking</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/storage.py#L78"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.UntypedStorage.cuda" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">cuda</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">non_blocking</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/storage.py#L78"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.UntypedStorage.cuda" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns a copy of this object in CUDA memory.</p>
 <p>If this object is already in CUDA memory and on the correct device, then
 no copy is performed and the original object is returned.</p>
@@ -811,7 +811,7 @@ <h2>Special cases<a class="headerlink" href="#special-cases" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.UntypedStorage.double">
-<span class="sig-name descname"><span class="pre">double</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/storage.py#L285"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.UntypedStorage.double" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">double</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/storage.py#L285"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.UntypedStorage.double" title="Permalink to this definition">¶</a></dt>
 <dd><p>Casts this storage to double type.</p>
 </dd></dl>
 
@@ -835,31 +835,31 @@ <h2>Special cases<a class="headerlink" href="#special-cases" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.UntypedStorage.float">
-<span class="sig-name descname"><span class="pre">float</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/storage.py#L289"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.UntypedStorage.float" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">float</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/storage.py#L289"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.UntypedStorage.float" title="Permalink to this definition">¶</a></dt>
 <dd><p>Casts this storage to float type.</p>
 </dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.UntypedStorage.float8_e4m3fn">
-<span class="sig-name descname"><span class="pre">float8_e4m3fn</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/storage.py#L337"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.UntypedStorage.float8_e4m3fn" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">float8_e4m3fn</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/storage.py#L337"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.UntypedStorage.float8_e4m3fn" title="Permalink to this definition">¶</a></dt>
 <dd><p>Casts this storage to float8_e4m3fn type</p>
 </dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.UntypedStorage.float8_e4m3fnuz">
-<span class="sig-name descname"><span class="pre">float8_e4m3fnuz</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/storage.py#L345"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.UntypedStorage.float8_e4m3fnuz" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">float8_e4m3fnuz</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/storage.py#L345"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.UntypedStorage.float8_e4m3fnuz" title="Permalink to this definition">¶</a></dt>
 <dd><p>Casts this storage to float8_e4m3fnuz type</p>
 </dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.UntypedStorage.float8_e5m2">
-<span class="sig-name descname"><span class="pre">float8_e5m2</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/storage.py#L333"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.UntypedStorage.float8_e5m2" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">float8_e5m2</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/storage.py#L333"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.UntypedStorage.float8_e5m2" title="Permalink to this definition">¶</a></dt>
 <dd><p>Casts this storage to float8_e5m2 type</p>
 </dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.UntypedStorage.float8_e5m2fnuz">
-<span class="sig-name descname"><span class="pre">float8_e5m2fnuz</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/storage.py#L341"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.UntypedStorage.float8_e5m2fnuz" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">float8_e5m2fnuz</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/storage.py#L341"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.UntypedStorage.float8_e5m2fnuz" title="Permalink to this definition">¶</a></dt>
 <dd><p>Casts this storage to float8_e5m2fnuz type</p>
 </dd></dl>
 
@@ -893,7 +893,7 @@ <h2>Special cases<a class="headerlink" href="#special-cases" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.UntypedStorage.get_device">
-<span class="sig-name descname"><span class="pre">get_device</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/storage.py#L113"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.UntypedStorage.get_device" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">get_device</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/storage.py#L113"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.UntypedStorage.get_device" title="Permalink to this definition">¶</a></dt>
 <dd><dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
 <dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)">int</a></p>
@@ -903,13 +903,13 @@ <h2>Special cases<a class="headerlink" href="#special-cases" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.UntypedStorage.half">
-<span class="sig-name descname"><span class="pre">half</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/storage.py#L293"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.UntypedStorage.half" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">half</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/storage.py#L293"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.UntypedStorage.half" title="Permalink to this definition">¶</a></dt>
 <dd><p>Casts this storage to half type.</p>
 </dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.UntypedStorage.hpu">
-<span class="sig-name descname"><span class="pre">hpu</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">non_blocking</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/storage.py#L95"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.UntypedStorage.hpu" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">hpu</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">non_blocking</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/storage.py#L95"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.UntypedStorage.hpu" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns a copy of this object in HPU memory.</p>
 <p>If this object is already in HPU memory and on the correct device, then
 no copy is performed and the original object is returned.</p>
@@ -930,7 +930,7 @@ <h2>Special cases<a class="headerlink" href="#special-cases" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.UntypedStorage.int">
-<span class="sig-name descname"><span class="pre">int</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/storage.py#L301"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.UntypedStorage.int" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">int</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/storage.py#L301"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.UntypedStorage.int" title="Permalink to this definition">¶</a></dt>
 <dd><p>Casts this storage to int type.</p>
 </dd></dl>
 
@@ -946,7 +946,7 @@ <h2>Special cases<a class="headerlink" href="#special-cases" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.UntypedStorage.is_pinned">
-<span class="sig-name descname"><span class="pre">is_pinned</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'cuda'</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/storage.py#L349"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.UntypedStorage.is_pinned" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">is_pinned</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'cuda'</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/storage.py#L349"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.UntypedStorage.is_pinned" title="Permalink to this definition">¶</a></dt>
 <dd><p>Determine whether the CPU storage is already pinned on device.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -976,13 +976,13 @@ <h2>Special cases<a class="headerlink" href="#special-cases" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.UntypedStorage.long">
-<span class="sig-name descname"><span class="pre">long</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/storage.py#L297"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.UntypedStorage.long" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">long</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/storage.py#L297"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.UntypedStorage.long" title="Permalink to this definition">¶</a></dt>
 <dd><p>Casts this storage to long type.</p>
 </dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.UntypedStorage.mps">
-<span class="sig-name descname"><span class="pre">mps</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/storage.py#L261"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.UntypedStorage.mps" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">mps</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/storage.py#L261"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.UntypedStorage.mps" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return a MPS copy of this storage if it’s not already on the MPS.</p>
 </dd></dl>
 
@@ -998,7 +998,7 @@ <h2>Special cases<a class="headerlink" href="#special-cases" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.UntypedStorage.pin_memory">
-<span class="sig-name descname"><span class="pre">pin_memory</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'cuda'</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/storage.py#L365"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.UntypedStorage.pin_memory" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">pin_memory</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'cuda'</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/storage.py#L365"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.UntypedStorage.pin_memory" title="Permalink to this definition">¶</a></dt>
 <dd><p>Copy the CPU storage to pinned memory, if it’s not already pinned.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -1023,7 +1023,7 @@ <h2>Special cases<a class="headerlink" href="#special-cases" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.UntypedStorage.share_memory_">
-<span class="sig-name descname"><span class="pre">share_memory_</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/storage.html#UntypedStorage.share_memory_"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/storage.py#L481"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.UntypedStorage.share_memory_" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">share_memory_</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/storage.html#UntypedStorage.share_memory_"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/storage.py#L481"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.UntypedStorage.share_memory_" title="Permalink to this definition">¶</a></dt>
 <dd><p>Moves the storage to shared memory.</p>
 <p>This is a no-op for storages already in shared memory and for CUDA
 storages, which do not need to be moved for sharing across processes.
@@ -1058,13 +1058,13 @@ <h2>Special cases<a class="headerlink" href="#special-cases" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.UntypedStorage.short">
-<span class="sig-name descname"><span class="pre">short</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/storage.py#L305"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.UntypedStorage.short" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">short</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/storage.py#L305"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.UntypedStorage.short" title="Permalink to this definition">¶</a></dt>
 <dd><p>Casts this storage to short type.</p>
 </dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.UntypedStorage.size">
-<span class="sig-name descname"><span class="pre">size</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/storage.py#L70"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.UntypedStorage.size" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">size</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/storage.py#L70"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.UntypedStorage.size" title="Permalink to this definition">¶</a></dt>
 <dd><dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
 <dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)">int</a></p>
@@ -1074,20 +1074,20 @@ <h2>Special cases<a class="headerlink" href="#special-cases" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.UntypedStorage.to">
-<span class="sig-name descname"><span class="pre">to</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">non_blocking</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/storage.py#L280"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.UntypedStorage.to" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">to</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">non_blocking</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/storage.py#L280"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.UntypedStorage.to" title="Permalink to this definition">¶</a></dt>
 <dd><dl class="field-list simple">
 </dl>
 </dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.UntypedStorage.tolist">
-<span class="sig-name descname"><span class="pre">tolist</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/storage.py#L251"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.UntypedStorage.tolist" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">tolist</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/storage.py#L251"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.UntypedStorage.tolist" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return a list containing the elements of this storage.</p>
 </dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.UntypedStorage.type">
-<span class="sig-name descname"><span class="pre">type</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">non_blocking</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/storage.py#L73"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.UntypedStorage.type" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">type</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">non_blocking</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/storage.py#L73"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.UntypedStorage.type" title="Permalink to this definition">¶</a></dt>
 <dd><dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
 <dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Union" title="(in Python v3.13)"><em>Union</em></a>[<em>_StorageBase</em>, <a class="reference internal" href="#torch.TypedStorage" title="torch.storage.TypedStorage"><em>TypedStorage</em></a>]</p>
@@ -1097,7 +1097,7 @@ <h2>Special cases<a class="headerlink" href="#special-cases" title="Permalink to
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.UntypedStorage.untyped">
-<span class="sig-name descname"><span class="pre">untyped</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/storage.py#L410"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.UntypedStorage.untyped" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">untyped</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/storage.py#L410"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.UntypedStorage.untyped" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 </dd></dl>
@@ -1132,67 +1132,67 @@ <h2>Legacy Typed Storage<a class="headerlink" href="#legacy-typed-storage" title
 which stores all of the data that the <a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.Tensor</span></code></a> views.</p>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.TypedStorage">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">TypedStorage</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">wrap_storage</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_internal</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/storage.html#TypedStorage"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/storage.py#L661"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.TypedStorage" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">TypedStorage</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">wrap_storage</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_internal</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/storage.html#TypedStorage"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/storage.py#L661"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.TypedStorage" title="Permalink to this definition">¶</a></dt>
 <dd><dl class="field-list simple">
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.TypedStorage.bfloat16">
-<span class="sig-name descname"><span class="pre">bfloat16</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/storage.html#TypedStorage.bfloat16"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/storage.py#L1357"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.TypedStorage.bfloat16" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">bfloat16</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/storage.html#TypedStorage.bfloat16"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/storage.py#L1357"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.TypedStorage.bfloat16" title="Permalink to this definition">¶</a></dt>
 <dd><p>Casts this storage to bfloat16 type.</p>
 </dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.TypedStorage.bool">
-<span class="sig-name descname"><span class="pre">bool</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/storage.html#TypedStorage.bool"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/storage.py#L1352"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.TypedStorage.bool" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">bool</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/storage.html#TypedStorage.bool"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/storage.py#L1352"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.TypedStorage.bool" title="Permalink to this definition">¶</a></dt>
 <dd><p>Casts this storage to bool type.</p>
 </dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.TypedStorage.byte">
-<span class="sig-name descname"><span class="pre">byte</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/storage.html#TypedStorage.byte"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/storage.py#L1347"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.TypedStorage.byte" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">byte</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/storage.html#TypedStorage.byte"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/storage.py#L1347"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.TypedStorage.byte" title="Permalink to this definition">¶</a></dt>
 <dd><p>Casts this storage to byte type.</p>
 </dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.TypedStorage.char">
-<span class="sig-name descname"><span class="pre">char</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/storage.html#TypedStorage.char"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/storage.py#L1342"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.TypedStorage.char" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">char</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/storage.html#TypedStorage.char"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/storage.py#L1342"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.TypedStorage.char" title="Permalink to this definition">¶</a></dt>
 <dd><p>Casts this storage to char type.</p>
 </dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.TypedStorage.clone">
-<span class="sig-name descname"><span class="pre">clone</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/storage.html#TypedStorage.clone"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/storage.py#L1135"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.TypedStorage.clone" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">clone</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/storage.html#TypedStorage.clone"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/storage.py#L1135"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.TypedStorage.clone" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return a copy of this storage.</p>
 </dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.TypedStorage.complex_double">
-<span class="sig-name descname"><span class="pre">complex_double</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/storage.html#TypedStorage.complex_double"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/storage.py#L1362"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.TypedStorage.complex_double" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">complex_double</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/storage.html#TypedStorage.complex_double"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/storage.py#L1362"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.TypedStorage.complex_double" title="Permalink to this definition">¶</a></dt>
 <dd><p>Casts this storage to complex double type.</p>
 </dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.TypedStorage.complex_float">
-<span class="sig-name descname"><span class="pre">complex_float</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/storage.html#TypedStorage.complex_float"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/storage.py#L1367"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.TypedStorage.complex_float" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">complex_float</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/storage.html#TypedStorage.complex_float"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/storage.py#L1367"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.TypedStorage.complex_float" title="Permalink to this definition">¶</a></dt>
 <dd><p>Casts this storage to complex float type.</p>
 </dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.TypedStorage.copy_">
-<span class="sig-name descname"><span class="pre">copy_</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">source</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">non_blocking</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/storage.html#TypedStorage.copy_"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/storage.py#L1011"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.TypedStorage.copy_" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">copy_</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">source</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">non_blocking</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/storage.html#TypedStorage.copy_"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/storage.py#L1011"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.TypedStorage.copy_" title="Permalink to this definition">¶</a></dt>
 <dd><dl class="field-list simple">
 </dl>
 </dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.TypedStorage.cpu">
-<span class="sig-name descname"><span class="pre">cpu</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/storage.html#TypedStorage.cpu"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/storage.py#L1145"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.TypedStorage.cpu" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">cpu</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/storage.html#TypedStorage.cpu"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/storage.py#L1145"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.TypedStorage.cpu" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return a CPU copy of this storage if it’s not already on the CPU.</p>
 </dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.TypedStorage.cuda">
-<span class="sig-name descname"><span class="pre">cuda</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">non_blocking</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/storage.html#TypedStorage.cuda"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/storage.py#L1044"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.TypedStorage.cuda" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">cuda</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">non_blocking</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/storage.html#TypedStorage.cuda"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/storage.py#L1044"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.TypedStorage.cuda" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns a copy of this object in CUDA memory.</p>
 <p>If this object is already in CUDA memory and on the correct device, then
 no copy is performed and the original object is returned.</p>
@@ -1213,7 +1213,7 @@ <h2>Legacy Typed Storage<a class="headerlink" href="#legacy-typed-storage" title
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.TypedStorage.data_ptr">
-<span class="sig-name descname"><span class="pre">data_ptr</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/storage.html#TypedStorage.data_ptr"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/storage.py#L1235"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.TypedStorage.data_ptr" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">data_ptr</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/storage.html#TypedStorage.data_ptr"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/storage.py#L1235"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.TypedStorage.data_ptr" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py property">
@@ -1223,7 +1223,7 @@ <h2>Legacy Typed Storage<a class="headerlink" href="#legacy-typed-storage" title
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.TypedStorage.double">
-<span class="sig-name descname"><span class="pre">double</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/storage.html#TypedStorage.double"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/storage.py#L1312"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.TypedStorage.double" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">double</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/storage.html#TypedStorage.double"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/storage.py#L1312"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.TypedStorage.double" title="Permalink to this definition">¶</a></dt>
 <dd><p>Casts this storage to double type.</p>
 </dd></dl>
 
@@ -1234,7 +1234,7 @@ <h2>Legacy Typed Storage<a class="headerlink" href="#legacy-typed-storage" title
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.TypedStorage.element_size">
-<span class="sig-name descname"><span class="pre">element_size</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/storage.html#TypedStorage.element_size"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/storage.py#L1087"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.TypedStorage.element_size" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">element_size</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/storage.html#TypedStorage.element_size"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/storage.py#L1087"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.TypedStorage.element_size" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py property">
@@ -1246,47 +1246,47 @@ <h2>Legacy Typed Storage<a class="headerlink" href="#legacy-typed-storage" title
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.TypedStorage.fill_">
-<span class="sig-name descname"><span class="pre">fill_</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/storage.html#TypedStorage.fill_"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/storage.py#L678"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.TypedStorage.fill_" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">fill_</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/storage.html#TypedStorage.fill_"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/storage.py#L678"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.TypedStorage.fill_" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.TypedStorage.float">
-<span class="sig-name descname"><span class="pre">float</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/storage.html#TypedStorage.float"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/storage.py#L1317"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.TypedStorage.float" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">float</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/storage.html#TypedStorage.float"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/storage.py#L1317"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.TypedStorage.float" title="Permalink to this definition">¶</a></dt>
 <dd><p>Casts this storage to float type.</p>
 </dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.TypedStorage.float8_e4m3fn">
-<span class="sig-name descname"><span class="pre">float8_e4m3fn</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/storage.html#TypedStorage.float8_e4m3fn"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/storage.py#L1377"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.TypedStorage.float8_e4m3fn" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">float8_e4m3fn</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/storage.html#TypedStorage.float8_e4m3fn"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/storage.py#L1377"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.TypedStorage.float8_e4m3fn" title="Permalink to this definition">¶</a></dt>
 <dd><p>Casts this storage to float8_e4m3fn type</p>
 </dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.TypedStorage.float8_e4m3fnuz">
-<span class="sig-name descname"><span class="pre">float8_e4m3fnuz</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/storage.html#TypedStorage.float8_e4m3fnuz"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/storage.py#L1387"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.TypedStorage.float8_e4m3fnuz" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">float8_e4m3fnuz</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/storage.html#TypedStorage.float8_e4m3fnuz"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/storage.py#L1387"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.TypedStorage.float8_e4m3fnuz" title="Permalink to this definition">¶</a></dt>
 <dd><p>Casts this storage to float8_e4m3fnuz type</p>
 </dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.TypedStorage.float8_e5m2">
-<span class="sig-name descname"><span class="pre">float8_e5m2</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/storage.html#TypedStorage.float8_e5m2"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/storage.py#L1372"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.TypedStorage.float8_e5m2" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">float8_e5m2</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/storage.html#TypedStorage.float8_e5m2"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/storage.py#L1372"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.TypedStorage.float8_e5m2" title="Permalink to this definition">¶</a></dt>
 <dd><p>Casts this storage to float8_e5m2 type</p>
 </dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.TypedStorage.float8_e5m2fnuz">
-<span class="sig-name descname"><span class="pre">float8_e5m2fnuz</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/storage.html#TypedStorage.float8_e5m2fnuz"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/storage.py#L1382"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.TypedStorage.float8_e5m2fnuz" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">float8_e5m2fnuz</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/storage.html#TypedStorage.float8_e5m2fnuz"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/storage.py#L1382"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.TypedStorage.float8_e5m2fnuz" title="Permalink to this definition">¶</a></dt>
 <dd><p>Casts this storage to float8_e5m2fnuz type</p>
 </dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.TypedStorage.from_buffer">
-<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_buffer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/storage.html#TypedStorage.from_buffer"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/storage.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.TypedStorage.from_buffer" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_buffer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/storage.html#TypedStorage.from_buffer"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/storage.py#L1262"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.TypedStorage.from_buffer" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.TypedStorage.from_file">
-<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_file</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">filename</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shared</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">Storage</span></span></span><a class="reference internal" href="_modules/torch/storage.html#TypedStorage.from_file"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/storage.py#L1392"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.TypedStorage.from_file" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_file</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">filename</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shared</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">Storage</span></span></span><a class="reference internal" href="_modules/torch/storage.html#TypedStorage.from_file"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/storage.py#L1392"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.TypedStorage.from_file" title="Permalink to this definition">¶</a></dt>
 <dd><p>Creates a CPU storage backed by a memory-mapped file.</p>
 <p>If <code class="docutils literal notranslate"><span class="pre">shared</span></code> is <code class="docutils literal notranslate"><span class="pre">True</span></code>, then memory is shared between all processes.
 All changes are written to the file. If <code class="docutils literal notranslate"><span class="pre">shared</span></code> is <code class="docutils literal notranslate"><span class="pre">False</span></code>, then the changes on
@@ -1309,7 +1309,7 @@ <h2>Legacy Typed Storage<a class="headerlink" href="#legacy-typed-storage" title
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.TypedStorage.get_device">
-<span class="sig-name descname"><span class="pre">get_device</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/storage.html#TypedStorage.get_device"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/storage.py#L1095"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.TypedStorage.get_device" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">get_device</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/storage.html#TypedStorage.get_device"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/storage.py#L1095"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.TypedStorage.get_device" title="Permalink to this definition">¶</a></dt>
 <dd><dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
 <dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.13)">int</a></p>
@@ -1319,13 +1319,13 @@ <h2>Legacy Typed Storage<a class="headerlink" href="#legacy-typed-storage" title
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.TypedStorage.half">
-<span class="sig-name descname"><span class="pre">half</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/storage.html#TypedStorage.half"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/storage.py#L1322"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.TypedStorage.half" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">half</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/storage.html#TypedStorage.half"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/storage.py#L1322"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.TypedStorage.half" title="Permalink to this definition">¶</a></dt>
 <dd><p>Casts this storage to half type.</p>
 </dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.TypedStorage.hpu">
-<span class="sig-name descname"><span class="pre">hpu</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">non_blocking</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/storage.html#TypedStorage.hpu"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/storage.py#L1057"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.TypedStorage.hpu" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">hpu</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">non_blocking</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/storage.html#TypedStorage.hpu"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/storage.py#L1057"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.TypedStorage.hpu" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns a copy of this object in HPU memory.</p>
 <p>If this object is already in HPU memory and on the correct device, then
 no copy is performed and the original object is returned.</p>
@@ -1346,7 +1346,7 @@ <h2>Legacy Typed Storage<a class="headerlink" href="#legacy-typed-storage" title
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.TypedStorage.int">
-<span class="sig-name descname"><span class="pre">int</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/storage.html#TypedStorage.int"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/storage.py#L1332"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.TypedStorage.int" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">int</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/storage.html#TypedStorage.int"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/storage.py#L1332"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.TypedStorage.int" title="Permalink to this definition">¶</a></dt>
 <dd><p>Casts this storage to int type.</p>
 </dd></dl>
 
@@ -1362,7 +1362,7 @@ <h2>Legacy Typed Storage<a class="headerlink" href="#legacy-typed-storage" title
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.TypedStorage.is_pinned">
-<span class="sig-name descname"><span class="pre">is_pinned</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'cuda'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/storage.html#TypedStorage.is_pinned"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/storage.py#L1150"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.TypedStorage.is_pinned" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">is_pinned</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'cuda'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/storage.html#TypedStorage.is_pinned"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/storage.py#L1150"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.TypedStorage.is_pinned" title="Permalink to this definition">¶</a></dt>
 <dd><p>Determine whether the CPU TypedStorage is already pinned on device.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -1377,7 +1377,7 @@ <h2>Legacy Typed Storage<a class="headerlink" href="#legacy-typed-storage" title
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.TypedStorage.is_shared">
-<span class="sig-name descname"><span class="pre">is_shared</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/storage.html#TypedStorage.is_shared"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/storage.py#L1437"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.TypedStorage.is_shared" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">is_shared</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/storage.html#TypedStorage.is_shared"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/storage.py#L1437"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.TypedStorage.is_shared" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py attribute">
@@ -1387,23 +1387,23 @@ <h2>Legacy Typed Storage<a class="headerlink" href="#legacy-typed-storage" title
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.TypedStorage.long">
-<span class="sig-name descname"><span class="pre">long</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/storage.html#TypedStorage.long"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/storage.py#L1327"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.TypedStorage.long" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">long</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/storage.html#TypedStorage.long"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/storage.py#L1327"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.TypedStorage.long" title="Permalink to this definition">¶</a></dt>
 <dd><p>Casts this storage to long type.</p>
 </dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.TypedStorage.nbytes">
-<span class="sig-name descname"><span class="pre">nbytes</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/storage.html#TypedStorage.nbytes"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/storage.py#L1019"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.TypedStorage.nbytes" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">nbytes</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/storage.html#TypedStorage.nbytes"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/storage.py#L1019"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.TypedStorage.nbytes" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.TypedStorage.pickle_storage_type">
-<span class="sig-name descname"><span class="pre">pickle_storage_type</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/storage.html#TypedStorage.pickle_storage_type"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/storage.py#L1219"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.TypedStorage.pickle_storage_type" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">pickle_storage_type</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/storage.html#TypedStorage.pickle_storage_type"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/storage.py#L1219"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.TypedStorage.pickle_storage_type" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.TypedStorage.pin_memory">
-<span class="sig-name descname"><span class="pre">pin_memory</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'cuda'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/storage.html#TypedStorage.pin_memory"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/storage.py#L1163"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.TypedStorage.pin_memory" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">pin_memory</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'cuda'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/storage.html#TypedStorage.pin_memory"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/storage.py#L1163"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.TypedStorage.pin_memory" title="Permalink to this definition">¶</a></dt>
 <dd><p>Copy the CPU TypedStorage to pinned memory, if it’s not already pinned.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -1418,34 +1418,34 @@ <h2>Legacy Typed Storage<a class="headerlink" href="#legacy-typed-storage" title
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.TypedStorage.resizable">
-<span class="sig-name descname"><span class="pre">resizable</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/storage.html#TypedStorage.resizable"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/storage.py#L1243"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.TypedStorage.resizable" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">resizable</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/storage.html#TypedStorage.resizable"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/storage.py#L1243"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.TypedStorage.resizable" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.TypedStorage.resize_">
-<span class="sig-name descname"><span class="pre">resize_</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">size</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/storage.html#TypedStorage.resize_"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/storage.py#L1247"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.TypedStorage.resize_" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">resize_</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">size</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/storage.html#TypedStorage.resize_"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/storage.py#L1247"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.TypedStorage.resize_" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.TypedStorage.share_memory_">
-<span class="sig-name descname"><span class="pre">share_memory_</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/storage.html#TypedStorage.share_memory_"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/storage.py#L1178"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.TypedStorage.share_memory_" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">share_memory_</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/storage.html#TypedStorage.share_memory_"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/storage.py#L1178"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.TypedStorage.share_memory_" title="Permalink to this definition">¶</a></dt>
 <dd><p>See <a class="reference internal" href="#torch.UntypedStorage.share_memory_" title="torch.UntypedStorage.share_memory_"><code class="xref py py-meth docutils literal notranslate"><span class="pre">torch.UntypedStorage.share_memory_()</span></code></a></p>
 </dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.TypedStorage.short">
-<span class="sig-name descname"><span class="pre">short</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/storage.html#TypedStorage.short"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/storage.py#L1337"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.TypedStorage.short" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">short</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/storage.html#TypedStorage.short"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/storage.py#L1337"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.TypedStorage.short" title="Permalink to this definition">¶</a></dt>
 <dd><p>Casts this storage to short type.</p>
 </dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.TypedStorage.size">
-<span class="sig-name descname"><span class="pre">size</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/storage.html#TypedStorage.size"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/storage.py#L1209"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.TypedStorage.size" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">size</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/storage.html#TypedStorage.size"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/storage.py#L1209"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.TypedStorage.size" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.TypedStorage.to">
-<span class="sig-name descname"><span class="pre">to</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">non_blocking</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/storage.html#TypedStorage.to"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/storage.py#L1070"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.TypedStorage.to" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">to</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">non_blocking</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/storage.html#TypedStorage.to"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/storage.py#L1070"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.TypedStorage.to" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns a copy of this object in device memory.</p>
 <p>If this object is already on the correct device, then no copy is performed
 and the original object is returned.</p>
@@ -1466,13 +1466,13 @@ <h2>Legacy Typed Storage<a class="headerlink" href="#legacy-typed-storage" title
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.TypedStorage.tolist">
-<span class="sig-name descname"><span class="pre">tolist</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/storage.html#TypedStorage.tolist"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/storage.py#L1140"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.TypedStorage.tolist" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">tolist</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/storage.html#TypedStorage.tolist"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/storage.py#L1140"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.TypedStorage.tolist" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return a list containing the elements of this storage.</p>
 </dd></dl>
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.TypedStorage.type">
-<span class="sig-name descname"><span class="pre">type</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">non_blocking</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/storage.html#TypedStorage.type"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/storage.py#L1027"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.TypedStorage.type" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">type</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">non_blocking</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/storage.html#TypedStorage.type"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/storage.py#L1027"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.TypedStorage.type" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns the type if <cite>dtype</cite> is not provided, else casts this object to
 the specified type.</p>
 <p>If this is already of the correct type, no copy is performed and the
@@ -1497,7 +1497,7 @@ <h2>Legacy Typed Storage<a class="headerlink" href="#legacy-typed-storage" title
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.TypedStorage.untyped">
-<span class="sig-name descname"><span class="pre">untyped</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/storage.html#TypedStorage.untyped"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/storage.py#L876"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.TypedStorage.untyped" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">untyped</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/storage.html#TypedStorage.untyped"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/storage.py#L876"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.TypedStorage.untyped" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return the internal <a class="reference internal" href="#torch.UntypedStorage" title="torch.UntypedStorage"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.UntypedStorage</span></code></a>.</p>
 </dd></dl>
 
@@ -1505,7 +1505,7 @@ <h2>Legacy Typed Storage<a class="headerlink" href="#legacy-typed-storage" title
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.DoubleStorage">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">DoubleStorage</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">wrap_storage</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_internal</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch.html#DoubleStorage"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/__init__.py#L1831"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.DoubleStorage" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">DoubleStorage</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">wrap_storage</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_internal</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch.html#DoubleStorage"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/__init__.py#L1831"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.DoubleStorage" title="Permalink to this definition">¶</a></dt>
 <dd><dl class="field-list simple">
 </dl>
 <dl class="py attribute">
@@ -1517,7 +1517,7 @@ <h2>Legacy Typed Storage<a class="headerlink" href="#legacy-typed-storage" title
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.FloatStorage">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">FloatStorage</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">wrap_storage</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_internal</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch.html#FloatStorage"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/__init__.py#L1842"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.FloatStorage" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">FloatStorage</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">wrap_storage</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_internal</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch.html#FloatStorage"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/__init__.py#L1842"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.FloatStorage" title="Permalink to this definition">¶</a></dt>
 <dd><dl class="field-list simple">
 </dl>
 <dl class="py attribute">
@@ -1529,7 +1529,7 @@ <h2>Legacy Typed Storage<a class="headerlink" href="#legacy-typed-storage" title
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.HalfStorage">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">HalfStorage</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">wrap_storage</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_internal</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch.html#HalfStorage"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/__init__.py#L1853"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.HalfStorage" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">HalfStorage</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">wrap_storage</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_internal</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch.html#HalfStorage"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/__init__.py#L1853"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.HalfStorage" title="Permalink to this definition">¶</a></dt>
 <dd><dl class="field-list simple">
 </dl>
 <dl class="py attribute">
@@ -1541,7 +1541,7 @@ <h2>Legacy Typed Storage<a class="headerlink" href="#legacy-typed-storage" title
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.LongStorage">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">LongStorage</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">wrap_storage</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_internal</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch.html#LongStorage"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/__init__.py#L1864"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.LongStorage" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">LongStorage</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">wrap_storage</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_internal</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch.html#LongStorage"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/__init__.py#L1864"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.LongStorage" title="Permalink to this definition">¶</a></dt>
 <dd><dl class="field-list simple">
 </dl>
 <dl class="py attribute">
@@ -1553,7 +1553,7 @@ <h2>Legacy Typed Storage<a class="headerlink" href="#legacy-typed-storage" title
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.IntStorage">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">IntStorage</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">wrap_storage</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_internal</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch.html#IntStorage"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/__init__.py#L1875"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.IntStorage" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">IntStorage</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">wrap_storage</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_internal</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch.html#IntStorage"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/__init__.py#L1875"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.IntStorage" title="Permalink to this definition">¶</a></dt>
 <dd><dl class="field-list simple">
 </dl>
 <dl class="py attribute">
@@ -1565,7 +1565,7 @@ <h2>Legacy Typed Storage<a class="headerlink" href="#legacy-typed-storage" title
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ShortStorage">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">ShortStorage</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">wrap_storage</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_internal</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch.html#ShortStorage"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/__init__.py#L1886"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ShortStorage" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">ShortStorage</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">wrap_storage</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_internal</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch.html#ShortStorage"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/__init__.py#L1886"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ShortStorage" title="Permalink to this definition">¶</a></dt>
 <dd><dl class="field-list simple">
 </dl>
 <dl class="py attribute">
@@ -1577,7 +1577,7 @@ <h2>Legacy Typed Storage<a class="headerlink" href="#legacy-typed-storage" title
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.CharStorage">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">CharStorage</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">wrap_storage</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_internal</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch.html#CharStorage"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/__init__.py#L1897"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.CharStorage" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">CharStorage</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">wrap_storage</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_internal</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch.html#CharStorage"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/__init__.py#L1897"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.CharStorage" title="Permalink to this definition">¶</a></dt>
 <dd><dl class="field-list simple">
 </dl>
 <dl class="py attribute">
@@ -1589,7 +1589,7 @@ <h2>Legacy Typed Storage<a class="headerlink" href="#legacy-typed-storage" title
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ByteStorage">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">ByteStorage</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">wrap_storage</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_internal</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch.html#ByteStorage"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/__init__.py#L1820"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ByteStorage" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">ByteStorage</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">wrap_storage</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_internal</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch.html#ByteStorage"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/__init__.py#L1820"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ByteStorage" title="Permalink to this definition">¶</a></dt>
 <dd><dl class="field-list simple">
 </dl>
 <dl class="py attribute">
@@ -1601,7 +1601,7 @@ <h2>Legacy Typed Storage<a class="headerlink" href="#legacy-typed-storage" title
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.BoolStorage">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">BoolStorage</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">wrap_storage</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_internal</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch.html#BoolStorage"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/__init__.py#L1908"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.BoolStorage" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">BoolStorage</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">wrap_storage</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_internal</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch.html#BoolStorage"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/__init__.py#L1908"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.BoolStorage" title="Permalink to this definition">¶</a></dt>
 <dd><dl class="field-list simple">
 </dl>
 <dl class="py attribute">
@@ -1613,7 +1613,7 @@ <h2>Legacy Typed Storage<a class="headerlink" href="#legacy-typed-storage" title
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.BFloat16Storage">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">BFloat16Storage</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">wrap_storage</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_internal</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch.html#BFloat16Storage"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/__init__.py#L1919"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.BFloat16Storage" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">BFloat16Storage</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">wrap_storage</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_internal</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch.html#BFloat16Storage"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/__init__.py#L1919"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.BFloat16Storage" title="Permalink to this definition">¶</a></dt>
 <dd><dl class="field-list simple">
 </dl>
 <dl class="py attribute">
@@ -1625,7 +1625,7 @@ <h2>Legacy Typed Storage<a class="headerlink" href="#legacy-typed-storage" title
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ComplexDoubleStorage">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">ComplexDoubleStorage</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">wrap_storage</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_internal</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch.html#ComplexDoubleStorage"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/__init__.py#L1930"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ComplexDoubleStorage" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">ComplexDoubleStorage</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">wrap_storage</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_internal</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch.html#ComplexDoubleStorage"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/__init__.py#L1930"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ComplexDoubleStorage" title="Permalink to this definition">¶</a></dt>
 <dd><dl class="field-list simple">
 </dl>
 <dl class="py attribute">
@@ -1637,7 +1637,7 @@ <h2>Legacy Typed Storage<a class="headerlink" href="#legacy-typed-storage" title
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ComplexFloatStorage">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">ComplexFloatStorage</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">wrap_storage</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_internal</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch.html#ComplexFloatStorage"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/__init__.py#L1941"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ComplexFloatStorage" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">ComplexFloatStorage</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">wrap_storage</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_internal</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch.html#ComplexFloatStorage"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/__init__.py#L1941"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ComplexFloatStorage" title="Permalink to this definition">¶</a></dt>
 <dd><dl class="field-list simple">
 </dl>
 <dl class="py attribute">
@@ -1649,7 +1649,7 @@ <h2>Legacy Typed Storage<a class="headerlink" href="#legacy-typed-storage" title
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.QUInt8Storage">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">QUInt8Storage</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">wrap_storage</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_internal</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch.html#QUInt8Storage"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/__init__.py#L1952"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.QUInt8Storage" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">QUInt8Storage</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">wrap_storage</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_internal</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch.html#QUInt8Storage"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/__init__.py#L1952"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.QUInt8Storage" title="Permalink to this definition">¶</a></dt>
 <dd><dl class="field-list simple">
 </dl>
 <dl class="py attribute">
@@ -1661,7 +1661,7 @@ <h2>Legacy Typed Storage<a class="headerlink" href="#legacy-typed-storage" title
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.QInt8Storage">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">QInt8Storage</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">wrap_storage</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_internal</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch.html#QInt8Storage"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/__init__.py#L1963"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.QInt8Storage" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">QInt8Storage</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">wrap_storage</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_internal</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch.html#QInt8Storage"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/__init__.py#L1963"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.QInt8Storage" title="Permalink to this definition">¶</a></dt>
 <dd><dl class="field-list simple">
 </dl>
 <dl class="py attribute">
@@ -1673,7 +1673,7 @@ <h2>Legacy Typed Storage<a class="headerlink" href="#legacy-typed-storage" title
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.QInt32Storage">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">QInt32Storage</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">wrap_storage</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_internal</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch.html#QInt32Storage"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/__init__.py#L1974"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.QInt32Storage" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">QInt32Storage</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">wrap_storage</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_internal</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch.html#QInt32Storage"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/__init__.py#L1974"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.QInt32Storage" title="Permalink to this definition">¶</a></dt>
 <dd><dl class="field-list simple">
 </dl>
 <dl class="py attribute">
@@ -1685,7 +1685,7 @@ <h2>Legacy Typed Storage<a class="headerlink" href="#legacy-typed-storage" title
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.QUInt4x2Storage">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">QUInt4x2Storage</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">wrap_storage</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_internal</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch.html#QUInt4x2Storage"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/__init__.py#L1985"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.QUInt4x2Storage" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">QUInt4x2Storage</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">wrap_storage</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_internal</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch.html#QUInt4x2Storage"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/__init__.py#L1985"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.QUInt4x2Storage" title="Permalink to this definition">¶</a></dt>
 <dd><dl class="field-list simple">
 </dl>
 <dl class="py attribute">
@@ -1697,7 +1697,7 @@ <h2>Legacy Typed Storage<a class="headerlink" href="#legacy-typed-storage" title
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.QUInt2x4Storage">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">QUInt2x4Storage</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">wrap_storage</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_internal</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch.html#QUInt2x4Storage"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/__init__.py#L1996"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.QUInt2x4Storage" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">QUInt2x4Storage</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">wrap_storage</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_internal</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch.html#QUInt2x4Storage"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/__init__.py#L1996"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.QUInt2x4Storage" title="Permalink to this definition">¶</a></dt>
 <dd><dl class="field-list simple">
 </dl>
 <dl class="py attribute">
diff --git a/main/tensor_attributes.html b/main/tensor_attributes.html
index 4d8a379bb06..a073080a9e9 100644
--- a/main/tensor_attributes.html
+++ b/main/tensor_attributes.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/tensor_view.html b/main/tensor_view.html
index 2d90873f258..ed6ecbb5a6e 100644
--- a/main/tensor_view.html
+++ b/main/tensor_view.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/tensorboard.html b/main/tensorboard.html
index b0eafcd283e..cee1287f1e4 100644
--- a/main/tensorboard.html
+++ b/main/tensorboard.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -669,7 +669,7 @@
 </div>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.utils.tensorboard.writer.SummaryWriter">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.utils.tensorboard.writer.</span></span><span class="sig-name descname"><span class="pre">SummaryWriter</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">log_dir</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">comment</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">purge_step</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_queue</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">flush_secs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">120</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">filename_suffix</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/tensorboard/writer.html#SummaryWriter"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/utils/tensorboard/writer.py#L172"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.tensorboard.writer.SummaryWriter" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.utils.tensorboard.writer.</span></span><span class="sig-name descname"><span class="pre">SummaryWriter</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">log_dir</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">comment</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">purge_step</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_queue</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">flush_secs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">120</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">filename_suffix</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/tensorboard/writer.html#SummaryWriter"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/utils/tensorboard/writer.py#L172"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.tensorboard.writer.SummaryWriter" title="Permalink to this definition">¶</a></dt>
 <dd><p>Writes entries directly to event files in the log_dir to be consumed by TensorBoard.</p>
 <p>The <cite>SummaryWriter</cite> class provides a high-level API to create an event file
 in a given directory and add summaries and events to it. The class updates the
@@ -678,7 +678,7 @@
 training.</p>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.utils.tensorboard.writer.SummaryWriter.__init__">
-<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">log_dir</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">comment</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">purge_step</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_queue</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">flush_secs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">120</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">filename_suffix</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/tensorboard/writer.html#SummaryWriter.__init__"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/utils/tensorboard/writer.py#L182"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.tensorboard.writer.SummaryWriter.__init__" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">log_dir</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">comment</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">purge_step</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_queue</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">flush_secs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">120</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">filename_suffix</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/tensorboard/writer.html#SummaryWriter.__init__"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/utils/tensorboard/writer.py#L182"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.tensorboard.writer.SummaryWriter.__init__" title="Permalink to this definition">¶</a></dt>
 <dd><p>Create a <cite>SummaryWriter</cite> that will write out events and summaries to the event file.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -725,7 +725,7 @@
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.utils.tensorboard.writer.SummaryWriter.add_scalar">
-<span class="sig-name descname"><span class="pre">add_scalar</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tag</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scalar_value</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">global_step</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">walltime</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">new_style</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">double_precision</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/tensorboard/writer.html#SummaryWriter.add_scalar"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/utils/tensorboard/writer.py#L342"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.tensorboard.writer.SummaryWriter.add_scalar" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">add_scalar</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tag</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scalar_value</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">global_step</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">walltime</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">new_style</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">double_precision</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/tensorboard/writer.html#SummaryWriter.add_scalar"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/utils/tensorboard/writer.py#L342"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.tensorboard.writer.SummaryWriter.add_scalar" title="Permalink to this definition">¶</a></dt>
 <dd><p>Add scalar data to summary.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -755,7 +755,7 @@
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.utils.tensorboard.writer.SummaryWriter.add_scalars">
-<span class="sig-name descname"><span class="pre">add_scalars</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">main_tag</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tag_scalar_dict</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">global_step</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">walltime</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/tensorboard/writer.html#SummaryWriter.add_scalars"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/utils/tensorboard/writer.py#L383"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.tensorboard.writer.SummaryWriter.add_scalars" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">add_scalars</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">main_tag</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tag_scalar_dict</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">global_step</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">walltime</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/tensorboard/writer.html#SummaryWriter.add_scalars"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/utils/tensorboard/writer.py#L383"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.tensorboard.writer.SummaryWriter.add_scalars" title="Permalink to this definition">¶</a></dt>
 <dd><p>Add many scalar data to summary.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -787,7 +787,7 @@
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.utils.tensorboard.writer.SummaryWriter.add_histogram">
-<span class="sig-name descname"><span class="pre">add_histogram</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tag</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">values</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">global_step</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bins</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'tensorflow'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">walltime</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_bins</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/tensorboard/writer.html#SummaryWriter.add_histogram"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/utils/tensorboard/writer.py#L459"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.tensorboard.writer.SummaryWriter.add_histogram" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">add_histogram</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tag</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">values</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">global_step</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bins</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'tensorflow'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">walltime</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_bins</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/tensorboard/writer.html#SummaryWriter.add_histogram"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/utils/tensorboard/writer.py#L459"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.tensorboard.writer.SummaryWriter.add_histogram" title="Permalink to this definition">¶</a></dt>
 <dd><p>Add histogram to summary.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -818,7 +818,7 @@
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.utils.tensorboard.writer.SummaryWriter.add_image">
-<span class="sig-name descname"><span class="pre">add_image</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tag</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">img_tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">global_step</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">walltime</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dataformats</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'CHW'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/tensorboard/writer.html#SummaryWriter.add_image"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/utils/tensorboard/writer.py#L577"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.tensorboard.writer.SummaryWriter.add_image" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">add_image</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tag</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">img_tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">global_step</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">walltime</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dataformats</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'CHW'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/tensorboard/writer.html#SummaryWriter.add_image"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/utils/tensorboard/writer.py#L577"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.tensorboard.writer.SummaryWriter.add_image" title="Permalink to this definition">¶</a></dt>
 <dd><p>Add image data to summary.</p>
 <p>Note that this requires the <code class="docutils literal notranslate"><span class="pre">pillow</span></code> package.</p>
 <dl class="field-list simple">
@@ -866,7 +866,7 @@
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.utils.tensorboard.writer.SummaryWriter.add_images">
-<span class="sig-name descname"><span class="pre">add_images</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tag</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">img_tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">global_step</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">walltime</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dataformats</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'NCHW'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/tensorboard/writer.html#SummaryWriter.add_images"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/utils/tensorboard/writer.py#L628"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.tensorboard.writer.SummaryWriter.add_images" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">add_images</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tag</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">img_tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">global_step</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">walltime</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dataformats</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'NCHW'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/tensorboard/writer.html#SummaryWriter.add_images"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/utils/tensorboard/writer.py#L628"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.tensorboard.writer.SummaryWriter.add_images" title="Permalink to this definition">¶</a></dt>
 <dd><p>Add batched image data to summary.</p>
 <p>Note that this requires the <code class="docutils literal notranslate"><span class="pre">pillow</span></code> package.</p>
 <dl class="field-list simple">
@@ -907,7 +907,7 @@
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.utils.tensorboard.writer.SummaryWriter.add_figure">
-<span class="sig-name descname"><span class="pre">add_figure</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tag</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">figure</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">global_step</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">close</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">walltime</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/tensorboard/writer.html#SummaryWriter.add_figure"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/utils/tensorboard/writer.py#L723"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.tensorboard.writer.SummaryWriter.add_figure" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">add_figure</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tag</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">figure</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">global_step</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">close</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">walltime</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/tensorboard/writer.html#SummaryWriter.add_figure"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/utils/tensorboard/writer.py#L723"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.tensorboard.writer.SummaryWriter.add_figure" title="Permalink to this definition">¶</a></dt>
 <dd><p>Render matplotlib figure into an image and add it to summary.</p>
 <p>Note that this requires the <code class="docutils literal notranslate"><span class="pre">matplotlib</span></code> package.</p>
 <dl class="field-list simple">
@@ -926,7 +926,7 @@
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.utils.tensorboard.writer.SummaryWriter.add_video">
-<span class="sig-name descname"><span class="pre">add_video</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tag</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">vid_tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">global_step</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">4</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">walltime</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/tensorboard/writer.html#SummaryWriter.add_video"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/utils/tensorboard/writer.py#L761"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.tensorboard.writer.SummaryWriter.add_video" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">add_video</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tag</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">vid_tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">global_step</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">4</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">walltime</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/tensorboard/writer.html#SummaryWriter.add_video"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/utils/tensorboard/writer.py#L761"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.tensorboard.writer.SummaryWriter.add_video" title="Permalink to this definition">¶</a></dt>
 <dd><p>Add video data to summary.</p>
 <p>Note that this requires the <code class="docutils literal notranslate"><span class="pre">moviepy</span></code> package.</p>
 <dl class="field-list simple">
@@ -949,7 +949,7 @@
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.utils.tensorboard.writer.SummaryWriter.add_audio">
-<span class="sig-name descname"><span class="pre">add_audio</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tag</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">snd_tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">global_step</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_rate</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">44100</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">walltime</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/tensorboard/writer.html#SummaryWriter.add_audio"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/utils/tensorboard/writer.py#L781"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.tensorboard.writer.SummaryWriter.add_audio" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">add_audio</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tag</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">snd_tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">global_step</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sample_rate</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">44100</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">walltime</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/tensorboard/writer.html#SummaryWriter.add_audio"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/utils/tensorboard/writer.py#L781"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.tensorboard.writer.SummaryWriter.add_audio" title="Permalink to this definition">¶</a></dt>
 <dd><p>Add audio data to summary.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -971,7 +971,7 @@
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.utils.tensorboard.writer.SummaryWriter.add_text">
-<span class="sig-name descname"><span class="pre">add_text</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tag</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">text_string</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">global_step</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">walltime</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/tensorboard/writer.html#SummaryWriter.add_text"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/utils/tensorboard/writer.py#L801"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.tensorboard.writer.SummaryWriter.add_text" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">add_text</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tag</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">text_string</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">global_step</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">walltime</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/tensorboard/writer.html#SummaryWriter.add_text"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/utils/tensorboard/writer.py#L801"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.tensorboard.writer.SummaryWriter.add_text" title="Permalink to this definition">¶</a></dt>
 <dd><p>Add text data to summary.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -993,7 +993,7 @@
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.utils.tensorboard.writer.SummaryWriter.add_graph">
-<span class="sig-name descname"><span class="pre">add_graph</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_to_model</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_strict_trace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/tensorboard/writer.html#SummaryWriter.add_graph"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/utils/tensorboard/writer.py#L824"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.tensorboard.writer.SummaryWriter.add_graph" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">add_graph</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_to_model</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_strict_trace</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/tensorboard/writer.html#SummaryWriter.add_graph"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/utils/tensorboard/writer.py#L824"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.tensorboard.writer.SummaryWriter.add_graph" title="Permalink to this definition">¶</a></dt>
 <dd><p>Add graph data to summary.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -1012,7 +1012,7 @@
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.utils.tensorboard.writer.SummaryWriter.add_embedding">
-<span class="sig-name descname"><span class="pre">add_embedding</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mat</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">metadata</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">label_img</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">global_step</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tag</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'default'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">metadata_header</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/tensorboard/writer.html#SummaryWriter.add_embedding"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/utils/tensorboard/writer.py#L853"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.tensorboard.writer.SummaryWriter.add_embedding" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">add_embedding</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mat</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">metadata</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">label_img</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">global_step</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tag</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'default'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">metadata_header</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/tensorboard/writer.html#SummaryWriter.add_embedding"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/utils/tensorboard/writer.py#L853"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.tensorboard.writer.SummaryWriter.add_embedding" title="Permalink to this definition">¶</a></dt>
 <dd><p>Add embedding projector data to summary.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -1061,7 +1061,7 @@
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.utils.tensorboard.writer.SummaryWriter.add_pr_curve">
-<span class="sig-name descname"><span class="pre">add_pr_curve</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tag</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">labels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">predictions</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">global_step</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_thresholds</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">127</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weights</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">walltime</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/tensorboard/writer.html#SummaryWriter.add_pr_curve"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/utils/tensorboard/writer.py#L958"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.tensorboard.writer.SummaryWriter.add_pr_curve" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">add_pr_curve</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tag</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">labels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">predictions</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">global_step</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_thresholds</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">127</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">weights</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">walltime</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/tensorboard/writer.html#SummaryWriter.add_pr_curve"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/utils/tensorboard/writer.py#L958"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.tensorboard.writer.SummaryWriter.add_pr_curve" title="Permalink to this definition">¶</a></dt>
 <dd><p>Add precision recall curve.</p>
 <p>Plotting a precision-recall curve lets you understand your model’s
 performance under different threshold settings. With this function,
@@ -1096,7 +1096,7 @@
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.utils.tensorboard.writer.SummaryWriter.add_custom_scalars">
-<span class="sig-name descname"><span class="pre">add_custom_scalars</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">layout</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/tensorboard/writer.html#SummaryWriter.add_custom_scalars"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/utils/tensorboard/writer.py#L1094"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.tensorboard.writer.SummaryWriter.add_custom_scalars" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">add_custom_scalars</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">layout</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/tensorboard/writer.html#SummaryWriter.add_custom_scalars"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/utils/tensorboard/writer.py#L1094"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.tensorboard.writer.SummaryWriter.add_custom_scalars" title="Permalink to this definition">¶</a></dt>
 <dd><p>Create special chart by collecting charts tags in ‘scalars’.</p>
 <p>NOTE: This function can only be called once for each SummaryWriter() object.</p>
 <p>Because it only provides metadata to tensorboard, the function can be called before or after the training loop.</p>
@@ -1120,7 +1120,7 @@
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.utils.tensorboard.writer.SummaryWriter.add_mesh">
-<span class="sig-name descname"><span class="pre">add_mesh</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tag</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">vertices</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">colors</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">faces</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">config_dict</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">global_step</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">walltime</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/tensorboard/writer.html#SummaryWriter.add_mesh"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/utils/tensorboard/writer.py#L1118"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.tensorboard.writer.SummaryWriter.add_mesh" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">add_mesh</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tag</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">vertices</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">colors</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">faces</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">config_dict</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">global_step</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">walltime</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/tensorboard/writer.html#SummaryWriter.add_mesh"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/utils/tensorboard/writer.py#L1118"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.tensorboard.writer.SummaryWriter.add_mesh" title="Permalink to this definition">¶</a></dt>
 <dd><p>Add meshes or 3D point clouds to TensorBoard.</p>
 <p>The visualization is based on Three.js,
 so it allows users to interact with the rendered object. Besides the basic definitions
@@ -1178,7 +1178,7 @@
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.utils.tensorboard.writer.SummaryWriter.add_hparams">
-<span class="sig-name descname"><span class="pre">add_hparams</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hparam_dict</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">metric_dict</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">hparam_domain_discrete</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">run_name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">global_step</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/tensorboard/writer.html#SummaryWriter.add_hparams"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/utils/tensorboard/writer.py#L287"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.tensorboard.writer.SummaryWriter.add_hparams" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">add_hparams</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hparam_dict</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">metric_dict</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">hparam_domain_discrete</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">run_name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">global_step</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/tensorboard/writer.html#SummaryWriter.add_hparams"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/utils/tensorboard/writer.py#L287"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.tensorboard.writer.SummaryWriter.add_hparams" title="Permalink to this definition">¶</a></dt>
 <dd><p>Add a set of hyperparameters to be compared in TensorBoard.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -1214,7 +1214,7 @@
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.utils.tensorboard.writer.SummaryWriter.flush">
-<span class="sig-name descname"><span class="pre">flush</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/tensorboard/writer.html#SummaryWriter.flush"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/utils/tensorboard/writer.py#L1185"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.tensorboard.writer.SummaryWriter.flush" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">flush</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/tensorboard/writer.html#SummaryWriter.flush"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/utils/tensorboard/writer.py#L1185"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.tensorboard.writer.SummaryWriter.flush" title="Permalink to this definition">¶</a></dt>
 <dd><p>Flushes the event file to disk.</p>
 <p>Call this method to make sure that all pending events have been written to
 disk.</p>
@@ -1222,7 +1222,7 @@
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.utils.tensorboard.writer.SummaryWriter.close">
-<span class="sig-name descname"><span class="pre">close</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/tensorboard/writer.html#SummaryWriter.close"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/utils/tensorboard/writer.py#L1196"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.tensorboard.writer.SummaryWriter.close" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">close</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/utils/tensorboard/writer.html#SummaryWriter.close"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/utils/tensorboard/writer.py#L1196"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.utils.tensorboard.writer.SummaryWriter.close" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 </dd></dl>
diff --git a/main/tensors.html b/main/tensors.html
index d3e574ea8cf..70da4f95f38 100644
--- a/main/tensors.html
+++ b/main/tensors.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/testing.html b/main/testing.html
index 9f5319333f8..32f5c459659 100644
--- a/main/testing.html
+++ b/main/testing.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -610,7 +610,7 @@
 <span id="torch-testing"></span><h1>torch.testing<a class="headerlink" href="#module-torch.testing" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.testing.assert_close">
-<span class="sig-prename descclassname"><span class="pre">torch.testing.</span></span><span class="sig-name descname"><span class="pre">assert_close</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">actual</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">expected</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">allow_subclasses</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">rtol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">atol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">equal_nan</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">check_device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">check_dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">check_layout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">check_stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">msg</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/testing/_comparison.html#assert_close"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/testing/_comparison.py#L1249"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.testing.assert_close" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.testing.</span></span><span class="sig-name descname"><span class="pre">assert_close</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">actual</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">expected</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">allow_subclasses</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">rtol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">atol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">equal_nan</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">check_device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">check_dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">check_layout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">check_stride</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">msg</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/testing/_comparison.html#assert_close"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/testing/_comparison.py#L1249"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.testing.assert_close" title="Permalink to this definition">¶</a></dt>
 <dd><p>Asserts that <code class="docutils literal notranslate"><span class="pre">actual</span></code> and <code class="docutils literal notranslate"><span class="pre">expected</span></code> are close.</p>
 <p>If <code class="docutils literal notranslate"><span class="pre">actual</span></code> and <code class="docutils literal notranslate"><span class="pre">expected</span></code> are strided, non-quantized, real-valued, and finite, they are considered close if</p>
 <div class="math">
@@ -882,7 +882,7 @@
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.testing.make_tensor">
-<span class="sig-prename descclassname"><span class="pre">torch.testing.</span></span><span class="sig-name descname"><span class="pre">make_tensor</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">shape</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">low</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">high</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">requires_grad</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">noncontiguous</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">exclude_zero</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">memory_format</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/testing/_creation.html#make_tensor"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/testing/_creation.py#L45"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.testing.make_tensor" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.testing.</span></span><span class="sig-name descname"><span class="pre">make_tensor</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">shape</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">low</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">high</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">requires_grad</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">noncontiguous</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">exclude_zero</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">memory_format</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/testing/_creation.html#make_tensor"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/testing/_creation.py#L45"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.testing.make_tensor" title="Permalink to this definition">¶</a></dt>
 <dd><p>Creates a tensor with the given <code class="xref py py-attr docutils literal notranslate"><span class="pre">shape</span></code>, <code class="xref py py-attr docutils literal notranslate"><span class="pre">device</span></code>, and <code class="xref py py-attr docutils literal notranslate"><span class="pre">dtype</span></code>, and filled with
 values uniformly drawn from <code class="docutils literal notranslate"><span class="pre">[low,</span> <span class="pre">high)</span></code>.</p>
 <p>If <code class="xref py py-attr docutils literal notranslate"><span class="pre">low</span></code> or <code class="xref py py-attr docutils literal notranslate"><span class="pre">high</span></code> are specified and are outside the range of the <code class="xref py py-attr docutils literal notranslate"><span class="pre">dtype</span></code>’s representable
@@ -977,7 +977,7 @@
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.testing.assert_allclose">
-<span class="sig-prename descclassname"><span class="pre">torch.testing.</span></span><span class="sig-name descname"><span class="pre">assert_allclose</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">actual</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">expected</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">rtol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">atol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">equal_nan</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">msg</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/testing/_comparison.html#assert_allclose"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/testing/_comparison.py#L1522"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.testing.assert_allclose" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.testing.</span></span><span class="sig-name descname"><span class="pre">assert_allclose</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">actual</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">expected</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">rtol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">atol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">equal_nan</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">msg</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/testing/_comparison.html#assert_allclose"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/testing/_comparison.py#L1522"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.testing.assert_allclose" title="Permalink to this definition">¶</a></dt>
 <dd><div class="admonition warning">
 <p class="admonition-title">Warning</p>
 <p><a class="reference internal" href="#torch.testing.assert_allclose" title="torch.testing.assert_allclose"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.testing.assert_allclose()</span></code></a> is deprecated since <code class="docutils literal notranslate"><span class="pre">1.12</span></code> and will be removed in a future release.
diff --git a/main/threading_environment_variables.html b/main/threading_environment_variables.html
index 484e537576e..8bfc06a7c73 100644
--- a/main/threading_environment_variables.html
+++ b/main/threading_environment_variables.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/torch.ao.ns._numeric_suite.html b/main/torch.ao.ns._numeric_suite.html
index b8df030f3a3..81b7397c3d0 100644
--- a/main/torch.ao.ns._numeric_suite.html
+++ b/main/torch.ao.ns._numeric_suite.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -618,7 +618,7 @@
 </div>
 <span class="target" id="module-torch.ao.ns._numeric_suite"></span><dl class="py function">
 <dt class="sig sig-object py" id="torch.ao.ns._numeric_suite.compare_weights">
-<span class="sig-prename descclassname"><span class="pre">torch.ao.ns._numeric_suite.</span></span><span class="sig-name descname"><span class="pre">compare_weights</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">float_dict</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">quantized_dict</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/ao/ns/_numeric_suite.html#compare_weights"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/ns/_numeric_suite.py#L55"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.ns._numeric_suite.compare_weights" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.ao.ns._numeric_suite.</span></span><span class="sig-name descname"><span class="pre">compare_weights</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">float_dict</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">quantized_dict</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/ao/ns/_numeric_suite.html#compare_weights"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/ns/_numeric_suite.py#L55"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.ns._numeric_suite.compare_weights" title="Permalink to this definition">¶</a></dt>
 <dd><p>Compare the weights of the float module with its corresponding quantized
 module. Return a dict with key corresponding to module names and each entry being
 a dictionary with two keys ‘float’ and ‘quantized’, containing the float and
@@ -657,7 +657,7 @@
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.ao.ns._numeric_suite.get_logger_dict">
-<span class="sig-prename descclassname"><span class="pre">torch.ao.ns._numeric_suite.</span></span><span class="sig-name descname"><span class="pre">get_logger_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mod</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prefix</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/ao/ns/_numeric_suite.html#get_logger_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/ns/_numeric_suite.py#L150"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.ns._numeric_suite.get_logger_dict" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.ao.ns._numeric_suite.</span></span><span class="sig-name descname"><span class="pre">get_logger_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mod</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prefix</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/ao/ns/_numeric_suite.html#get_logger_dict"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/ns/_numeric_suite.py#L150"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.ns._numeric_suite.get_logger_dict" title="Permalink to this definition">¶</a></dt>
 <dd><p>Traverse the modules and save all logger stats into target dict.
 This is mainly used for quantization accuracy debug.</p>
 <dl class="simple">
@@ -683,41 +683,41 @@
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.ns._numeric_suite.Logger">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.ns._numeric_suite.</span></span><span class="sig-name descname"><span class="pre">Logger</span></span><a class="reference internal" href="_modules/torch/ao/ns/_numeric_suite.html#Logger"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/ns/_numeric_suite.py#L173"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.ns._numeric_suite.Logger" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.ns._numeric_suite.</span></span><span class="sig-name descname"><span class="pre">Logger</span></span><a class="reference internal" href="_modules/torch/ao/ns/_numeric_suite.html#Logger"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/ns/_numeric_suite.py#L173"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.ns._numeric_suite.Logger" title="Permalink to this definition">¶</a></dt>
 <dd><p>Base class for stats logging</p>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.ao.ns._numeric_suite.Logger.forward">
-<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/ao/ns/_numeric_suite.html#Logger.forward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/ns/_numeric_suite.py#L184"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.ns._numeric_suite.Logger.forward" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/ao/ns/_numeric_suite.html#Logger.forward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/ns/_numeric_suite.py#L184"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.ns._numeric_suite.Logger.forward" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 </dd></dl>
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.ns._numeric_suite.ShadowLogger">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.ns._numeric_suite.</span></span><span class="sig-name descname"><span class="pre">ShadowLogger</span></span><a class="reference internal" href="_modules/torch/ao/ns/_numeric_suite.html#ShadowLogger"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/ns/_numeric_suite.py#L191"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.ns._numeric_suite.ShadowLogger" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.ns._numeric_suite.</span></span><span class="sig-name descname"><span class="pre">ShadowLogger</span></span><a class="reference internal" href="_modules/torch/ao/ns/_numeric_suite.html#ShadowLogger"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/ns/_numeric_suite.py#L191"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.ns._numeric_suite.ShadowLogger" title="Permalink to this definition">¶</a></dt>
 <dd><p>Class used in Shadow module to record the outputs of the original and
 shadow modules.</p>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.ao.ns._numeric_suite.ShadowLogger.forward">
-<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/ao/ns/_numeric_suite.html#ShadowLogger.forward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/ns/_numeric_suite.py#L201"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.ns._numeric_suite.ShadowLogger.forward" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/ao/ns/_numeric_suite.html#ShadowLogger.forward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/ns/_numeric_suite.py#L201"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.ns._numeric_suite.ShadowLogger.forward" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 </dd></dl>
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.ns._numeric_suite.OutputLogger">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.ns._numeric_suite.</span></span><span class="sig-name descname"><span class="pre">OutputLogger</span></span><a class="reference internal" href="_modules/torch/ao/ns/_numeric_suite.html#OutputLogger"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/ns/_numeric_suite.py#L214"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.ns._numeric_suite.OutputLogger" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.ns._numeric_suite.</span></span><span class="sig-name descname"><span class="pre">OutputLogger</span></span><a class="reference internal" href="_modules/torch/ao/ns/_numeric_suite.html#OutputLogger"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/ns/_numeric_suite.py#L214"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.ns._numeric_suite.OutputLogger" title="Permalink to this definition">¶</a></dt>
 <dd><p>Class used to log the outputs of the module</p>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.ao.ns._numeric_suite.OutputLogger.forward">
-<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/ao/ns/_numeric_suite.html#OutputLogger.forward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/ns/_numeric_suite.py#L221"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.ns._numeric_suite.OutputLogger.forward" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/ao/ns/_numeric_suite.html#OutputLogger.forward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/ns/_numeric_suite.py#L221"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.ns._numeric_suite.OutputLogger.forward" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 </dd></dl>
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.ns._numeric_suite.Shadow">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.ns._numeric_suite.</span></span><span class="sig-name descname"><span class="pre">Shadow</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">q_module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">float_module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">logger_cls</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/ao/ns/_numeric_suite.html#Shadow"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/ns/_numeric_suite.py#L244"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.ns._numeric_suite.Shadow" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.ns._numeric_suite.</span></span><span class="sig-name descname"><span class="pre">Shadow</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">q_module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">float_module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">logger_cls</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/ao/ns/_numeric_suite.html#Shadow"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/ns/_numeric_suite.py#L244"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.ns._numeric_suite.Shadow" title="Permalink to this definition">¶</a></dt>
 <dd><p>Shadow module attaches the float module to its matching quantized module
 as the shadow. Then it uses Logger module to process the outputs of both
 modules.</p>
@@ -733,7 +733,7 @@
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.ao.ns._numeric_suite.Shadow.forward">
-<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">x</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/ao/ns/_numeric_suite.html#Shadow.forward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/ns/_numeric_suite.py#L263"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.ns._numeric_suite.Shadow.forward" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">x</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/ao/ns/_numeric_suite.html#Shadow.forward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/ns/_numeric_suite.py#L263"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.ns._numeric_suite.Shadow.forward" title="Permalink to this definition">¶</a></dt>
 <dd><dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
 <dd class="field-odd"><p><a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a></p>
@@ -743,7 +743,7 @@
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.ao.ns._numeric_suite.Shadow.add">
-<span class="sig-name descname"><span class="pre">add</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/ao/ns/_numeric_suite.html#Shadow.add"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/ns/_numeric_suite.py#L275"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.ns._numeric_suite.Shadow.add" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">add</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/ao/ns/_numeric_suite.html#Shadow.add"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/ns/_numeric_suite.py#L275"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.ns._numeric_suite.Shadow.add" title="Permalink to this definition">¶</a></dt>
 <dd><dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
 <dd class="field-odd"><p><a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a></p>
@@ -753,7 +753,7 @@
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.ao.ns._numeric_suite.Shadow.add_scalar">
-<span class="sig-name descname"><span class="pre">add_scalar</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/ao/ns/_numeric_suite.html#Shadow.add_scalar"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/ns/_numeric_suite.py#L287"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.ns._numeric_suite.Shadow.add_scalar" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">add_scalar</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/ao/ns/_numeric_suite.html#Shadow.add_scalar"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/ns/_numeric_suite.py#L287"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.ns._numeric_suite.Shadow.add_scalar" title="Permalink to this definition">¶</a></dt>
 <dd><dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
 <dd class="field-odd"><p><a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a></p>
@@ -763,7 +763,7 @@
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.ao.ns._numeric_suite.Shadow.mul">
-<span class="sig-name descname"><span class="pre">mul</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/ao/ns/_numeric_suite.html#Shadow.mul"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/ns/_numeric_suite.py#L298"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.ns._numeric_suite.Shadow.mul" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">mul</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/ao/ns/_numeric_suite.html#Shadow.mul"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/ns/_numeric_suite.py#L298"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.ns._numeric_suite.Shadow.mul" title="Permalink to this definition">¶</a></dt>
 <dd><dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
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@@ -773,7 +773,7 @@
 
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 <dt class="sig sig-object py" id="torch.ao.ns._numeric_suite.Shadow.mul_scalar">
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+<span class="sig-name descname"><span class="pre">mul_scalar</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/ao/ns/_numeric_suite.html#Shadow.mul_scalar"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/ns/_numeric_suite.py#L310"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.ns._numeric_suite.Shadow.mul_scalar" title="Permalink to this definition">¶</a></dt>
 <dd><dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
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@@ -783,7 +783,7 @@
 
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 <dt class="sig sig-object py" id="torch.ao.ns._numeric_suite.Shadow.cat">
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+<span class="sig-name descname"><span class="pre">cat</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/ao/ns/_numeric_suite.html#Shadow.cat"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/ns/_numeric_suite.py#L321"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.ns._numeric_suite.Shadow.cat" title="Permalink to this definition">¶</a></dt>
 <dd><dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
 <dd class="field-odd"><p><a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a></p>
@@ -793,7 +793,7 @@
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.ao.ns._numeric_suite.Shadow.add_relu">
-<span class="sig-name descname"><span class="pre">add_relu</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/ao/ns/_numeric_suite.html#Shadow.add_relu"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/ns/_numeric_suite.py#L332"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.ns._numeric_suite.Shadow.add_relu" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">add_relu</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/ao/ns/_numeric_suite.html#Shadow.add_relu"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/ns/_numeric_suite.py#L332"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.ns._numeric_suite.Shadow.add_relu" title="Permalink to this definition">¶</a></dt>
 <dd><dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
 <dd class="field-odd"><p><a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a></p>
@@ -805,7 +805,7 @@
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.ao.ns._numeric_suite.prepare_model_with_stubs">
-<span class="sig-prename descclassname"><span class="pre">torch.ao.ns._numeric_suite.</span></span><span class="sig-name descname"><span class="pre">prepare_model_with_stubs</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">float_module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">q_module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">module_swap_list</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">logger_cls</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/ao/ns/_numeric_suite.html#prepare_model_with_stubs"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/ns/_numeric_suite.py#L345"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.ns._numeric_suite.prepare_model_with_stubs" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.ao.ns._numeric_suite.</span></span><span class="sig-name descname"><span class="pre">prepare_model_with_stubs</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">float_module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">q_module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">module_swap_list</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">logger_cls</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/ao/ns/_numeric_suite.html#prepare_model_with_stubs"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/ns/_numeric_suite.py#L345"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.ns._numeric_suite.prepare_model_with_stubs" title="Permalink to this definition">¶</a></dt>
 <dd><p>Prepare the model by attaching the float module to its matching quantized
 module as the shadow if the float module type is in module_swap_list.</p>
 <p>Example usage:</p>
@@ -829,7 +829,7 @@
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.ao.ns._numeric_suite.compare_model_stub">
-<span class="sig-prename descclassname"><span class="pre">torch.ao.ns._numeric_suite.</span></span><span class="sig-name descname"><span class="pre">compare_model_stub</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">float_model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">q_model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">module_swap_list</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">*data</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">logger_cls=&lt;class</span> <span class="pre">'torch.ao.ns._numeric_suite.ShadowLogger'&gt;</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/ao/ns/_numeric_suite.html#compare_model_stub"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/ns/_numeric_suite.py#L403"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.ns._numeric_suite.compare_model_stub" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.ao.ns._numeric_suite.</span></span><span class="sig-name descname"><span class="pre">compare_model_stub</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">float_model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">q_model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">module_swap_list</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">*data</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">logger_cls=&lt;class</span> <span class="pre">'torch.ao.ns._numeric_suite.ShadowLogger'&gt;</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/ao/ns/_numeric_suite.html#compare_model_stub"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/ns/_numeric_suite.py#L403"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.ns._numeric_suite.compare_model_stub" title="Permalink to this definition">¶</a></dt>
 <dd><p>Compare quantized module in a model with its floating point counterpart,
 feeding both of them the same input. Return a dict with key corresponding to
 module names and each entry being a dictionary with two keys ‘float’ and
@@ -870,7 +870,7 @@
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.ao.ns._numeric_suite.get_matching_activations">
-<span class="sig-prename descclassname"><span class="pre">torch.ao.ns._numeric_suite.</span></span><span class="sig-name descname"><span class="pre">get_matching_activations</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">float_module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">q_module</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/ao/ns/_numeric_suite.html#get_matching_activations"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/ns/_numeric_suite.py#L448"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.ns._numeric_suite.get_matching_activations" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.ao.ns._numeric_suite.</span></span><span class="sig-name descname"><span class="pre">get_matching_activations</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">float_module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">q_module</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/ao/ns/_numeric_suite.html#get_matching_activations"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/ns/_numeric_suite.py#L448"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.ns._numeric_suite.get_matching_activations" title="Permalink to this definition">¶</a></dt>
 <dd><p>Find the matching activation between float and quantized modules.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -892,7 +892,7 @@
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.ao.ns._numeric_suite.prepare_model_outputs">
-<span class="sig-prename descclassname"><span class="pre">torch.ao.ns._numeric_suite.</span></span><span class="sig-name descname"><span class="pre">prepare_model_outputs</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">float_module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">q_module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">logger_cls=&lt;class</span> <span class="pre">'torch.ao.ns._numeric_suite.OutputLogger'&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">allow_list=None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/ao/ns/_numeric_suite.html#prepare_model_outputs"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/ns/_numeric_suite.py#L480"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.ns._numeric_suite.prepare_model_outputs" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.ao.ns._numeric_suite.</span></span><span class="sig-name descname"><span class="pre">prepare_model_outputs</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">float_module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">q_module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">logger_cls=&lt;class</span> <span class="pre">'torch.ao.ns._numeric_suite.OutputLogger'&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">allow_list=None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/ao/ns/_numeric_suite.html#prepare_model_outputs"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/ns/_numeric_suite.py#L480"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.ns._numeric_suite.prepare_model_outputs" title="Permalink to this definition">¶</a></dt>
 <dd><p>Prepare the model by attaching the logger to both float module
 and quantized module if they are in the allow_list.</p>
 <dl class="field-list simple">
@@ -909,7 +909,7 @@
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.ao.ns._numeric_suite.compare_model_outputs">
-<span class="sig-prename descclassname"><span class="pre">torch.ao.ns._numeric_suite.</span></span><span class="sig-name descname"><span class="pre">compare_model_outputs</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">float_model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">q_model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">*data</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">logger_cls=&lt;class</span> <span class="pre">'torch.ao.ns._numeric_suite.OutputLogger'&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">allow_list=None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/ao/ns/_numeric_suite.html#compare_model_outputs"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/ns/_numeric_suite.py#L516"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.ns._numeric_suite.compare_model_outputs" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.ao.ns._numeric_suite.</span></span><span class="sig-name descname"><span class="pre">compare_model_outputs</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">float_model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">q_model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">*data</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">logger_cls=&lt;class</span> <span class="pre">'torch.ao.ns._numeric_suite.OutputLogger'&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">allow_list=None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/ao/ns/_numeric_suite.html#compare_model_outputs"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/ns/_numeric_suite.py#L516"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.ns._numeric_suite.compare_model_outputs" title="Permalink to this definition">¶</a></dt>
 <dd><p>Compare output activations between float and quantized models at
 corresponding locations for the same input. Return a dict with key corresponding
 to quantized module names and each entry being a dictionary with two keys
diff --git a/main/torch.ao.ns._numeric_suite_fx.html b/main/torch.ao.ns._numeric_suite_fx.html
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+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
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 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.ns._numeric_suite_fx.OutputLogger">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.ns._numeric_suite_fx.</span></span><span class="sig-name descname"><span class="pre">OutputLogger</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">ref_node_name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prev_node_name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">model_name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ref_name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prev_node_target_type</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ref_node_target_type</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">results_type</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">index_within_arg</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">index_of_arg</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fqn</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">qconfig_str</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/ao/ns/_numeric_suite_fx.html#OutputLogger"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/ns/_numeric_suite_fx.py#L138"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.ns._numeric_suite_fx.OutputLogger" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.ns._numeric_suite_fx.</span></span><span class="sig-name descname"><span class="pre">OutputLogger</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">ref_node_name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prev_node_name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">model_name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ref_name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prev_node_target_type</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ref_node_target_type</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">results_type</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">index_within_arg</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">index_of_arg</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fqn</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">qconfig_str</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/ao/ns/_numeric_suite_fx.html#OutputLogger"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/ns/_numeric_suite_fx.py#L138"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.ns._numeric_suite_fx.OutputLogger" title="Permalink to this definition">¶</a></dt>
 <dd><p>Base class for capturing intermediate values.</p>
 <dl class="field-list simple">
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.ao.ns._numeric_suite_fx.OutputLogger.forward">
-<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/ao/ns/_numeric_suite_fx.html#OutputLogger.forward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/ns/_numeric_suite_fx.py#L216"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.ns._numeric_suite_fx.OutputLogger.forward" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/ao/ns/_numeric_suite_fx.html#OutputLogger.forward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/ns/_numeric_suite_fx.py#L216"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.ns._numeric_suite_fx.OutputLogger.forward" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 </dd></dl>
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.ns._numeric_suite_fx.OutputComparisonLogger">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.ns._numeric_suite_fx.</span></span><span class="sig-name descname"><span class="pre">OutputComparisonLogger</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/ao/ns/_numeric_suite_fx.html#OutputComparisonLogger"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/ns/_numeric_suite_fx.py#L246"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.ns._numeric_suite_fx.OutputComparisonLogger" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.ns._numeric_suite_fx.</span></span><span class="sig-name descname"><span class="pre">OutputComparisonLogger</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/ao/ns/_numeric_suite_fx.html#OutputComparisonLogger"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/ns/_numeric_suite_fx.py#L246"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.ns._numeric_suite_fx.OutputComparisonLogger" title="Permalink to this definition">¶</a></dt>
 <dd><p>Same as OutputLogger, but also requires the original activation
 in order to calculate the comparison at calibration time</p>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.ao.ns._numeric_suite_fx.OutputComparisonLogger.forward">
-<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">x_ref</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/ao/ns/_numeric_suite_fx.html#OutputComparisonLogger.forward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/ns/_numeric_suite_fx.py#L261"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.ns._numeric_suite_fx.OutputComparisonLogger.forward" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">x_ref</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/ao/ns/_numeric_suite_fx.html#OutputComparisonLogger.forward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/ns/_numeric_suite_fx.py#L261"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.ns._numeric_suite_fx.OutputComparisonLogger.forward" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 </dd></dl>
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.ao.ns._numeric_suite_fx.NSTracer">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.ns._numeric_suite_fx.</span></span><span class="sig-name descname"><span class="pre">NSTracer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">skipped_module_names</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">skipped_module_classes</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/ao/ns/_numeric_suite_fx.html#NSTracer"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/ns/_numeric_suite_fx.py#L286"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.ns._numeric_suite_fx.NSTracer" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.ao.ns._numeric_suite_fx.</span></span><span class="sig-name descname"><span class="pre">NSTracer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">skipped_module_names</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">skipped_module_classes</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/ao/ns/_numeric_suite_fx.html#NSTracer"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/ns/_numeric_suite_fx.py#L286"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.ns._numeric_suite_fx.NSTracer" title="Permalink to this definition">¶</a></dt>
 <dd><p>Just like a regular FX quantization tracer, but treats observers and fake_quantize
 modules as leaf modules.</p>
 <dl class="field-list simple">
 </dl>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.ao.ns._numeric_suite_fx.NSTracer.is_leaf_module">
-<span class="sig-name descname"><span class="pre">is_leaf_module</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">m</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">module_qualified_name</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/ao/ns/_numeric_suite_fx.html#NSTracer.is_leaf_module"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/ns/_numeric_suite_fx.py#L292"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.ns._numeric_suite_fx.NSTracer.is_leaf_module" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">is_leaf_module</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">m</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">module_qualified_name</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/ao/ns/_numeric_suite_fx.html#NSTracer.is_leaf_module"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/ns/_numeric_suite_fx.py#L292"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.ns._numeric_suite_fx.NSTracer.is_leaf_module" title="Permalink to this definition">¶</a></dt>
 <dd><dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
 <dd class="field-odd"><p><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.13)">bool</a></p>
@@ -745,7 +745,7 @@
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.ao.ns._numeric_suite_fx.extract_weights">
-<span class="sig-prename descclassname"><span class="pre">torch.ao.ns._numeric_suite_fx.</span></span><span class="sig-name descname"><span class="pre">extract_weights</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model_name_a</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">model_a</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">model_name_b</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">model_b</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">base_name_to_sets_of_related_ops</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">unmatchable_types_map</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">op_to_type_to_weight_extraction_fn</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/ao/ns/_numeric_suite_fx.html#extract_weights"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/ns/_numeric_suite_fx.py#L379"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.ns._numeric_suite_fx.extract_weights" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.ao.ns._numeric_suite_fx.</span></span><span class="sig-name descname"><span class="pre">extract_weights</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model_name_a</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">model_a</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">model_name_b</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">model_b</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">base_name_to_sets_of_related_ops</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">unmatchable_types_map</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">op_to_type_to_weight_extraction_fn</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/ao/ns/_numeric_suite_fx.html#extract_weights"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/ns/_numeric_suite_fx.py#L379"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.ns._numeric_suite_fx.extract_weights" title="Permalink to this definition">¶</a></dt>
 <dd><p>Extract weights from model A and model B, and return a comparison.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -771,7 +771,7 @@
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.ao.ns._numeric_suite_fx.add_loggers">
-<span class="sig-prename descclassname"><span class="pre">torch.ao.ns._numeric_suite_fx.</span></span><span class="sig-name descname"><span class="pre">add_loggers</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name_a</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">model_a</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">name_b</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">model_b</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">logger_cls</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">should_log_inputs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">base_name_to_sets_of_related_ops</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">unmatchable_types_map</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/ao/ns/_numeric_suite_fx.html#add_loggers"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/ns/_numeric_suite_fx.py#L525"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.ns._numeric_suite_fx.add_loggers" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.ao.ns._numeric_suite_fx.</span></span><span class="sig-name descname"><span class="pre">add_loggers</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name_a</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">model_a</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">name_b</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">model_b</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">logger_cls</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">should_log_inputs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">base_name_to_sets_of_related_ops</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">unmatchable_types_map</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/ao/ns/_numeric_suite_fx.html#add_loggers"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/ns/_numeric_suite_fx.py#L525"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.ns._numeric_suite_fx.add_loggers" title="Permalink to this definition">¶</a></dt>
 <dd><p>Instrument model A and model B with loggers.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -796,7 +796,7 @@
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.ao.ns._numeric_suite_fx.extract_logger_info">
-<span class="sig-prename descclassname"><span class="pre">torch.ao.ns._numeric_suite_fx.</span></span><span class="sig-name descname"><span class="pre">extract_logger_info</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model_a</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">model_b</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">logger_cls</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">model_name_to_use_for_layer_names</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/ao/ns/_numeric_suite_fx.html#extract_logger_info"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/ns/_numeric_suite_fx.py#L636"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.ns._numeric_suite_fx.extract_logger_info" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.ao.ns._numeric_suite_fx.</span></span><span class="sig-name descname"><span class="pre">extract_logger_info</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model_a</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">model_b</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">logger_cls</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">model_name_to_use_for_layer_names</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/ao/ns/_numeric_suite_fx.html#extract_logger_info"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/ns/_numeric_suite_fx.py#L636"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.ns._numeric_suite_fx.extract_logger_info" title="Permalink to this definition">¶</a></dt>
 <dd><p>Traverse all loggers in <cite>model_a</cite> and <cite>model_b</cite>, and extract the logged
 information.</p>
 <dl class="field-list simple">
@@ -820,7 +820,7 @@
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.ao.ns._numeric_suite_fx.add_shadow_loggers">
-<span class="sig-prename descclassname"><span class="pre">torch.ao.ns._numeric_suite_fx.</span></span><span class="sig-name descname"><span class="pre">add_shadow_loggers</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name_a</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">model_a</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">name_b</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">model_b</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">logger_cls</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">should_log_inputs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">base_name_to_sets_of_related_ops</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">node_type_to_io_type_map</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">unmatchable_types_map</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/ao/ns/_numeric_suite_fx.html#add_shadow_loggers"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/ns/_numeric_suite_fx.py#L701"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.ns._numeric_suite_fx.add_shadow_loggers" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.ao.ns._numeric_suite_fx.</span></span><span class="sig-name descname"><span class="pre">add_shadow_loggers</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name_a</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">model_a</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">name_b</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">model_b</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">logger_cls</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">should_log_inputs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">base_name_to_sets_of_related_ops</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">node_type_to_io_type_map</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">unmatchable_types_map</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/ao/ns/_numeric_suite_fx.html#add_shadow_loggers"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/ns/_numeric_suite_fx.py#L701"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.ns._numeric_suite_fx.add_shadow_loggers" title="Permalink to this definition">¶</a></dt>
 <dd><p>Instrument model A and model B with shadow loggers.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@@ -843,7 +843,7 @@
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.ao.ns._numeric_suite_fx.extract_shadow_logger_info">
-<span class="sig-prename descclassname"><span class="pre">torch.ao.ns._numeric_suite_fx.</span></span><span class="sig-name descname"><span class="pre">extract_shadow_logger_info</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model_a_shadows_b</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">logger_cls</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">model_name_to_use_for_layer_names</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/ao/ns/_numeric_suite_fx.html#extract_shadow_logger_info"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/ns/_numeric_suite_fx.py#L758"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.ns._numeric_suite_fx.extract_shadow_logger_info" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.ao.ns._numeric_suite_fx.</span></span><span class="sig-name descname"><span class="pre">extract_shadow_logger_info</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model_a_shadows_b</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">logger_cls</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">model_name_to_use_for_layer_names</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/ao/ns/_numeric_suite_fx.html#extract_shadow_logger_info"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/ns/_numeric_suite_fx.py#L758"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.ns._numeric_suite_fx.extract_shadow_logger_info" title="Permalink to this definition">¶</a></dt>
 <dd><p>Traverse all loggers in a shadow model, and extract the logged
 information.</p>
 <dl class="field-list simple">
@@ -866,7 +866,7 @@
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.ao.ns._numeric_suite_fx.extend_logger_results_with_comparison">
-<span class="sig-prename descclassname"><span class="pre">torch.ao.ns._numeric_suite_fx.</span></span><span class="sig-name descname"><span class="pre">extend_logger_results_with_comparison</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">results</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">model_name_1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">model_name_2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">comparison_fn</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">comparison_name</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/ao/ns/_numeric_suite_fx.html#extend_logger_results_with_comparison"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/ns/_numeric_suite_fx.py#L790"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.ns._numeric_suite_fx.extend_logger_results_with_comparison" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.ao.ns._numeric_suite_fx.</span></span><span class="sig-name descname"><span class="pre">extend_logger_results_with_comparison</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">results</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">model_name_1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">model_name_2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">comparison_fn</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">comparison_name</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/ao/ns/_numeric_suite_fx.html#extend_logger_results_with_comparison"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/ns/_numeric_suite_fx.py#L790"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.ns._numeric_suite_fx.extend_logger_results_with_comparison" title="Permalink to this definition">¶</a></dt>
 <dd><p>Compares the logged values from <cite>model_name_2</cite> against the corresponding
 values in <cite>model_name_1</cite>, using <cite>comparison_fn</cite>. Records the result
 in <cite>model_name_2</cite>’s results under <cite>comparison_name</cite>. Modifies <cite>results</cite> inplace.</p>
@@ -887,7 +887,7 @@
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.ao.ns._numeric_suite_fx.prepare_n_shadows_model">
-<span class="sig-prename descclassname"><span class="pre">torch.ao.ns._numeric_suite_fx.</span></span><span class="sig-name descname"><span class="pre">prepare_n_shadows_model</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">example_inputs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">qconfig_multi_mapping</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">backend_config</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">custom_prepare_fn</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">custom_prepare_kwargs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">custom_tracer</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/ao/ns/_numeric_suite_fx.html#prepare_n_shadows_model"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/ns/_numeric_suite_fx.py#L846"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.ns._numeric_suite_fx.prepare_n_shadows_model" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.ao.ns._numeric_suite_fx.</span></span><span class="sig-name descname"><span class="pre">prepare_n_shadows_model</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">example_inputs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">qconfig_multi_mapping</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">backend_config</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">custom_prepare_fn</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">custom_prepare_kwargs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">custom_tracer</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/ao/ns/_numeric_suite_fx.html#prepare_n_shadows_model"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/ns/_numeric_suite_fx.py#L846"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.ns._numeric_suite_fx.prepare_n_shadows_model" title="Permalink to this definition">¶</a></dt>
 <dd><p>Given a model with a graph with M ops such as</p>
 <blockquote>
 <div><p>args_kwargs_m -&gt; op_m -&gt; output_m</p>
@@ -921,7 +921,7 @@
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.ao.ns._numeric_suite_fx.loggers_set_enabled">
-<span class="sig-prename descclassname"><span class="pre">torch.ao.ns._numeric_suite_fx.</span></span><span class="sig-name descname"><span class="pre">loggers_set_enabled</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">enabled</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/ao/ns/_numeric_suite_fx.html#loggers_set_enabled"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/ns/_numeric_suite_fx.py#L1063"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.ns._numeric_suite_fx.loggers_set_enabled" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.ao.ns._numeric_suite_fx.</span></span><span class="sig-name descname"><span class="pre">loggers_set_enabled</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">enabled</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/ao/ns/_numeric_suite_fx.html#loggers_set_enabled"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/ns/_numeric_suite_fx.py#L1063"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.ns._numeric_suite_fx.loggers_set_enabled" title="Permalink to this definition">¶</a></dt>
 <dd><p>Sets the <cite>enabled</cite> setting on a <cite>model</cite>’s loggers</p>
 <dl class="field-list simple">
 </dl>
@@ -929,7 +929,7 @@
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.ao.ns._numeric_suite_fx.loggers_set_save_activations">
-<span class="sig-prename descclassname"><span class="pre">torch.ao.ns._numeric_suite_fx.</span></span><span class="sig-name descname"><span class="pre">loggers_set_save_activations</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">save_activations</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/ao/ns/_numeric_suite_fx.html#loggers_set_save_activations"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/ns/_numeric_suite_fx.py#L1074"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.ns._numeric_suite_fx.loggers_set_save_activations" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.ao.ns._numeric_suite_fx.</span></span><span class="sig-name descname"><span class="pre">loggers_set_save_activations</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">save_activations</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/ao/ns/_numeric_suite_fx.html#loggers_set_save_activations"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/ns/_numeric_suite_fx.py#L1074"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.ns._numeric_suite_fx.loggers_set_save_activations" title="Permalink to this definition">¶</a></dt>
 <dd><p>Sets the <cite>save_activations</cite> setting on a <cite>model</cite>’s loggers</p>
 <dl class="field-list simple">
 </dl>
@@ -937,7 +937,7 @@
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.ao.ns._numeric_suite_fx.convert_n_shadows_model">
-<span class="sig-prename descclassname"><span class="pre">torch.ao.ns._numeric_suite_fx.</span></span><span class="sig-name descname"><span class="pre">convert_n_shadows_model</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">custom_convert_fn</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">custom_convert_kwargs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/ao/ns/_numeric_suite_fx.html#convert_n_shadows_model"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/ns/_numeric_suite_fx.py#L1086"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.ns._numeric_suite_fx.convert_n_shadows_model" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.ao.ns._numeric_suite_fx.</span></span><span class="sig-name descname"><span class="pre">convert_n_shadows_model</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">custom_convert_fn</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">custom_convert_kwargs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/ao/ns/_numeric_suite_fx.html#convert_n_shadows_model"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/ns/_numeric_suite_fx.py#L1086"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.ns._numeric_suite_fx.convert_n_shadows_model" title="Permalink to this definition">¶</a></dt>
 <dd><p>Given a model from <cite>prepare_n_shadows_model</cite>, runs <cite>convert_fx</cite>
 on each shadow submodule.</p>
 <dl class="field-list simple">
@@ -949,7 +949,7 @@
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.ao.ns._numeric_suite_fx.extract_results_n_shadows_model">
-<span class="sig-prename descclassname"><span class="pre">torch.ao.ns._numeric_suite_fx.</span></span><span class="sig-name descname"><span class="pre">extract_results_n_shadows_model</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/ao/ns/_numeric_suite_fx.html#extract_results_n_shadows_model"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/ns/_numeric_suite_fx.py#L1111"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.ns._numeric_suite_fx.extract_results_n_shadows_model" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.ao.ns._numeric_suite_fx.</span></span><span class="sig-name descname"><span class="pre">extract_results_n_shadows_model</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/ao/ns/_numeric_suite_fx.html#extract_results_n_shadows_model"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/ns/_numeric_suite_fx.py#L1111"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.ns._numeric_suite_fx.extract_results_n_shadows_model" title="Permalink to this definition">¶</a></dt>
 <dd><p>Extracts logger results from <cite>model</cite>.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -960,7 +960,7 @@
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.ao.ns._numeric_suite_fx.print_comparisons_n_shadows_model">
-<span class="sig-prename descclassname"><span class="pre">torch.ao.ns._numeric_suite_fx.</span></span><span class="sig-name descname"><span class="pre">print_comparisons_n_shadows_model</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">results</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/ao/ns/_numeric_suite_fx.html#print_comparisons_n_shadows_model"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/ns/_numeric_suite_fx.py#L1120"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.ns._numeric_suite_fx.print_comparisons_n_shadows_model" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.ao.ns._numeric_suite_fx.</span></span><span class="sig-name descname"><span class="pre">print_comparisons_n_shadows_model</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">results</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/ao/ns/_numeric_suite_fx.html#print_comparisons_n_shadows_model"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/ns/_numeric_suite_fx.py#L1120"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.ns._numeric_suite_fx.print_comparisons_n_shadows_model" title="Permalink to this definition">¶</a></dt>
 <dd><p>Prints a summary of extracted <cite>results</cite>.</p>
 <dl class="field-list simple">
 </dl>
@@ -975,17 +975,17 @@ <h1>torch.ao.ns.fx.utils<a class="headerlink" href="#torch-ao-ns-fx-utils" title
 </div>
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-<span class="sig-prename descclassname"><span class="pre">torch.ao.ns.fx.utils.</span></span><span class="sig-name descname"><span class="pre">compute_sqnr</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/ao/ns/fx/utils.html#compute_sqnr"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/ns/fx/utils.py#L409"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.ns.fx.utils.compute_sqnr" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.ao.ns.fx.utils.</span></span><span class="sig-name descname"><span class="pre">compute_sqnr</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/ao/ns/fx/utils.html#compute_sqnr"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/ns/fx/utils.py#L409"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.ns.fx.utils.compute_sqnr" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
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-<span class="sig-prename descclassname"><span class="pre">torch.ao.ns.fx.utils.</span></span><span class="sig-name descname"><span class="pre">compute_normalized_l2_error</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/ao/ns/fx/utils.html#compute_normalized_l2_error"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/ns/fx/utils.py#L409"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.ns.fx.utils.compute_normalized_l2_error" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.ao.ns.fx.utils.</span></span><span class="sig-name descname"><span class="pre">compute_normalized_l2_error</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/ao/ns/fx/utils.html#compute_normalized_l2_error"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/ns/fx/utils.py#L409"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.ns.fx.utils.compute_normalized_l2_error" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.ao.ns.fx.utils.compute_cosine_similarity">
-<span class="sig-prename descclassname"><span class="pre">torch.ao.ns.fx.utils.</span></span><span class="sig-name descname"><span class="pre">compute_cosine_similarity</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/ao/ns/fx/utils.html#compute_cosine_similarity"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/ao/ns/fx/utils.py#L409"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.ns.fx.utils.compute_cosine_similarity" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.ao.ns.fx.utils.</span></span><span class="sig-name descname"><span class="pre">compute_cosine_similarity</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/ao/ns/fx/utils.html#compute_cosine_similarity"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/ao/ns/fx/utils.py#L409"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.ao.ns.fx.utils.compute_cosine_similarity" title="Permalink to this definition">¶</a></dt>
 <dd></dd></dl>
 
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+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
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diff --git a/main/torch.compiler_performance_dashboard.html b/main/torch.compiler_performance_dashboard.html
index f447a4000e5..46583ddab0e 100644
--- a/main/torch.compiler_performance_dashboard.html
+++ b/main/torch.compiler_performance_dashboard.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/torch.compiler_profiling_torch_compile.html b/main/torch.compiler_profiling_torch_compile.html
index 1edf9ac3c98..7a3c68c1095 100644
--- a/main/torch.compiler_profiling_torch_compile.html
+++ b/main/torch.compiler_profiling_torch_compile.html
@@ -300,7 +300,7 @@
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     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
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     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/torch.compiler_transformations.html b/main/torch.compiler_transformations.html
index f9dfdf3667b..28f2122059a 100644
--- a/main/torch.compiler_transformations.html
+++ b/main/torch.compiler_transformations.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/torch.compiler_troubleshooting.html b/main/torch.compiler_troubleshooting.html
index 0a46db1ee65..4c521298957 100644
--- a/main/torch.compiler_troubleshooting.html
+++ b/main/torch.compiler_troubleshooting.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/torch.compiler_troubleshooting_old.html b/main/torch.compiler_troubleshooting_old.html
index e6116c9b84a..c8b0092a29c 100644
--- a/main/torch.compiler_troubleshooting_old.html
+++ b/main/torch.compiler_troubleshooting_old.html
@@ -298,7 +298,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/torch.html b/main/torch.html
index 8bd253406f0..13c272e59fe 100644
--- a/main/torch.html
+++ b/main/torch.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -2257,13 +2257,13 @@ <h2>Utilities<a class="headerlink" href="#utilities" title="Permalink to this he
 <h2>Symbolic Numbers<a class="headerlink" href="#symbolic-numbers" title="Permalink to this heading">¶</a></h2>
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.SymInt">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">SymInt</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">node</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch.html#SymInt"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/__init__.py#L404"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.SymInt" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">SymInt</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">node</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch.html#SymInt"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/__init__.py#L404"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.SymInt" title="Permalink to this definition">¶</a></dt>
 <dd><p>Like an int (including magic methods), but redirects all operations on the
 wrapped node. This is used in particular to symbolically record operations
 in the symbolic shape workflow.</p>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.SymInt.as_integer_ratio">
-<span class="sig-name descname"><span class="pre">as_integer_ratio</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch.html#SymInt.as_integer_ratio"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/__init__.py#L586"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.SymInt.as_integer_ratio" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">as_integer_ratio</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch.html#SymInt.as_integer_ratio"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/__init__.py#L586"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.SymInt.as_integer_ratio" title="Permalink to this definition">¶</a></dt>
 <dd><p>Represent this int as an exact integer ratio</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -2276,13 +2276,13 @@ <h2>Symbolic Numbers<a class="headerlink" href="#symbolic-numbers" title="Permal
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.SymFloat">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">SymFloat</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">node</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch.html#SymFloat"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/__init__.py#L601"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.SymFloat" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">SymFloat</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">node</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch.html#SymFloat"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/__init__.py#L601"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.SymFloat" title="Permalink to this definition">¶</a></dt>
 <dd><p>Like an float (including magic methods), but redirects all operations on the
 wrapped node. This is used in particular to symbolically record operations
 in the symbolic shape workflow.</p>
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.SymFloat.as_integer_ratio">
-<span class="sig-name descname"><span class="pre">as_integer_ratio</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch.html#SymFloat.as_integer_ratio"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/__init__.py#L698"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.SymFloat.as_integer_ratio" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">as_integer_ratio</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch.html#SymFloat.as_integer_ratio"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/__init__.py#L698"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.SymFloat.as_integer_ratio" title="Permalink to this definition">¶</a></dt>
 <dd><p>Represent this float as an exact integer ratio</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -2293,7 +2293,7 @@ <h2>Symbolic Numbers<a class="headerlink" href="#symbolic-numbers" title="Permal
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.SymFloat.conjugate">
-<span class="sig-name descname"><span class="pre">conjugate</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch.html#SymFloat.conjugate"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/__init__.py#L711"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.SymFloat.conjugate" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">conjugate</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch.html#SymFloat.conjugate"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/__init__.py#L711"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.SymFloat.conjugate" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns the complex conjugate of the float.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -2304,7 +2304,7 @@ <h2>Symbolic Numbers<a class="headerlink" href="#symbolic-numbers" title="Permal
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.SymFloat.hex">
-<span class="sig-name descname"><span class="pre">hex</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch.html#SymFloat.hex"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/__init__.py#L715"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.SymFloat.hex" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">hex</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch.html#SymFloat.hex"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/__init__.py#L715"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.SymFloat.hex" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns the hexadecimal representation of the float.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Return type</dt>
@@ -2315,7 +2315,7 @@ <h2>Symbolic Numbers<a class="headerlink" href="#symbolic-numbers" title="Permal
 
 <dl class="py method">
 <dt class="sig sig-object py" id="torch.SymFloat.is_integer">
-<span class="sig-name descname"><span class="pre">is_integer</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch.html#SymFloat.is_integer"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/__init__.py#L694"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.SymFloat.is_integer" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">is_integer</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch.html#SymFloat.is_integer"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/__init__.py#L694"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.SymFloat.is_integer" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return True if the float is an integer.</p>
 </dd></dl>
 
@@ -2323,7 +2323,7 @@ <h2>Symbolic Numbers<a class="headerlink" href="#symbolic-numbers" title="Permal
 
 <dl class="py class">
 <dt class="sig sig-object py" id="torch.SymBool">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">SymBool</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">node</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch.html#SymBool"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/__init__.py#L720"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.SymBool" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch.</span></span><span class="sig-name descname"><span class="pre">SymBool</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">node</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch.html#SymBool"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/__init__.py#L720"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.SymBool" title="Permalink to this definition">¶</a></dt>
 <dd><p>Like an bool (including magic methods), but redirects all operations on the
 wrapped node. This is used in particular to symbolically record operations
 in the symbolic shape workflow.</p>
diff --git a/main/torch.overrides.html b/main/torch.overrides.html
index d4c2da78184..cc1e514c9ff 100644
--- a/main/torch.overrides.html
+++ b/main/torch.overrides.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -615,7 +615,7 @@
 <h2>Functions<a class="headerlink" href="#functions" title="Permalink to this heading">¶</a></h2>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.overrides.get_ignored_functions">
-<span class="sig-prename descclassname"><span class="pre">torch.overrides.</span></span><span class="sig-name descname"><span class="pre">get_ignored_functions</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/overrides.html#get_ignored_functions"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/overrides.py#L97"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.overrides.get_ignored_functions" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.overrides.</span></span><span class="sig-name descname"><span class="pre">get_ignored_functions</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/overrides.html#get_ignored_functions"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/overrides.py#L97"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.overrides.get_ignored_functions" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return public functions that cannot be overridden by <code class="docutils literal notranslate"><span class="pre">__torch_function__</span></code>.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Returns</dt>
@@ -638,7 +638,7 @@ <h2>Functions<a class="headerlink" href="#functions" title="Permalink to this he
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.overrides.get_overridable_functions">
-<span class="sig-prename descclassname"><span class="pre">torch.overrides.</span></span><span class="sig-name descname"><span class="pre">get_overridable_functions</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/overrides.html#get_overridable_functions"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/overrides.py#L1894"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.overrides.get_overridable_functions" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.overrides.</span></span><span class="sig-name descname"><span class="pre">get_overridable_functions</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/overrides.html#get_overridable_functions"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/overrides.py#L1894"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.overrides.get_overridable_functions" title="Permalink to this definition">¶</a></dt>
 <dd><p>List functions that are overridable via __torch_function__</p>
 <dl class="field-list simple">
 <dt class="field-odd">Returns</dt>
@@ -653,7 +653,7 @@ <h2>Functions<a class="headerlink" href="#functions" title="Permalink to this he
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.overrides.resolve_name">
-<span class="sig-prename descclassname"><span class="pre">torch.overrides.</span></span><span class="sig-name descname"><span class="pre">resolve_name</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">f</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/overrides.html#resolve_name"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/overrides.py#L1907"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.overrides.resolve_name" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.overrides.</span></span><span class="sig-name descname"><span class="pre">resolve_name</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">f</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/overrides.html#resolve_name"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/overrides.py#L1907"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.overrides.resolve_name" title="Permalink to this definition">¶</a></dt>
 <dd><p>Get a human readable string name for a function passed to
 __torch_function__</p>
 <dl class="field-list simple">
@@ -672,7 +672,7 @@ <h2>Functions<a class="headerlink" href="#functions" title="Permalink to this he
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.overrides.get_testing_overrides">
-<span class="sig-prename descclassname"><span class="pre">torch.overrides.</span></span><span class="sig-name descname"><span class="pre">get_testing_overrides</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/overrides.html#get_testing_overrides"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/overrides.py#L403"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.overrides.get_testing_overrides" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.overrides.</span></span><span class="sig-name descname"><span class="pre">get_testing_overrides</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/overrides.html#get_testing_overrides"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/overrides.py#L403"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.overrides.get_testing_overrides" title="Permalink to this definition">¶</a></dt>
 <dd><p>Return a dict containing dummy overrides for all overridable functions</p>
 <dl class="field-list simple">
 <dt class="field-odd">Returns</dt>
@@ -696,7 +696,7 @@ <h2>Functions<a class="headerlink" href="#functions" title="Permalink to this he
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.overrides.handle_torch_function">
-<span class="sig-prename descclassname"><span class="pre">torch.overrides.</span></span><span class="sig-name descname"><span class="pre">handle_torch_function</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">public_api</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">relevant_args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/overrides.html#handle_torch_function"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/overrides.py#L1670"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.overrides.handle_torch_function" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.overrides.</span></span><span class="sig-name descname"><span class="pre">handle_torch_function</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">public_api</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">relevant_args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/overrides.html#handle_torch_function"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/overrides.py#L1670"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.overrides.handle_torch_function" title="Permalink to this definition">¶</a></dt>
 <dd><p>Implement a function with checks for <code class="docutils literal notranslate"><span class="pre">__torch_function__</span></code> overrides.</p>
 <p>See torch::autograd::handle_torch_function for the equivalent of this
 function in the C++ implementation.</p>
@@ -759,7 +759,7 @@ <h2>Functions<a class="headerlink" href="#functions" title="Permalink to this he
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.overrides.is_tensor_like">
-<span class="sig-prename descclassname"><span class="pre">torch.overrides.</span></span><span class="sig-name descname"><span class="pre">is_tensor_like</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">inp</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/overrides.html#is_tensor_like"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/overrides.py#L1962"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.overrides.is_tensor_like" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.overrides.</span></span><span class="sig-name descname"><span class="pre">is_tensor_like</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">inp</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/overrides.html#is_tensor_like"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/overrides.py#L1962"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.overrides.is_tensor_like" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns <code class="docutils literal notranslate"><span class="pre">True</span></code> if the passed-in input is a Tensor-like.</p>
 <p>Currently, this occurs whenever there’s a <code class="docutils literal notranslate"><span class="pre">__torch_function__</span></code>
 attribute on the type of the input.</p>
@@ -793,7 +793,7 @@ <h2>Functions<a class="headerlink" href="#functions" title="Permalink to this he
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.overrides.is_tensor_method_or_property">
-<span class="sig-prename descclassname"><span class="pre">torch.overrides.</span></span><span class="sig-name descname"><span class="pre">is_tensor_method_or_property</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">func</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/overrides.html#is_tensor_method_or_property"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/overrides.py#L1936"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.overrides.is_tensor_method_or_property" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.overrides.</span></span><span class="sig-name descname"><span class="pre">is_tensor_method_or_property</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">func</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/overrides.html#is_tensor_method_or_property"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/overrides.py#L1936"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.overrides.is_tensor_method_or_property" title="Permalink to this definition">¶</a></dt>
 <dd><p>Returns True if the function passed in is a handler for a
 method or property belonging to <code class="docutils literal notranslate"><span class="pre">torch.Tensor</span></code>, as passed
 into <code class="docutils literal notranslate"><span class="pre">__torch_function__</span></code>.</p>
@@ -823,7 +823,7 @@ <h2>Functions<a class="headerlink" href="#functions" title="Permalink to this he
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.overrides.wrap_torch_function">
-<span class="sig-prename descclassname"><span class="pre">torch.overrides.</span></span><span class="sig-name descname"><span class="pre">wrap_torch_function</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dispatcher</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/overrides.html#wrap_torch_function"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/overrides.py#L1554"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.overrides.wrap_torch_function" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.overrides.</span></span><span class="sig-name descname"><span class="pre">wrap_torch_function</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dispatcher</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/overrides.html#wrap_torch_function"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/overrides.py#L1554"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.overrides.wrap_torch_function" title="Permalink to this definition">¶</a></dt>
 <dd><p>Wraps a given function with <code class="docutils literal notranslate"><span class="pre">__torch_function__</span></code> -related functionality.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
diff --git a/main/torch_cuda_memory.html b/main/torch_cuda_memory.html
index 34f3c7bb192..eeb8fe5529b 100644
--- a/main/torch_cuda_memory.html
+++ b/main/torch_cuda_memory.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
@@ -654,7 +654,7 @@ <h2>Allocator State History<a class="headerlink" href="#allocator-state-history"
 <h1>Snapshot API Reference<a class="headerlink" href="#snapshot-api-reference" title="Permalink to this heading">¶</a></h1>
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.cuda.memory._record_memory_history">
-<span class="sig-prename descclassname"><span class="pre">torch.cuda.memory.</span></span><span class="sig-name descname"><span class="pre">_record_memory_history</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">enabled</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'all'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">context</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'all'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stacks</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'all'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_entries</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">9223372036854775807</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/cuda/memory.html#_record_memory_history"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/memory.py#L758"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.memory._record_memory_history" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.cuda.memory.</span></span><span class="sig-name descname"><span class="pre">_record_memory_history</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">enabled</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'all'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">context</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'all'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stacks</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'all'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_entries</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">9223372036854775807</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/cuda/memory.html#_record_memory_history"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/cuda/memory.py#L758"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.memory._record_memory_history" title="Permalink to this definition">¶</a></dt>
 <dd><p>Enable recording of stack traces associated with memory
 allocations, so you can tell what allocated any piece of memory in
 <a class="reference internal" href="#torch.cuda.memory._snapshot" title="torch.cuda.memory._snapshot"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.cuda.memory._snapshot()</span></code></a>.</p>
@@ -691,7 +691,7 @@ <h1>Snapshot API Reference<a class="headerlink" href="#snapshot-api-reference" t
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.cuda.memory._snapshot">
-<span class="sig-prename descclassname"><span class="pre">torch.cuda.memory.</span></span><span class="sig-name descname"><span class="pre">_snapshot</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/cuda/memory.html#_snapshot"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/memory.py#L816"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.memory._snapshot" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.cuda.memory.</span></span><span class="sig-name descname"><span class="pre">_snapshot</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/cuda/memory.html#_snapshot"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/cuda/memory.py#L816"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.memory._snapshot" title="Permalink to this definition">¶</a></dt>
 <dd><p>Save a snapshot of CUDA memory state at the time it was called.</p>
 <p>The state is represented as a dictionary with the following structure.</p>
 <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="k">class</span> <span class="nc">Snapshot</span><span class="p">(</span><span class="n">TypedDict</span><span class="p">):</span>
@@ -768,7 +768,7 @@ <h1>Snapshot API Reference<a class="headerlink" href="#snapshot-api-reference" t
 
 <dl class="py function">
 <dt class="sig sig-object py" id="torch.cuda.memory._dump_snapshot">
-<span class="sig-prename descclassname"><span class="pre">torch.cuda.memory.</span></span><span class="sig-name descname"><span class="pre">_dump_snapshot</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">filename</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'dump_snapshot.pickle'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/cuda/memory.html#_dump_snapshot"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/6b41f310c20be05903d0994a51a4c927dfd5f286/torch/cuda/memory.py#L893"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.memory._dump_snapshot" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">torch.cuda.memory.</span></span><span class="sig-name descname"><span class="pre">_dump_snapshot</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">filename</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'dump_snapshot.pickle'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/cuda/memory.html#_dump_snapshot"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="reference external" href="https://github.com/pytorch/pytorch/blob/c72b53642050d9c5614ef2ae296505c3e0c11302/torch/cuda/memory.py#L893"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch.cuda.memory._dump_snapshot" title="Permalink to this definition">¶</a></dt>
 <dd><p>Save a pickled version of the <cite>torch.memory._snapshot()</cite> dictionary to a file.</p>
 <p>This file can be opened by the interactive snapshot viewer at pytorch.org/memory_viz</p>
 <dl class="field-list simple">
diff --git a/main/torch_environment_variables.html b/main/torch_environment_variables.html
index b801056e263..55a8fd6c7be 100644
--- a/main/torch_environment_variables.html
+++ b/main/torch_environment_variables.html
@@ -300,7 +300,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/torch_nccl_environment_variables.html b/main/torch_nccl_environment_variables.html
index 6954200a348..e3630ad5661 100644
--- a/main/torch_nccl_environment_variables.html
+++ b/main/torch_nccl_environment_variables.html
@@ -299,7 +299,7 @@
           <div class="pytorch-left-menu-search">
             
     <div class="version">
-      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+git6b41f31 ) &#x25BC</a>
+      <a href='https://pytorch.org/docs/versions.html'>main (2.7.0a0+gitc72b536 ) &#x25BC</a>
     </div>
     <div id="searchBox">
     <div class="searchbox" id="googleSearchBox">
diff --git a/main/type_info.html b/main/type_info.html
index 42fe9986e32..056b9e34d1b 100644
--- a/main/type_info.html
+++ b/main/type_info.html
@@ -300,7 +300,7 @@
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index 4496d2edb7d..2452ff08d2c 100644
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